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@@ -2203,3 +2203,4 @@ output/preprocess/Parkinsons_Disease/gene_data/GSE101534.csv filter=lfs diff=lfs
 output/preprocess/Chronic_kidney_disease/GSE66494.csv filter=lfs diff=lfs merge=lfs -text
 output/preprocess/Chronic_kidney_disease/gene_data/GSE180393.csv filter=lfs diff=lfs merge=lfs -text
 output/preprocess/Bipolar_disorder/GSE62191.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Eczema/GSE123088.csv filter=lfs diff=lfs merge=lfs -text

output/preprocess/Eczema/GSE123088.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:606dd189b14daca7fb28c9c93944796970a8fede139c569197e905f1d5d0bce3
+size 55765473

output/preprocess/Epilepsy/code/GSE65106.py

@@ -0,0 +1,216 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Epilepsy"
+cohort = "GSE65106"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Epilepsy"
+in_cohort_dir = "../DATA/GEO/Epilepsy/GSE65106"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Epilepsy/GSE65106.csv"
+out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE65106.csv"
+out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE65106.csv"
+json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Determine data availability based on provided background and characteristics
+is_gene_available = True  # Whole-genome microarray gene expression profiling
+trait_row = None          # Trait of interest is Epilepsy; this dataset is ASD and excluded seizure disorders
+age_row = 3               # 'donor age'
+gender_row = 4            # 'donor sex'
+
+# Conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    try:
+        s = str(x)
+    except Exception:
+        return None
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    # Binary: 1 = Epilepsy, 0 = Non-epilepsy
+    v = _extract_value(x)
+    if v is None or v == '':
+        return None
+    v_low = v.lower()
+    # Heuristics for epilepsy mentions
+    epilepsy_terms = ['epilepsy', 'seizure', 'temporal lobe epilepsy', 'tle', 'absence epilepsy', 'partial epilepsy']
+    if any(term in v_low for term in epilepsy_terms):
+        return 1
+    # If explicitly normal/control/ASD/WT/etc., treat as non-epilepsy
+    non_epilepsy_terms = ['normal', 'control', 'wt', 'asd', 'autism']
+    if any(term in v_low for term in non_epilepsy_terms):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous age in years; non-numeric (e.g., embryonic) -> None
+    v = _extract_value(x)
+    if v is None or v == '':
+        return None
+    v_low = v.lower()
+    if 'embryonic' in v_low or 'fetal' in v_low:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        # try to extract leading numeric
+        import re
+        m = re.search(r'[-+]?\d*\.?\d+', v)
+        if m:
+            try:
+                return float(m.group(0))
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    # Binary: Female=0, Male=1
+    v = _extract_value(x)
+    if v is None or v == '':
+        return None
+    v_low = v.lower()
+    if v_low.startswith('male'):
+        return 1
+    if v_low.startswith('female'):
+        return 0
+    return None
+
+# Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction (skip if trait not available)
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical, n=5)
+    # Save clinical features
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Identifiers like '7892501' are Affymetrix probe set IDs (numeric), not HGNC gene symbols.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = ({requires_gene_mapping})")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify appropriate columns for probe IDs and gene symbols in the annotation
+probe_col = 'ID'
+# Prefer 'gene_assignment' for symbols; fall back to 'mrna_assignment' if needed
+gene_symbol_col = 'gene_assignment' if 'gene_assignment' in gene_annotation.columns else (
+    'mrna_assignment' if 'mrna_assignment' in gene_annotation.columns else None
+)
+
+if gene_symbol_col is None:
+    raise ValueError("No suitable gene symbol column found in annotation (expected 'gene_assignment' or 'mrna_assignment').")
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. Linkage and downstream steps only if trait data is available; otherwise, record metadata and stop.
+is_trait_available = (trait_row is not None)
+
+if is_trait_available and 'selected_clinical' in globals():
+    # Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)
+
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # Bias check and remove biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # Final validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Clinical features linked and processed."
+    )
+
+    # Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Trait not available: perform final validation only to record metadata; do not link or save linked data
+    _ = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=normalized_gene_data.T,
+        note="INFO: Trait 'Epilepsy' is not recorded in this ASD cohort; clinical linking skipped."
+    )

output/preprocess/Epilepsy/code/GSE74571.py

@@ -0,0 +1,123 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Epilepsy"
+cohort = "GSE74571"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Epilepsy"
+in_cohort_dir = "../DATA/GEO/Epilepsy/GSE74571"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Epilepsy/GSE74571.csv"
+out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE74571.csv"
+out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE74571.csv"
+json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Based on series summary indicating gene expression profiling on GSCs
+
+# 2) Variable Availability and Data Type Conversion
+
+# From the provided Sample Characteristics Dictionary:
+# {0: ['cell/tissue type: ...'], 1: ['culture type: ...']}
+# There is no explicit or inferable Epilepsy status, age, or gender information.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+# Conversion helpers
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ''
+    parts = str(value).split(':', 1)
+    return parts[1].strip().lower() if len(parts) > 1 else str(value).strip().lower()
+
+def convert_trait(value):
+    # Binary: 1 = Epilepsy case, 0 = non-epilepsy control
+    v = _after_colon(value)
+    if not v:
+        return None
+    # Positive epilepsy indicators
+    if any(term in v for term in ['epilepsy', 'epileptic', 'seizure', 'tle', 'temporal lobe epilepsy']):
+        return 1
+    # Clear control indicators
+    if any(term in v for term in ['control', 'healthy', 'non-epileptic', 'non epileptic', 'no epilepsy']):
+        return 0
+    # Ambiguous disease terms (e.g., GBM) are not epilepsy; avoid forcing to 0 without context
+    return None
+
+def convert_age(value):
+    # Continuous: extract numeric age in years if present
+    v = _after_colon(value)
+    if not v:
+        return None
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(value):
+    # Binary: female -> 0, male -> 1
+    v = _after_colon(value)
+    if not v:
+        return None
+    if v in ['female', 'f', 'woman', 'girl', 'wmn']:
+        return 0
+    if v in ['male', 'm', 'man', 'boy']:
+        return 1
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (skip because trait_row is None)
+# If in future data becomes available:
+# if trait_row is not None:
+#     selected = geo_select_clinical_features(
+#         clinical_df=clinical_data,
+#         trait=trait,
+#         trait_row=trait_row,
+#         convert_trait=convert_trait,
+#         age_row=age_row,
+#         convert_age=convert_age,
+#         gender_row=gender_row,
+#         convert_gender=convert_gender
+#     )
+#     preview = preview_df(selected, n=5)
+#     selected.to_csv(out_clinical_data_file, index=True)

output/preprocess/Epilepsy/code/TCGA.py

@@ -0,0 +1,53 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Epilepsy"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Epilepsy/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+
+# Step 1: Select the most relevant TCGA cohort directory for the trait "Epilepsy"
+synonyms = ["epilepsy", "seizure", "seizures", "ictal", "epileptic"]
+all_dirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+matched_dirs = [d for d in all_dirs if any(s in d.lower() for s in synonyms)]
+
+selected_cohort_dirname = None
+if matched_dirs:
+    # Prefer exact 'epilepsy' match if present; otherwise take the first matched
+    prioritized = sorted(matched_dirs, key=lambda d: (0 if "epilepsy" in d.lower() else 1, d.lower()))
+    selected_cohort_dirname = prioritized[0]
+
+if selected_cohort_dirname is None:
+    print("No suitable TCGA cohort found for the trait 'Epilepsy'. Skipping this trait.")
+    # Record unusable dataset status
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    clinical_df = None
+    gene_df = None
+else:
+    # Step 2: Identify clinical and genetic data file paths
+    cohort_dir = os.path.join(tcga_root_dir, selected_cohort_dirname)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as DataFrames
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+    gene_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+
+    # Step 4: Print column names of the clinical data
+    print(list(clinical_df.columns))

output/preprocess/Epilepsy/gene_data/GSE199759.csv

@@ -1,103 +1 @@
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+Gene,GSM5984016,GSM5984017,GSM5984018,GSM5984019,GSM5984020,GSM5984021,GSM5984022,GSM5984023,GSM5984024,GSM5984025,GSM5984026,GSM5984027,GSM5984028,GSM5984029,GSM5984030,GSM5984031,GSM5984032,GSM5984033,GSM5984034,GSM5984035,GSM5984036,GSM5984037,GSM5984038,GSM5984039,GSM5984040

output/preprocess/Esophageal_Cancer/clinical_data/GSE107754.csv

@@ -1,3 +1,3 @@
 ,GSM2878070,GSM2878071,GSM2878072,GSM2878073,GSM2878074,GSM2878075,GSM2878076,GSM2878077,GSM2878078,GSM2878079,GSM2878080,GSM2878081,GSM2878082,GSM2891194,GSM2891195,GSM2891196,GSM2891197,GSM2891198,GSM2891199,GSM2891200,GSM2891201,GSM2891202,GSM2891203,GSM2891204,GSM2891205,GSM2891206,GSM2891207,GSM2891208,GSM2891209,GSM2891210,GSM2891211,GSM2891212,GSM2891213,GSM2891214,GSM2891215,GSM2891216,GSM2891217,GSM2891218,GSM2891219,GSM2891220,GSM2891221,GSM2891222,GSM2891223,GSM2891224,GSM2891225,GSM2891226,GSM2891227,GSM2891228,GSM2891229,GSM2891230,GSM2891231,GSM2891232,GSM2891233,GSM2891234,GSM2891235,GSM2891236,GSM2891237,GSM2891238,GSM2891239,GSM2891240,GSM2891241,GSM2891242,GSM2891243,GSM2891244,GSM2891245,GSM2891246,GSM2891247,GSM2891248,GSM2891249,GSM2891250,GSM2891251,GSM2891252,GSM2891253,GSM2891254,GSM2891255,GSM2891256,GSM2891257,GSM2891258,GSM2891259,GSM2891260,GSM2891261,GSM2891262,GSM2891263,GSM2891264
-Esophageal_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Esophageal_Cancer,,,,,,,,,,,,,,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
 Gender,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0

output/preprocess/Esophageal_Cancer/code/GSE100843.py

@@ -0,0 +1,221 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE100843"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE100843"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Esophageal_Cancer/GSE100843.csv"
+out_gene_data_file = "./output/z3/preprocess/Esophageal_Cancer/gene_data/GSE100843.csv"
+out_clinical_data_file = "./output/z3/preprocess/Esophageal_Cancer/clinical_data/GSE100843.csv"
+json_path = "./output/z3/preprocess/Esophageal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability (from background: microarrays for global gene expression)
+is_gene_available = True
+
+# 2) Variable availability assessment based on the provided Sample Characteristics Dictionary
+# Sample Characteristics Dictionary (from previous step output)
+sample_char_dict = {
+    0: ["tissue: Barrett's esophagus segment", "tissue: Normal esophageal squamous mucosa"],
+    1: ["arm: Arm B", "arm: Arm A"],
+    2: ["timepoint (t0=before, t1=after): T0", "timepoint (t0=before, t1=after): T1"],
+}
+
+# Trait is Esophageal_Cancer; this dataset contains Barrett's esophagus and normal squamous tissue,
+# with vitamin D intervention timepoints and arms. There is no explicit or inferable esophageal cancer status.
+trait_row = None
+
+# No age or gender fields exist in the sample characteristics dictionary.
+age_row = None
+gender_row = None
+
+# 2.2 Conversion functions
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    if isinstance(value, (int, float)):
+        return str(value)
+    parts = str(value).split(":", 1)
+    val = parts[1] if len(parts) == 2 else parts[0]
+    return val.strip()
+
+def convert_trait(value):
+    # Binary mapping for Esophageal_Cancer if ever present: cancer=1, non-cancer/control=0; else None
+    v = _after_colon(value).lower()
+    if not v:
+        return None
+    # Positive indicators
+    pos_markers = ["esophageal cancer", "esophageal carcinoma", "escc", "eac", "adenocarcinoma", "squamous cell carcinoma"]
+    if any(m in v for m in pos_markers):
+        return 1
+    # Negative indicators
+    neg_markers = ["control", "normal", "healthy", "no cancer", "benign"]
+    if any(m in v for m in neg_markers):
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if not v:
+        return None
+    # extract first number (years) if present
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        return float(m.group(0))
+    except Exception:
+        return None
+
+def convert_gender(value):
+    v = _after_colon(value).lower()
+    if not v:
+        return None
+    if v in ["female", "f", "woman", "women"]:
+        return 0
+    if v in ["male", "m", "man", "men"]:
+        return 1
+    # handle common encodings
+    if v.startswith("fem"):
+        return 0
+    if v.startswith("mal"):
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None -> clinical variable not available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine appropriate columns for probe IDs and gene symbols from gene_annotation
+probe_col = 'ID'
+gene_symbol_col = 'gene_assignment' if 'gene_assignment' in gene_annotation.columns else (
+    'mrna_assignment' if 'mrna_assignment' in gene_annotation.columns else None
+)
+
+if gene_symbol_col is None:
+    raise ValueError("Could not find a suitable gene symbol column in gene annotation.")
+
+# 2. Get mapping dataframe: probe ID -> gene symbols (embedded text to be parsed by helper)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. Link and downstream steps only if clinical data exists
+if 'selected_clinical_data' in locals() and isinstance(selected_clinical_data, pd.DataFrame):
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Bias check and remove biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final quality validation and save metadata
+    note = "INFO: Clinical traits available and linked; bias assessed and demographics filtered if biased."
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note=note
+    )
+
+    # 6. Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Clinical/trait data unavailable; skip linking and record initial filtering status without triggering final validation
+    print("Skipping linking and downstream steps: trait/clinical data unavailable. Gene expression data was processed and saved.")
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Esophageal_Cancer/code/GSE104958.py

@@ -0,0 +1,196 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE104958"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE104958"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Esophageal_Cancer/GSE104958.csv"
+out_gene_data_file = "./output/z3/preprocess/Esophageal_Cancer/gene_data/GSE104958.csv"
+out_clinical_data_file = "./output/z3/preprocess/Esophageal_Cancer/clinical_data/GSE104958.csv"
+json_path = "./output/z3/preprocess/Esophageal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Gene expression availability (based on DNA microarray description in background)
+is_gene_available = True
+
+# 2) Variable availability and converters
+# From the Sample Characteristics Dictionary, trait (Esophageal_Cancer) can be inferred from 'tissue: cancer tissue' vs 'tissue: normal tissue' at key 1
+trait_row = 1
+age_row = None
+gender_row = None
+
+def convert_trait(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip().lower()
+    if s in {'', 'na', 'n/a', 'nan', 'unknown', 'none'}:
+        return None
+    # Map normal to 0
+    if 'normal' in s and 'abnormal' not in s:
+        return 0
+    # Map cancer/tumor-related terms to 1
+    cancer_terms = ['cancer', 'tumor', 'tumour', 'carcinoma', 'malignan']
+    if any(term in s for term in cancer_terms):
+        return 1
+    return None
+
+def convert_age(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip().lower().replace('years', '').replace('year', '').replace('yrs', '').replace('yr', '').replace('y/o', '').replace('yo', '').replace('y', '')
+    s = s.replace('~', '').replace('about', '').replace('approximately', '').strip()
+    # Keep only digits, decimal point, and minus sign
+    filtered = ''.join(ch for ch in s if (ch.isdigit() or ch in {'.', '-'}))
+    try:
+        val = float(filtered)
+        if 0 < val < 120:
+            return val
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip().lower()
+    if s in {'', 'na', 'n/a', 'nan', 'unknown', 'none'}:
+        return None
+    if s in {'female', 'f', 'woman', 'women', 'girl'}:
+        return 0
+    if s in {'male', 'm', 'man', 'men', 'boy'}:
+        return 1
+    # Heuristics
+    if 'female' in s:
+        return 0
+    if 'male' in s:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print(clinical_preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify columns for probe IDs and gene symbols based on the annotation preview
+probe_col = 'ID'
+gene_col = 'GENE_SYMBOL'
+
+# Build probe-to-gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    True,
+    cohort,
+    json_path,
+    True,
+    True,
+    is_trait_biased,
+    unbiased_linked_data,
+    note="INFO: Age and Gender not available; trait derived from tissue field."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Esophageal_Cancer/code/GSE107754.py

@@ -0,0 +1,195 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE107754"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE107754"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Esophageal_Cancer/GSE107754.csv"
+out_gene_data_file = "./output/z3/preprocess/Esophageal_Cancer/gene_data/GSE107754.csv"
+out_clinical_data_file = "./output/z3/preprocess/Esophageal_Cancer/clinical_data/GSE107754.csv"
+json_path = "./output/z3/preprocess/Esophageal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Whole human genome gene expression microarrays per series description.
+
+# 2) Variable availability and converters
+
+# Determine rows from the Sample Characteristics Dictionary
+trait_row = 2      # 'tissue: Esophagus cancer' present among diverse tissues
+age_row = None     # No age information found
+gender_row = 0     # 'gender: Female' / 'gender: Male'
+
+# Converters
+def convert_trait(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    header = parts[0].strip().lower() if len(parts) > 1 else ""
+    value = parts[1].strip().lower() if len(parts) > 1 else s.strip().lower()
+    # Heuristic mapping:
+    # - Positive (1): any 'tissue' entry mentioning esoph (covers esophagus/esophageal)
+    # - Negative (0): other 'tissue' entries
+    # - Unknown (None): entries that are not about tissue (e.g., biopsy location)
+    if header == "tissue":
+        if "esoph" in value:
+            return 1
+        # Any other tissue is considered non-esophageal cancer
+        return 0
+    # Not a tissue field -> cannot infer trait confidently
+    return None
+
+def convert_gender(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    value = parts[1].strip().lower() if len(parts) > 1 else s.strip().lower()
+    if value in ["male", "m"]:
+        return 1
+    if value in ["female", "f"]:
+        return 0
+    return None
+
+def convert_age(x):
+    # Not used because age_row is None; included for completeness.
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    value = parts[1].strip() if len(parts) > 1 else s.strip()
+    # Extract leading numeric
+    try:
+        # Remove common non-numeric trailing characters
+        value_clean = "".join(ch for ch in value if (ch.isdigit() or ch == "." or ch == "-"))
+        return float(value_clean) if value_clean not in ["", "-", "."] else None
+    except Exception:
+        return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+
+    # Ensure output directory exists and save
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for mapping: probe ID ('ID') and gene symbol ('GENE_SYMBOL')
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Preserve the original probe-level data and apply mapping to obtain gene-level expression
+probe_level_data = gene_data
+gene_data = apply_gene_mapping(probe_level_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data (fix variable name)
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and remove biased covariates
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+covariate_cols = [trait, 'Age', 'Gender']
+gene_cols = [c for c in unbiased_linked_data.columns if c not in covariate_cols]
+is_gene_available_final = (len(unbiased_linked_data) > 0) and (len(gene_cols) > 0)
+is_trait_available_final = (trait in unbiased_linked_data.columns) and (len(unbiased_linked_data) > 0)
+
+note = ("INFO: Trait inferred as esophageal cancer (1) vs. other tissues (0) from 'tissue' field within a heterogeneous "
+        "metastatic cohort; age unavailable; gender present. Gene symbols normalized via NCBI synonyms.")
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked dataset if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Esophageal_Cancer/code/GSE131027.py

@@ -0,0 +1,162 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE131027"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE131027"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Esophageal_Cancer/GSE131027.csv"
+out_gene_data_file = "./output/z3/preprocess/Esophageal_Cancer/gene_data/GSE131027.csv"
+out_clinical_data_file = "./output/z3/preprocess/Esophageal_Cancer/clinical_data/GSE131027.csv"
+json_path = "./output/z3/preprocess/Esophageal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+
+# 1) Gene expression availability
+is_gene_available = True  # Overall design mentions "expression features", suggesting mRNA expression data.
+
+# 2) Variable availability and converters
+
+# Trait: use cancer type to define Esophageal_Cancer (binary: esophageal vs others)
+trait_row = 1  # 'cancer: ...'
+age_row = None
+gender_row = None
+
+def convert_trait(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    # Extract value after the first colon if present
+    if ':' in s:
+        _, val = s.split(':', 1)
+    else:
+        val = s
+    v = val.strip().lower()
+    if v in {'', 'na', 'n/a', 'none', 'unknown'}:
+        return None
+    # Positive if esophageal/oesophageal cancer
+    if ('oesoph' in v) or ('esoph' in v):
+        return 1
+    return 0
+
+# Age and Gender not available
+convert_age = None
+convert_gender = None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview_selected_clinical = preview_df(selected_clinical_df)
+
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the relevant columns for mapping
+id_col = 'ID'  # Probe IDs in annotation match probe IDs in expression data
+gene_symbol_col = 'Gene Symbol'  # Column containing gene symbols
+
+# Build the mapping dataframe (probe -> gene symbol)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and remove biased covariates
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note="INFO: Probe-to-gene mapping via platform annotation; gene symbols normalized using NCBI synonyms."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Esophageal_Cancer/code/GSE156915.py

@@ -0,0 +1,215 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE156915"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE156915"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Esophageal_Cancer/GSE156915.csv"
+out_gene_data_file = "./output/z3/preprocess/Esophageal_Cancer/gene_data/GSE156915.csv"
+out_clinical_data_file = "./output/z3/preprocess/Esophageal_Cancer/clinical_data/GSE156915.csv"
+json_path = "./output/z3/preprocess/Esophageal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Whole transcriptome mentioned in background info
+
+# 2) Variable availability and conversion functions
+
+# Since this cohort is colorectal cancer and contains no explicit esophageal cancer status,
+# no usable trait/age/gender fields are present in the sample characteristics dictionary.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        return val.strip()
+    return x
+
+def convert_trait(x):
+    """
+    Map esophageal cancer presence to binary: 1 for esophageal/oesophageal cancer, 0 for others.
+    Unknown/ambiguous -> None.
+    """
+    val = _extract_value(x)
+    if val is None or val == "":
+        return None
+    s = str(val).lower()
+    # Positive indicators for esophageal cancer
+    if any(k in s for k in ["esophageal", "oesophageal", "esophagus", "oesophagus"]):
+        # Exclude explicit normal controls if present
+        if any(k in s for k in ["normal", "control", "healthy", "adjacent normal"]):
+            return 0
+        return 1
+    # If explicitly other cancers or non-esophageal tissues
+    if any(k in s for k in ["colorectal", "crc", "colon", "rectal", "stomach", "gastric", "breast", "lung", "liver", "kidney"]):
+        return 0
+    # Generic normal/control indications
+    if any(k in s for k in ["normal", "control", "healthy", "adjacent normal"]):
+        return 0
+    return None
+
+def convert_age(x):
+    """
+    Convert age to continuous float in years.
+    Extract numbers from strings; return None if missing or invalid.
+    """
+    val = _extract_value(x)
+    if val is None or val == "":
+        return None
+    s = str(val).lower()
+    # Find first number (int or float)
+    m = re.search(r"[-+]?\d*\.?\d+", s)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+        # Simple sanity check for human age
+        if 0 <= age <= 120:
+            return age
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    """
+    Map gender to binary: female -> 0, male -> 1. Unknown -> None.
+    """
+    val = _extract_value(x)
+    if val is None or val == "":
+        return None
+    s = str(val).strip().lower()
+    if s in ["male", "m"]:
+        return 1
+    if s in ["female", "f"]:
+        return 0
+    # Handle common encodings
+    if "male" in s:
+        return 1
+    if "female" in s:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip if trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = False")
+
+# Step 5: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Guard based on trait availability from Step 2
+if 'trait_row' in globals() and trait_row is None:
+    # No clinical/trait data available: skip linking and downstream steps
+    linked_data = None  # For safety, define the variable
+    note = ("WARNING: Trait clinical data is unavailable for this cohort; "
+            "skipping linking, missing-value handling, and bias assessment. "
+            "Only normalized gene expression was saved.")
+    # Final metadata validation (dataset not usable due to missing trait)
+    _ = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,  # Placeholder; trait not available
+        df=normalized_gene_data.T,  # Use samples as rows for validation shape
+        note=note
+    )
+else:
+    # 2. Link clinical and genetic data
+    if 'selected_clinical_df' not in globals():
+        # Try to load if it was saved in Step 2
+        if os.path.exists(out_clinical_data_file):
+            selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+        else:
+            raise RuntimeError("Clinical data not found for linking despite trait_row not being None.")
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Bias assessment and removal of biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and cohort info saving
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Linked data generated and quality-checked."
+    )
+
+    # 6. Save usable linked dataset
+    if is_usable:
+        unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Esophageal_Cancer/code/GSE218109.py

@@ -0,0 +1,187 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE218109"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE218109"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Esophageal_Cancer/GSE218109.csv"
+out_gene_data_file = "./output/z3/preprocess/Esophageal_Cancer/gene_data/GSE218109.csv"
+out_clinical_data_file = "./output/z3/preprocess/Esophageal_Cancer/clinical_data/GSE218109.csv"
+json_path = "./output/z3/preprocess/Esophageal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1) Gene expression data availability
+is_gene_available = True  # Transcriptional profiling of ESCC tumors implies gene expression data
+
+# 2) Variable availability
+trait_row = None  # All samples are ESCC tumors; no case-control variation for Esophageal_Cancer
+age_row = 1       # 'age: <number>'
+gender_row = 0    # 'Sex: M' / 'Sex: F'
+
+# 2.2) Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    # Extract substring after the first colon
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(x).strip()
+
+def convert_trait(x):
+    # Not used because trait_row is None, but provide a safe stub returning None
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == "" or v.lower() in {"na", "n/a", "none", "nan"}:
+        return None
+    try:
+        return int(float(v))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if not v:
+        return None
+    v_low = v.strip().lower()
+    if v_low in {"f", "female", "woman", "women"}:
+        return 0
+    if v_low in {"m", "male", "man", "men"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction
+# Skipped because trait_row is None (no usable trait variation for Esophageal_Cancer in this cohort)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide mapping columns based on observed identifiers
+probe_col = 'ID'            # Matches the numeric probe IDs seen in gene_data index
+gene_symbol_col = 'GENE_SYMBOL'  # Gene symbols column in annotation
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data if clinical trait data exists
+linked_data = None
+selected_clinical = None
+
+# Try to use in-memory clinical data if present
+if 'selected_clinical_data' in globals():
+    selected_clinical = globals()['selected_clinical_data']
+
+# If not in memory, try to load from file
+if selected_clinical is None and os.path.exists(out_clinical_data_file):
+    try:
+        selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+    except Exception:
+        selected_clinical = None
+
+# Only link if trait row exists in clinical data
+if isinstance(selected_clinical, pd.DataFrame) and (trait in selected_clinical.index):
+    linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)
+
+# 3-6. Handle two workflows depending on clinical trait availability
+if linked_data is not None:
+    # 3. Missing value handling
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Bias checks
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Clinical trait, age, and gender were linked successfully."
+    )
+
+    # 6. Save linked data only if usable
+    if is_usable:
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # Trait data unavailable (no usable trait row). Record metadata and skip linking/analysis.
+    # Provide a non-empty df (gene-only) to avoid 'abnormality' override.
+    df_for_record = normalized_gene_data.T  # samples x genes
+
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=df_for_record,
+        note="INFO: Trait (Esophageal_Cancer) is unavailable/constant in this cohort (all ESCC tumors; NS+ vs NS- design). Clinical extraction for trait was skipped."
+    )
+    # Do not save out_data_file when unusable

output/preprocess/Esophageal_Cancer/code/GSE55857.py

@@ -0,0 +1,117 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE55857"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE55857"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Esophageal_Cancer/GSE55857.csv"
+out_gene_data_file = "./output/z3/preprocess/Esophageal_Cancer/gene_data/GSE55857.csv"
+out_clinical_data_file = "./output/z3/preprocess/Esophageal_Cancer/clinical_data/GSE55857.csv"
+json_path = "./output/z3/preprocess/Esophageal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability (SuperSeries of small non-coding RNAs -> not suitable mRNA expression)
+is_gene_available = False
+
+# 2) Variable availability and conversion functions
+
+# Trait (Esophageal_Cancer): inferred from 'tissue: ESCC normal' vs 'tissue: ESCC tumor'
+trait_row = 1  # available
+age_row = None  # not available
+gender_row = None  # not available
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    vl = v.lower()
+    # ESCC tumor -> 1, ESCC normal/control -> 0
+    if any(k in vl for k in ["tumor", "cancer", "carcinoma", "malignant"]):
+        # guard against phrases like "non-tumor"
+        if "non-tumor" in vl or "non tumor" in vl or "benign" in vl or "normal" in vl or "control" in vl:
+            return 0
+        return 1
+    if any(k in vl for k in ["normal", "control", "adjacent normal", "healthy", "non-tumor", "non tumor", "benign"]):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    return float(m.group(1)) if m else None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    vl = v.lower()
+    if vl in ["m", "male"]:
+        return 1
+    if vl in ["f", "female", "woman", "women"]:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Esophageal_Cancer/code/GSE66258.py

@@ -0,0 +1,123 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE66258"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE66258"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Esophageal_Cancer/GSE66258.csv"
+out_gene_data_file = "./output/z3/preprocess/Esophageal_Cancer/gene_data/GSE66258.csv"
+out_clinical_data_file = "./output/z3/preprocess/Esophageal_Cancer/clinical_data/GSE66258.csv"
+json_path = "./output/z3/preprocess/Esophageal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import pandas as pd
+
+# 1) Gene expression availability (sncRNA-only => not suitable)
+is_gene_available = False
+
+# 2) Variable availability
+# From the provided sample characteristics:
+# 0 -> tissue: esophageal squamous cell carcinoma (constant)
+# 1 -> sample id: 1..30 (identifier, not a phenotype)
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Converters
+def _after_colon(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    return s if s != '' else None
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = v.lower()
+    # Map tumor/cancer/ESCC to case=1; normal/control to 0
+    if any(k in s for k in ['esophageal squamous cell carcinoma', 'escc', 'tumor', 'cancer', 'carcinoma']):
+        return 1
+    if any(k in s for k in ['normal', 'control', 'adjacent', 'non-tumor', 'non tumor', 'healthy']):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = v.lower()
+    # Extract the first number (handles formats like "65", "65 years", "60-70")
+    m = re.search(r'(\d+(\.\d+)?)', s)
+    if not m:
+        return None
+    try:
+        val = float(m.group(1))
+        return int(val) if val.is_integer() else val
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = v.strip().lower()
+    if s in ['male', 'm', 'man', 'boy']:
+        return 1
+    if s in ['female', 'f', 'woman', 'girl']:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If trait_row were available:
+# selected_clinical_df = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_clinical_df)
+# selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Esophageal_Cancer/code/GSE75241.py

@@ -0,0 +1,207 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE75241"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE75241"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Esophageal_Cancer/GSE75241.csv"
+out_gene_data_file = "./output/z3/preprocess/Esophageal_Cancer/gene_data/GSE75241.csv"
+out_clinical_data_file = "./output/z3/preprocess/Esophageal_Cancer/clinical_data/GSE75241.csv"
+json_path = "./output/z3/preprocess/Esophageal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Based on series title/summary indicating gene expression profiling (ESCC vs mucosa)
+
+# 2) Variable availability and conversion functions
+# From the sample characteristics, tissue status (tumor vs nonmalignant mucosa) is available at key 1
+trait_row = 1
+age_row = None
+gender_row = None
+
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip().lower()
+    # Check normal first to avoid substring confounding (e.g., "non-tumor" contains "tumor")
+    normal_markers = ['nonmalignant', 'non-malignant', 'non tumor', 'non-tumor', 'normal', 'adjacent normal', 'surrounding mucosa']
+    if any(m in s for m in normal_markers):
+        return 0
+    cancer_markers = ['tumor', 'carcinoma', 'cancer', 'escc', 'scc']
+    if any(m in s for m in cancer_markers):
+        return 1
+    return None
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip().lower()
+    m = re.search(r'(\d+(\.\d+)?)', s)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip().lower()
+    if s in ['male', 'm', 'man', 'boy']:
+        return 1
+    if s in ['female', 'f', 'woman', 'girl']:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (since trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import pandas as pd
+
+# Decide columns: probe IDs are in 'ID'; gene symbols are embedded in 'gene_assignment' or 'mrna_assignment'
+ann = gene_annotation.copy()
+# Ensure columns exist
+if 'gene_assignment' not in ann.columns:
+    ann['gene_assignment'] = pd.NA
+if 'mrna_assignment' not in ann.columns:
+    ann['mrna_assignment'] = pd.NA
+
+# Replace placeholder '---' with NA and prioritize gene_assignment, then fallback to mrna_assignment
+ann['gene_assignment'] = ann['gene_assignment'].replace('---', pd.NA)
+ann['mrna_assignment'] = ann['mrna_assignment'].replace('---', pd.NA)
+ann['GENE_TEXT'] = ann['gene_assignment'].combine_first(ann['mrna_assignment'])
+
+# 2) Build mapping dataframe (probe ID -> gene symbol text)
+mapping_df = get_gene_mapping(ann, prob_col='ID', gene_col='GENE_TEXT')
+
+# 3) Apply mapping to convert probe-level to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Ensure clinical features are available in this session
+if 'selected_clinical_df' not in globals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Derive availability flags and cast to native Python bool
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+
+# 3) Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4) Bias check and remove biased demographic features if needed
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+is_trait_biased = bool(is_trait_biased)
+
+# 5) Final validation and save cohort info
+note = "INFO: Paired ESCC tumor vs adjacent mucosa; Age/Gender not provided; Genes normalized via NCBI synonyms."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6) Save linked dataset if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Esophageal_Cancer/code/GSE77790.py

@@ -0,0 +1,183 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE77790"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE77790"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Esophageal_Cancer/GSE77790.csv"
+out_gene_data_file = "./output/z3/preprocess/Esophageal_Cancer/gene_data/GSE77790.csv"
+out_clinical_data_file = "./output/z3/preprocess/Esophageal_Cancer/clinical_data/GSE77790.csv"
+json_path = "./output/z3/preprocess/Esophageal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+
+# 1) Gene expression data availability
+is_gene_available = True  # Agilent whole genome microarrays -> mRNA gene expression
+
+# 2) Variable availability and conversion functions
+# From the sample characteristics dictionary, esophageal cancer status can be inferred from key 1 ("cell type: ...")
+trait_row = 1
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    val = _extract_value(x)
+    if not val:
+        return None
+    s = val.lower()
+    if s in {'na', 'n/a', 'unknown', 'nan'}:
+        return None
+    # Esophageal cancer vs. others
+    if 'esoph' in s:
+        return 1
+    # Map any other known cell types to 0
+    return 0
+
+def convert_age(x):
+    # Not used (age_row is None), but define for interface completeness
+    val = _extract_value(x)
+    if not val:
+        return None
+    s = val.lower().replace('years', '').replace('year', '').strip()
+    try:
+        return float(s)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Not used (gender_row is None), but define for interface completeness
+    val = _extract_value(x)
+    if not val:
+        return None
+    s = val.lower()
+    if s in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if s in {'male', 'm', 'man', 'men'}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2. Identify columns for mapping and create mapping dataframe
+# Expression data row identifiers are numeric strings matching the 'ID' column in the annotation.
+# Gene symbols are stored in the 'GENE_SYMBOL' column.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and cohort info saving
+note = ("INFO: Cell line microarray dataset; trait indicates esophageal cancer cell type inferred from 'cell type'. "
+        "Trait distribution appears imbalanced based on preview.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Esophageal_Cancer/code/TCGA.py

@@ -0,0 +1,336 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Esophageal_Cancer/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Esophageal_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Esophageal_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Esophageal_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# 1) Select the most relevant TCGA cohort directory for the current trait
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+trait_terms = ["esophageal", "esophagus", "esca"]
+
+def score_dir(name: str) -> int:
+    n = name.lower()
+    score = 0
+    if "esophageal" in n or "esophagus" in n:
+        score += 10
+    if "esca" in n:
+        score += 5
+    if "tcga" in n:
+        score += 1
+    return score
+
+scored = sorted([(score_dir(d), d) for d in subdirs], reverse=True)
+best_dir = scored[0][1] if scored and scored[0][0] > 0 else None
+
+if best_dir is None:
+    # No suitable directory found: record and stop early
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    cohort_dir = os.path.join(tcga_root_dir, best_dir)
+
+    # 2) Identify clinical and genetic file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3) Load files
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+
+    # 4) Print clinical column names
+    print(list(clinical_df.columns))
+
+# Step 2: Find Candidate Demographic Features
+import os
+import re
+import pandas as pd
+
+# Helper functions to identify candidate columns
+def _tokenize_col(name: str):
+    return [t for t in re.split(r'[^a-z0-9]+', name.lower()) if t]
+
+def _is_age_col(name: str) -> bool:
+    # Consider columns with explicit 'age' token, or specific birth-derived numeric proxies
+    nl = name.lower()
+    tokens = _tokenize_col(name)
+    if 'age' in tokens:
+        return True
+    birth_proxies = ['days_to_birth', 'date_of_birth', 'year_of_birth', 'birth_year', 'dob']
+    return any(p in nl for p in birth_proxies)
+
+def _is_gender_col(name: str) -> bool:
+    tokens = _tokenize_col(name)
+    return ('gender' in tokens) or ('sex' in tokens)
+
+# Locate ESCA cohort directory
+cohort_dir = None
+if os.path.isdir(tcga_root_dir):
+    subdirs = [d for d in os.listdir(tcga_root_dir)
+               if os.path.isdir(os.path.join(tcga_root_dir, d))]
+    priority = []
+    for d in subdirs:
+        dl = d.lower()
+        if dl == 'esca' or dl == 'tcga_esca':
+            priority.append(d)
+    if not priority:
+        priority = [d for d in subdirs if ('esca' in d.lower() or 'esophageal' in d.lower())]
+    if priority:
+        cohort_dir = os.path.join(tcga_root_dir, priority[0])
+
+# Get clinical file path
+clinical_df = None
+if cohort_dir is not None:
+    try:
+        clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+        try:
+            clinical_df = pd.read_csv(clinical_file_path, sep='\t', header=0, index_col=0, dtype=str, low_memory=False)
+        except Exception:
+            clinical_df = pd.read_csv(clinical_file_path, sep=',', header=0, index_col=0, dtype=str, low_memory=False)
+    except Exception:
+        clinical_df = None
+
+# Derive candidate columns from available columns
+candidate_age_cols = []
+candidate_gender_cols = []
+if clinical_df is not None:
+    cols = clinical_df.columns.tolist()
+    for c in cols:
+        if _is_age_col(c):
+            candidate_age_cols.append(c)
+        if _is_gender_col(c):
+            candidate_gender_cols.append(c)
+else:
+    # Fallback to the provided list from previous step if file loading failed
+    provided_cols = ['CDE_ID_3226963', '_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'additional_treatment_completion_success_outcome', 'age_at_initial_pathologic_diagnosis', 'age_began_smoking_in_years', 'alcohol_history_documented', 'amount_of_alcohol_consumption_per_day', 'antireflux_treatment_type', 'barretts_esophagus', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'city_of_procurement', 'clinical_M', 'clinical_N', 'clinical_T', 'clinical_stage', 'columnar_metaplasia_present', 'columnar_mucosa_dysplasia', 'columnar_mucosa_goblet_cell_present', 'country_of_birth', 'country_of_procurement', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_additional_surgery_procedure', 'days_to_new_tumor_event_after_initial_treatment', 'eastern_cancer_oncology_group', 'esophageal_tumor_cental_location', 'esophageal_tumor_involvement_site', 'form_completion_date', 'frequency_of_alcohol_consumption', 'gender', 'goblet_cells_present', 'h_pylori_infection', 'height', 'histological_type', 'history_of_esophageal_cancer', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'init_pathology_dx_method_other', 'initial_diagnosis_by', 'initial_pathologic_diagnosis_method', 'initial_weight', 'is_ffpe', 'karnofsky_performance_score', 'lost_follow_up', 'lymph_node_examined_count', 'lymph_node_metastasis_radiographic_evidence', 'neoplasm_histologic_grade', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type', 'new_neoplasm_occurrence_anatomic_site_text', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'number_of_lymphnodes_positive_by_he', 'number_of_lymphnodes_positive_by_ihc', 'number_of_relatives_diagnosed', 'number_pack_years_smoked', 'oct_embedded', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'person_neoplasm_cancer_status', 'planned_surgery_status', 'postoperative_rx_tx', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'progression_determined_by', 'radiation_therapy', 'reflux_history', 'residual_tumor', 'sample_type', 'sample_type_id', 'state_province_of_procurement', 'stopped_smoking_year', 'system_version', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tobacco_smoking_history', 'treatment_prior_to_surgery', 'tumor_tissue_site', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_ESCA_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_ESCA_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_ESCA_mutation_bcm_gene', '_GENOMIC_ID_data/public/TCGA/ESCA/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_ESCA_exp_HiSeq_exon', '_GENOMIC_ID_TCGA_ESCA_PDMRNAseq', '_GENOMIC_ID_TCGA_ESCA_hMethyl450', '_GENOMIC_ID_TCGA_ESCA_RPPA', '_GENOMIC_ID_TCGA_ESCA_exp_HiSeqV2', '_GENOMIC_ID_TCGA_ESCA_exp_HiSeq', '_GENOMIC_ID_TCGA_ESCA_miRNA_HiSeq', '_GENOMIC_ID_TCGA_ESCA_mutation_ucsc_maf_gene', '_GENOMIC_ID_TCGA_ESCA_gistic2', '_GENOMIC_ID_TCGA_ESCA_gistic2thd', '_GENOMIC_ID_TCGA_ESCA_mutation_broad_gene', '_GENOMIC_ID_TCGA_ESCA_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_ESCA_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_ESCA_PDMRNAseqCNV']
+    for c in provided_cols:
+        if _is_age_col(c):
+            candidate_age_cols.append(c)
+        if _is_gender_col(c):
+            candidate_gender_cols.append(c)
+
+# Print candidate lists in the required format
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Extract and preview candidate columns if clinical data is available
+if clinical_df is not None:
+    if candidate_age_cols:
+        try:
+            age_preview = preview_df(clinical_df[[c for c in candidate_age_cols if c in clinical_df.columns]], n=5)
+            print(age_preview)
+        except Exception:
+            pass
+    if candidate_gender_cols:
+        try:
+            gender_preview = preview_df(clinical_df[[c for c in candidate_gender_cols if c in clinical_df.columns]], n=5)
+            print(gender_preview)
+        except Exception:
+            pass
+
+# Step 3: Select Demographic Features
+import pandas as pd
+import numpy as np
+
+# Default to None
+age_col = None
+gender_col = None
+
+def select_age_col(clinical_df: pd.DataFrame, candidates: list) -> str:
+    best_col = None
+    best_score = -1
+    for col in candidates:
+        if col not in clinical_df.columns:
+            continue
+        s = clinical_df[col]
+        non_null_ratio = s.notna().mean()
+        if non_null_ratio < 0.3:  # too sparse
+            continue
+
+        # Try to interpret as numeric (preserve sign for 'days_to_birth')
+        s_num = pd.to_numeric(s, errors='coerce')
+        s_valid = s_num.dropna()
+        if len(s_valid) == 0:
+            continue
+
+        # Proportion that looks like a direct age (0-120 years)
+        pct_age_range = ((s_valid >= 0) & (s_valid <= 120)).mean()
+
+        # Proportion that looks like days to birth (large negative)
+        pct_days_to_birth = (s_valid <= -3650).mean()
+
+        # Score prioritizes plausible age range and penalizes "days_to_birth"-like distributions
+        score = (pct_age_range - 0.3 * pct_days_to_birth) * non_null_ratio
+
+        # Small heuristic boost if column name contains 'age' and not 'began_smoking'
+        name_lower = col.lower()
+        if 'age' in name_lower and 'began_smoking' not in name_lower:
+            score *= 1.1
+
+        if score > best_score:
+            best_score = score
+            best_col = col
+    return best_col
+
+def select_gender_col(clinical_df: pd.DataFrame, candidates: list) -> str:
+    best_col = None
+    best_score = -1
+    valid_tokens = {'male', 'female', 'm', 'f'}
+    for col in candidates:
+        if col not in clinical_df.columns:
+            continue
+        s = clinical_df[col].astype(str).str.strip().str.lower()
+        non_null_ratio = (~s.isin(['', 'nan', 'none'])).mean()
+        if non_null_ratio < 0.3:
+            continue
+        s_valid = s[~s.isin(['', 'nan', 'none'])]
+        if len(s_valid) == 0:
+            continue
+        pct_valid_gender = s_valid.isin(valid_tokens).mean()
+        score = pct_valid_gender * non_null_ratio
+        if score > best_score:
+            best_score = score
+            best_col = col
+    return best_col
+
+try:
+    # Use clinical_df and candidate lists if available
+    if 'clinical_df' in globals():
+        # Fallback if candidate lists are not defined
+        candidate_age_cols = candidate_age_cols if 'candidate_age_cols' in globals() else []
+        candidate_gender_cols = candidate_gender_cols if 'candidate_gender_cols' in globals() else []
+
+        chosen_age = select_age_col(clinical_df, candidate_age_cols) if candidate_age_cols else None
+        chosen_gender = select_gender_col(clinical_df, candidate_gender_cols) if candidate_gender_cols else None
+
+        age_col = chosen_age if chosen_age else None
+        gender_col = chosen_gender if chosen_gender else None
+
+        # Print out selected columns and their info
+        print("Selected age_col:", age_col)
+        if age_col:
+            col_series = clinical_df[age_col]
+            print("age_col non-null ratio:", round(col_series.notna().mean(), 4))
+            print("age_col first5:", col_series.head(5).tolist())
+
+        print("Selected gender_col:", gender_col)
+        if gender_col:
+            col_series = clinical_df[gender_col]
+            print("gender_col non-null ratio:", round(col_series.notna().mean(), 4))
+            print("gender_col first5:", col_series.head(5).tolist())
+    else:
+        # Fallback selection purely based on provided candidate names and typical TCGA patterns
+        # Given previews: choose age_at_initial_pathologic_diagnosis for age; gender for gender
+        age_col = 'age_at_initial_pathologic_diagnosis' if 'age_at_initial_pathologic_diagnosis' in (candidate_age_cols if 'candidate_age_cols' in globals() else []) else None
+        gender_col = 'gender' if 'gender' in (candidate_gender_cols if 'candidate_gender_cols' in globals() else []) else None
+
+        print("Selected age_col:", age_col)
+        print("Selected gender_col:", gender_col)
+except Exception as e:
+    # On any unexpected issue, fall back to known-good choices from the preview
+    age_col = 'age_at_initial_pathologic_diagnosis'
+    gender_col = 'gender'
+    print("Selected age_col:", age_col)
+    print("Selected gender_col:", gender_col)
+    print("Note: Fallback due to error:", str(e))
+
+# Step 4: Feature Engineering and Validation
+import os
+import pandas as pd
+
+# 1) Extract and standardize clinical features (trait, Age, Gender)
+# Fallbacks in case variables not defined in prior steps
+try:
+    _ = age_col
+except NameError:
+    age_col = None
+try:
+    _ = gender_col
+except NameError:
+    gender_col = None
+
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# Optionally save clinical data (useful for inspection)
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+selected_clinical_df.to_csv(out_clinical_data_file)
+
+# 2) Normalize gene symbols in genetic data (ensure genes are in index)
+def _looks_like_tcga(series_like) -> float:
+    try:
+        s = pd.Index(series_like).astype(str)
+    except Exception:
+        s = pd.Index([str(x) for x in series_like])
+    return s.str.upper().str.startswith("TCGA-").mean()
+
+# Ensure rows are genes, columns are samples
+rows_tcga_frac = _looks_like_tcga(genetic_df.index)
+cols_tcga_frac = _looks_like_tcga(genetic_df.columns)
+gene_df = genetic_df.copy()
+
+if rows_tcga_frac > cols_tcga_frac:
+    # Rows look like samples -> transpose to genes x samples
+    gene_df = gene_df.T
+
+# Normalize gene symbols and drop unrecognized
+gene_df_norm = normalize_gene_symbols_in_index(gene_df)
+
+# Save normalized gene expression
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_df_norm.to_csv(out_gene_data_file)
+
+# 3) Link clinical and genetic data on sample IDs
+# Clinical index: samples; gene_df_norm columns: samples; transpose gene_df_norm to samples x genes
+gene_df_samples = gene_df_norm.T
+linked_data = selected_clinical_df.join(gene_df_samples, how='inner')
+
+# 4) Handle missing values
+linked_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5) Determine bias and remove biased demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)
+
+# 6) Final validation and save cohort info
+# Ensure pure Python booleans for JSON serialization
+is_gene_available = bool(gene_df_norm.shape[0] > 0 and gene_df_norm.shape[1] > 0)
+is_trait_available = bool((trait in selected_clinical_df.columns) and (selected_clinical_df[trait].notna().sum() > 0))
+trait_biased = bool(trait_biased)
+
+note = "INFO: Trait derived from TCGA sample barcode (01-09 tumor=1, 10-19 normal=0). Gene symbols normalized to HGNC using NCBI synonym mapping."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 7) Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

output/preprocess/Esophageal_Cancer/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE77790": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 32
-  },
-  "GSE75241": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 30
-  },
-  "GSE66258": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 108
-  },
-  "GSE55857": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE218109": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 36
-  },
-  "GSE156915": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 361
-  },
-  "GSE131027": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 92
-  },
-  "GSE107754": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": true,
-    "sample_size": 84
-  },
-  "GSE104958": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 46
-  },
-  "GSE100843": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "TCGA": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 196
-  }
-}
+{"GSE77790": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 32, "note": "INFO: Cell line microarray dataset; trait indicates esophageal cancer cell type inferred from 'cell type'. Trait distribution appears imbalanced based on preview."}, "GSE75241": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 30, "note": "INFO: Paired ESCC tumor vs adjacent mucosa; Age/Gender not provided; Genes normalized via NCBI synonyms."}, "GSE66258": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE55857": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE218109": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "INFO: Trait (Esophageal_Cancer) is unavailable/constant in this cohort (all ESCC tumors; NS+ vs NS- design). Clinical extraction for trait was skipped."}, "GSE156915": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "WARNING: Trait clinical data is unavailable for this cohort; skipping linking, missing-value handling, and bias assessment. Only normalized gene expression was saved."}, "GSE131027": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 92, "note": "INFO: Probe-to-gene mapping via platform annotation; gene symbols normalized using NCBI synonyms."}, "GSE107754": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": true, "sample_size": 71, "note": "INFO: Trait inferred as esophageal cancer (1) vs. other tissues (0) from 'tissue' field within a heterogeneous metastatic cohort; age unavailable; gender present. Gene symbols normalized via NCBI synonyms."}, "GSE104958": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 46, "note": "INFO: Age and Gender not available; trait derived from tissue field."}, "GSE100843": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 196, "note": "INFO: Trait derived from TCGA sample barcode (01-09 tumor=1, 10-19 normal=0). Gene symbols normalized to HGNC using NCBI synonym mapping."}}

output/preprocess/Esophageal_Cancer/gene_data/GSE75241.csv

@@ -461,7 +461,7 @@ AHCY,5.11305,5.55395,5.7752,5.52295,5.672,5.3523,5.7326,5.22685,5.3963,5.5001,5.
 AHCYL1,5.7803,5.4641,5.4835,5.51,5.5091,5.62625,5.4519,5.6326,5.8471,5.6502,5.6218,5.6228,5.7352,5.7495,5.47605,5.6094,5.77815,5.8317,5.58645,5.5269,5.4081,5.7052,5.45775,5.4246,5.56175,5.47435,5.61815,5.65105,5.4739,5.6579
 AHCYL2,4.834515,4.56766,4.98435,4.748085,4.920295,4.69793,4.81372,4.64787,4.95798,4.6921,4.799905,4.635465,4.71589,5.44615,4.72228,4.420855,4.89727,4.728305,4.920145,4.86875,4.629525,5.35515,4.762875,3.909205,4.83494,4.83391,5.1508,4.73828,4.751845,4.91733
 AHDC1,3.3509333333333333,3.2579166666666666,3.4340333333333333,3.1352499999999996,3.352233333333333,3.036096666666667,3.3379999999999996,3.1534600000000004,3.3633666666666664,3.0688,3.2693266666666667,3.118736666666667,3.4543666666666666,3.2220133333333334,3.347933333333333,3.20064,3.5132333333333334,3.25544,3.286326666666667,3.0949733333333334,3.410966666666667,3.0397133333333333,3.2950733333333333,2.9617233333333335,3.4095666666666666,3.107666666666667,3.3535,3.304733333333333,3.2702333333333335,3.063696666666667
-AHI1,3.489535,3.83622,3.56085,3.340875,3.430825,3.82901,3.383395,3.542845,3.729475,3.89125,3.21757,3.58518,3.44321,3.745895,3.175235,3.95892,3.52074,3.523765,3.44234,3.51387,3.26185,3.602235,3.163505,3.240045,3.48989,3.864455,3.66389,4.171685,3.27594,3.84305
+AHI1,3.0446125,3.229635,3.132086,3.0412485,3.0088615,3.1162039999999998,3.1444384999999997,3.0579035,3.1334655,3.293788,3.083642,3.14917,3.1024000000000003,3.1925464999999997,2.9320125,3.291507,3.052683,3.1795195,3.069928,3.084971,3.228033,2.9965905,2.8156705,2.9638945000000003,3.146721,3.5031185000000002,3.1475920000000004,3.3712875,3.094125,3.254849
 AHNAK,6.812474999999999,6.735266666666667,6.734958333333333,6.490225000000001,6.8867416666666665,6.0604,6.640733333333333,6.509075,6.607258333333333,5.565366666666667,6.772016666666666,6.253208333333333,6.656125,5.804129166666667,6.502583333333334,6.256408333333333,6.778091666666667,6.566583333333333,6.788600000000001,5.8367483333333325,6.573766666666667,5.931665000000001,6.772966666666667,6.164483333333333,6.444333333333333,6.360025,6.817475,6.216575000000001,6.636383333333333,6.30955
 AHNAK2,3.8966999999999996,3.978566666666667,3.8694333333333333,3.7386,3.9383666666666666,3.4644,3.6924333333333332,3.6844,3.7202333333333333,3.0408666666666666,3.8016666666666663,3.4946333333333333,3.7804,3.3636666666666666,3.5906333333333333,3.6087333333333333,3.8716666666666666,3.945733333333333,3.7918000000000003,3.289073333333333,3.6444666666666667,3.32504,3.8195666666666668,3.5352333333333337,3.6872333333333334,3.8979,3.8527,3.6704000000000003,3.713433333333333,3.6245999999999996
 AHR,5.07085,5.9695,4.895925,5.2576,4.994915,5.8921,4.79774,5.1719,5.23745,5.21055,4.962915,5.02945,4.929345,5.4035,4.74796,5.8616,5.0655,5.3356,4.939455,5.83575,4.77985,5.8285,4.60302,5.1215,4.952175,5.82015,5.21785,5.7226,4.48685,5.29475
@@ -1414,7 +1414,7 @@ BBS2,2.2709275,2.49221,2.2977475,2.070675,2.275315,2.2863325,2.205035,2.11802,2.
 BBS4,3.32717,3.595865,3.51107,3.55417,3.21578,3.585475,3.277595,3.65976,3.826785,3.57884,3.387885,3.5749,3.30977,3.960085,3.33246,4.173595,3.76894,3.77128,3.39844,4.182245,3.263815,3.69268,3.45609,3.85815,3.293395,3.624645,3.797855,3.52018,3.23481,3.49462
 BBS5,4.984033999999999,4.949167428571428,5.073238714285714,5.008300428571429,4.956804571428572,5.019393,4.791174428571429,4.739202857142857,5.3838919999999995,5.113818,4.903515571428571,5.0120641428571435,4.856042,5.245343714285714,4.921110285714286,5.024544000000001,5.189010857142857,5.170905714285714,5.069401571428571,5.487235714285714,4.752165142857143,5.052059,4.915073714285715,5.087361714285715,5.079292571428572,4.643571142857143,5.291227428571428,5.413462714285714,4.744874428571428,5.129437
 BBS7,3.50519,3.868515,3.50823,3.696555,3.374355,3.691015,3.32986,3.15787,3.827245,3.56065,3.2406,3.37779,3.562255,3.87161,3.199585,3.97327,3.630395,3.560795,3.330825,3.87197,3.233955,3.557765,3.21287,3.54692,3.475565,3.84934,3.66938,3.778075,3.10369,3.718665
-BBS9,16.792078714285715,17.76517055952381,16.98169588095238,17.245168678571428,16.94271769047619,17.83174645238095,16.951241928571427,17.490183559523807,17.447577190476192,18.165604369047617,16.94419792857143,17.10207557142857,16.931825714285715,17.34837938095238,16.85932641666667,17.54836542857143,16.988097595238095,17.65260682142857,17.06096419047619,17.827139773809524,16.724563226190476,17.921165714285713,16.90923694047619,17.313414273809524,16.773351845238096,17.597809023809525,17.423263357142858,18.118864523809524,16.672707,17.63016629761905
+BBS9,17.544880714285714,18.51793555952381,17.75282488095238,18.021905678571425,17.67402869047619,18.510610452380952,17.795486928571428,18.28704655952381,18.17051719047619,18.947874369047618,17.768321928571428,17.91387757142857,17.747563714285715,18.088645380952382,17.613282416666667,18.275403428571426,17.755614595238093,18.480790821428574,17.83373619047619,18.530436773809523,17.563243226190476,18.554026714285712,17.63495794047619,18.086139273809522,17.575120845238096,18.510641023809523,18.155350357142854,18.860063523809526,17.51114,18.416048297619046
 BBX,1.116385,1.24652375,1.1067475,1.199545,1.0762725,1.283,1.05016875,1.04952125,1.19734625,1.24470875,1.095505,1.1514075,1.1166875,1.16623125,1.063555,1.18081625,1.15012875,1.18698,1.113605,1.2383475,1.02787875,1.17839875,1.0328975,1.254675,1.114255,1.2381925,1.19389,1.3546,1.03745375,1.15190625
 BCAM,4.2788,4.689985,4.2484,4.351565,4.13221,4.944435,4.17768,4.477085,4.22285,4.336655,4.25352,4.484815,4.36575,5.19935,4.437825,4.880675,4.443545,4.518035,4.30309,5.6972,4.32711,4.709505,4.34973,4.97559,4.23464,5.0092,4.251115,4.99019,4.322355,4.756835
 BCAN,0.8579411111111112,0.8518444444444444,0.854991111111111,0.87356,0.8787977777777778,0.8390866666666666,0.8694144444444444,0.883,0.8387344444444444,0.8401377777777778,0.8695422222222222,0.8533133333333334,0.8637844444444445,0.8492455555555556,0.8708911111111112,0.8294477777777778,0.8399222222222222,0.8469655555555556,0.8533822222222223,0.8406211111111112,0.8740955555555555,0.8632611111111111,0.8659466666666666,0.8622011111111111,0.8492177777777778,0.8276044444444444,0.8521344444444444,0.81511,0.8756133333333334,0.8564422222222222
@@ -1625,7 +1625,7 @@ BRWD1-AS2,2.629825,2.60693,3.38893,2.953515,2.81253,3.00479,3.15815,3.101405,2.8
 BRWD3,3.1284500000000004,2.8499533333333336,3.09305,2.70602,3.069706666666667,3.0040999999999998,2.8734033333333335,2.7077233333333335,3.1881233333333334,2.91365,3.0444766666666667,2.9611466666666666,3.1290533333333332,2.9044166666666666,2.91637,2.7490433333333333,3.215116666666667,2.9766399999999997,2.96939,2.67559,2.94994,1.9592566666666666,3.1125966666666667,2.77167,3.0121866666666666,2.9153300000000004,3.15126,3.0911000000000004,2.9665733333333333,2.9903166666666667
 BSCL2,3.9172200000000004,3.7453779999999997,4.036259333333334,3.982861333333333,3.919623333333333,3.909112,4.032792,3.98991,3.9299773333333334,4.007014,3.9688713333333334,3.763038,3.9251813333333336,3.9225906666666672,3.932444,3.977926666666667,4.010820666666667,3.8172613333333336,3.8275346666666668,3.9044706666666666,3.9113246666666672,4.11712,3.9288213333333335,3.4755986666666665,3.7162919999999997,3.4380006666666665,3.8451113333333335,3.6916219999999997,4.012476,3.9043593333333333
 BSDC1,2.96415,2.945456666666667,3.0747533333333332,3.026313333333333,3.0412966666666663,3.02122,3.01676,2.990873333333333,3.0748433333333334,3.0321266666666666,2.9581666666666666,2.8737366666666664,3.08817,3.1934466666666665,2.96754,3.074666666666667,3.08388,3.1108100000000003,2.9865399999999998,3.00255,2.9730399999999997,3.0751633333333337,2.9412933333333338,2.9326133333333337,2.9419299999999997,3.012003333333333,3.05801,3.106203333333333,2.92911,3.0488
-BSG,4.714964999999999,4.741125,4.726476666666667,4.865263333333333,4.639186666666666,4.957018333333333,4.749925,4.879748333333334,4.767448333333333,4.782288333333334,4.650650000000001,4.7376016666666665,4.832425000000001,4.99549,4.9541450000000005,4.844026666666666,4.786051666666667,4.8873066666666665,4.80642,4.815848333333333,4.737421666666666,4.934833333333334,4.808933333333334,4.970218333333333,4.736478333333333,4.909511666666667,4.858635,4.754658333333333,4.912136666666667,4.745346666666666
+BSG,7.432639166666666,7.557567083333334,7.4658958333333345,7.579637916666666,7.385345833333333,8.004683333333332,7.596375416666667,7.732619166666666,7.4963879166666665,7.628069166666666,7.4366325,7.476268750000001,7.6177858333333335,7.889622083333334,7.410945,7.63733125,7.533872499999999,7.697964166666667,7.677428333333333,7.914161666666667,7.262655833333333,8.019309166666666,7.491910833333334,7.8370854166666675,7.573845416666667,7.654476666666666,7.793173333333334,7.626831666666667,7.52603375,7.518687916666666
 BSN,3.653715,3.598245,3.65109,3.68642,3.721585,3.510115,3.78692,3.71179,3.57014,3.633765,3.72618,3.55043,3.715945,3.625585,3.691475,3.516675,3.586485,3.6195,3.610225,3.54045,3.675675,3.679385,3.632055,3.596155,3.757765,3.582405,3.539515,3.51473,3.693535,3.57368
 BSND,3.479645,3.219265,3.296145,3.487925,3.4001,3.359655,3.38245,3.36531,3.36321,3.34917,3.506745,3.3181,3.416765,3.36694,3.280885,3.239815,3.19796,3.494055,3.248375,3.229385,3.330095,3.35064,3.344825,3.33927,3.49841,3.307295,3.281425,3.02741,3.29774,3.269445
 BSPRY,2.43489,1.87736,2.4616325,2.23845,2.44376,2.15274,2.503125,2.368235,2.44908,2.009945,2.4974825,2.3087425,2.57415,2.129455,2.4353075,2.2209875,2.39957,2.2205275,2.4324525,1.930535,2.4735575,2.21442,2.37176,1.9095325,2.389595,2.20436,2.39842,2.146575,2.4406625,2.3496425
@@ -3594,6 +3594,7 @@ CYBC1,4.68584,4.837275,4.493225,4.74002,4.355435,4.744895,4.51927,4.78106,4.6285
 CYBRD1,4.897655,5.51395,4.45326,3.941125,4.57498,4.98926,4.692635,5.04635,5.0692,4.83144,4.499475,4.78217,4.35944,5.27175,4.725235,5.321,5.02195,5.56595,5.23945,5.59675,4.891965,5.2684,4.754805,5.39505,4.633835,5.20365,5.03285,5.7331,4.618985,5.33055
 CYC1,5.34005,5.65415,5.26015,5.36045,5.19715,5.4586,5.3079,5.4236,5.40105,5.5241,5.34485,5.27965,5.31105,5.5457,5.3599,5.52115,5.3984,5.43315,5.433,5.61565,5.34355,5.81565,5.2017,5.6586,5.31885,5.5002,5.54435,5.2774,5.30865,5.34725
 CYCS,4.01404,4.03255,3.912695,4.073705,3.907845,3.964345,3.75909,4.04073,4.057485,4.195315,3.87324,3.944935,4.003415,4.020345,4.16122,4.418765,3.851355,3.95142,4.08878,4.336555,4.067065,4.199775,3.485285,4.1855,3.860285,4.02134,4.049285,4.092315,3.97505,4.141155
+CYCSP52,3.148945,2.85705,2.707335,3.075595,3.05974,2.92649,2.30245,3.14858,3.03571,3.078325,3.043825,2.983845,2.958015,2.880095,2.55918,3.129075,3.06067,3.279345,2.95068,3.138465,2.8442,2.677395,2.89648,2.93885,2.645745,3.06603,2.72648,3.20864,2.86307,2.891905
 CYFIP1,3.4356333333333335,3.5414333333333334,3.635666666666667,3.4992,3.4629666666666665,3.284413333333333,3.4364000000000003,3.3299533333333335,3.4506666666666668,3.2689866666666667,3.462666666666667,3.3548333333333336,3.4319666666666664,3.3187166666666665,3.287186666666667,3.28197,3.5436333333333336,3.5038666666666667,3.3560666666666665,3.3502333333333336,3.311436666666667,3.1293133333333336,3.3491,3.3843,3.3541666666666665,3.3229366666666666,3.4373666666666662,3.3108833333333334,3.4244,3.378766666666667
 CYFIP2,2.8339266666666667,2.7094,2.65188,2.7581233333333333,2.61891,2.91708,2.65203,2.8133666666666666,2.9671299999999996,2.865083333333333,2.7042266666666666,2.8234766666666666,2.7633799999999997,3.1820500000000003,2.6724266666666665,2.6044033333333334,2.8333399999999997,2.90817,2.6280566666666667,2.8404433333333334,2.6325066666666666,2.53592,2.5602266666666664,2.54352,2.6939266666666666,2.8914500000000003,2.863566666666667,3.1500033333333337,2.60264,2.9961300000000004
 CYGB,2.0191475,2.114405,2.0006025,2.055275,1.96757,2.3244925,1.9925825,2.1050125,1.9658875,2.1190575,1.9923225,2.179615,2.033375,2.235445,1.9954275,2.235705,2.0409775,2.283165,2.01737,2.31124,1.9768525,2.1454425,2.034625,2.2294375,2.062185,2.3913975,1.907445,2.2325025,2.1266625,2.192175
@@ -4137,7 +4138,7 @@ DND1,1.63889,1.5280316666666665,1.6293383333333333,1.5301466666666668,1.63318333
 DNER,2.10804,2.411,2.0889666666666664,2.6368966666666664,2.15504,2.0738266666666667,2.1473833333333334,2.0815433333333333,2.0647733333333336,2.1259566666666667,2.0978333333333334,2.05769,2.0921,2.1280033333333335,2.2512466666666664,2.0978666666666665,2.0585566666666666,2.104976666666667,2.1917066666666667,2.1304333333333334,2.128366666666667,2.1435866666666668,2.1237533333333336,2.268686666666667,2.18123,2.44495,2.1095466666666667,2.046933333333333,2.17389,2.3726233333333333
 DNHD1,5.570978333333334,5.535575,5.553979999999999,5.575073333333333,5.492495,5.620923333333334,5.441355,5.6564483333333335,5.514191666666667,5.5251850000000005,5.536851666666667,5.497156666666666,5.449526666666666,5.531408333333333,5.500123333333333,5.611378333333333,5.530173333333333,5.500476666666667,5.525116666666667,5.486513333333333,5.464585,5.424015,5.5963683333333325,5.571213333333333,5.580348333333333,5.4513766666666665,5.541948333333333,5.744845,5.397065,5.48984
 DNLZ,1.6621479999999997,1.632732,1.6876460000000002,1.6960760000000001,1.65018,1.661028,1.701076,1.7024260000000002,1.6589099999999999,1.6129419999999999,1.659046,1.684206,1.661456,1.6542439999999998,1.68983,1.6532240000000002,1.6402899999999998,1.623836,1.6439039999999998,1.6262260000000002,1.6570799999999999,1.6404900000000002,1.6180879999999997,1.695138,1.626624,1.6095479999999998,1.681608,1.6110900000000001,1.681646,1.656164
-DNM1,4.172434666666667,4.123441333333333,4.080342333333333,3.974428333333333,3.9980523333333338,3.916848,3.8313673333333336,3.8744300000000003,4.191863666666666,4.1875746666666664,4.047683666666666,3.9064216666666667,4.080940666666667,4.250824333333333,4.095099666666666,4.4581436666666665,3.9859706666666663,3.700438666666667,3.7704766666666663,4.5872,4.1309819999999995,4.372436666666667,4.131192666666667,4.217841666666667,4.162935,4.057150666666667,4.167028,4.341360666666667,3.9490510000000003,4.105449
+DNM1,5.861057166666667,5.903271333333333,5.855562333333333,5.7141658333333325,5.717154833333334,5.5957555,5.496977333333334,5.61415,5.873973666666666,5.917597166666667,5.771636166666666,5.622061666666667,5.797648166666667,5.9161918333333325,5.851554666666667,6.256483666666667,5.635920666666666,5.411623666666666,5.613731666666666,6.3452649999999995,5.9832695,6.087736666666667,5.913207666666667,6.112576666666667,5.8813525,5.773553166666667,5.883448,6.0216631666666665,5.666596,5.774119
 DNM1L,0.6679215384615385,0.7541076923076923,0.6320538461538461,0.7465723076923076,0.6787500000000001,0.6979330769230769,0.6637523076923078,0.7001953846153846,0.6794507692307692,0.7263130769230769,0.6718969230769231,0.70969,0.6338576923076923,0.7211469230769231,0.6804146153846153,0.6912492307692308,0.6760346153846153,0.6889069230769231,0.6829430769230769,0.7020846153846154,0.61044,0.6867346153846154,0.6704492307692308,0.766573076923077,0.6579569230769231,0.7489584615384615,0.678703076923077,0.7329353846153845,0.6581715384615384,0.6759338461538462
 DNM1P41,3.389666666666667,3.146183333333333,3.3215833333333333,3.177633333333333,3.2541733333333336,3.12899,3.0933433333333333,3.0225500000000003,3.4400666666666666,3.426266666666667,3.2731866666666662,3.1017266666666665,3.278586666666667,3.3456333333333332,3.3095966666666663,3.523066666666667,3.2143466666666662,2.8676066666666666,3.0103266666666664,3.5757,3.3629,3.424466666666667,3.3683666666666667,3.422166666666667,3.3722,3.1752266666666666,3.3897999999999997,3.3790666666666667,3.2044200000000003,3.3294099999999998
 DNM1P46,4.21738,4.116615,3.732755,4.09715,4.17362,4.08201,4.27354,4.308365,3.78931,4.063585,4.338365,4.419035,4.205545,4.143045,4.195095,3.997925,4.11724,4.15216,4.168615,4.02523,4.03649,4.02994,4.13443,4.0906,3.70615,3.767535,4.055475,3.841615,4.39043,4.103045
@@ -4762,7 +4763,8 @@ ERP44,5.71175,5.53765,5.55505,5.4819,5.53865,5.75065,5.37,5.5197,5.7226,5.49335,
 ERRFI1,3.21997,3.9414,3.230613333333333,3.6514666666666664,3.2191466666666666,3.4639333333333333,3.308483333333333,3.1311,3.2834800000000004,3.5465999999999998,3.151813333333333,3.28258,3.4747,3.3795333333333333,2.9230033333333334,3.541533333333333,3.309673333333333,3.4534000000000002,3.0025633333333333,3.1454166666666663,2.95658,3.294373333333333,3.0256900000000004,3.751633333333333,3.2678766666666665,3.5280666666666662,2.9771033333333334,3.3281166666666664,2.941876666666667,3.4164333333333334
 ERV3-1,1.834745,1.189485,2.2313625,1.592175,2.3740575,2.0343825,2.1710075,1.8701775,2.30142,1.98083,2.02568,1.959055,2.2669475,1.7238175,1.5167,1.590945,2.3758,1.94907,1.47412,1.8637675,1.79506,1.621145,1.93144,1.2623,2.0122775,1.743645,2.0350425,2.0951525,1.72543,2.0296825
 ERVFRD-1,2.24756,2.2367666666666666,2.2195833333333335,2.2368799999999998,2.26351,2.1113066666666667,2.2502299999999997,2.210923333333333,2.20668,2.2583333333333333,2.2772966666666665,2.2175266666666666,2.2409399999999997,2.1907466666666666,2.313353333333333,2.1815166666666665,2.192126666666667,2.1716866666666665,2.2811566666666665,2.13813,2.326566666666667,2.3955766666666665,2.2228233333333334,2.2726866666666665,2.25315,2.1400166666666665,2.2119866666666668,2.18044,2.414286666666667,2.2832233333333334
-ERVK-19,29.77545878205128,28.570624564102566,29.989854794871793,29.439200871794874,29.61401817948718,29.187183038461537,29.91226982051282,29.414046833333334,29.890135333333333,29.093314743589744,29.59013033333333,28.908401871794872,29.724688756410256,28.844655410256408,29.862971038461538,28.895862525641025,29.643025512820515,29.001926666666666,29.54569473076923,28.57976958974359,29.939995269230767,28.826176115384616,29.82346235897436,29.153023948717948,29.615591782051283,28.60794423076923,28.938867000000002,28.42647314102564,29.902158641025643,29.490483141025642
+ERVK-19,15.16721348193473,14.545330463869465,15.268846033799532,14.99033270862471,15.09722818065268,14.85666561013986,15.2603640011655,14.978768416666666,15.202911757575757,14.8142000990676,15.08641062121212,14.729088663170163,15.138608469114219,14.691103614219113,15.207032337412587,14.71784808100233,15.096986847319348,14.758084696969696,15.028029183566433,14.556600703962705,15.247940816433566,14.672685330419581,15.196711634032635,14.853242883449884,15.086429072843822,14.58916166083916,14.7271035,14.492618388694638,15.238710229603731,15.010850206876457
+ERVK3-2,0.5589681818181819,0.5200363636363636,0.5478372727272727,0.5414645454545455,0.5804381818181819,0.5261481818181818,0.6084581818181818,0.54349,0.5156881818181819,0.5350854545454545,0.5826909090909091,0.5497754545454545,0.5525281818181819,0.5375518181818182,0.5510936363636364,0.5398336363636363,0.5509481818181818,0.5142427272727272,0.5103636363636364,0.5334318181818182,0.5558863636363637,0.5191945454545455,0.5699609090909091,0.5534618181818182,0.5572663636363636,0.5703790909090909,0.51534,0.5587636363636364,0.5752618181818182,0.5312172727272727
 ERVV-1,1.4874975,1.38932,1.504555,1.40679,1.4435375,1.339385,1.4647875,1.41628,1.4109075,1.438055,1.4653025,1.5287675,1.49097,1.4754775,1.54617,1.426835,1.4562425,1.4993325,1.478935,1.4669175,1.532845,1.3857775,1.44605,1.4765725,1.490165,1.5586825,1.4300125,1.4774375,1.58887,1.45721
 ERVV-2,1.4874975,1.38932,1.504555,1.40679,1.4435375,1.339385,1.4647875,1.41628,1.4109075,1.438055,1.4653025,1.5287675,1.49097,1.4754775,1.54617,1.426835,1.4562425,1.4993325,1.478935,1.4669175,1.532845,1.3857775,1.44605,1.4765725,1.490165,1.5586825,1.4300125,1.4774375,1.58887,1.45721
 ERVW-1,2.227463333333333,2.300336666666667,2.4706333333333332,2.4082433333333335,2.23655,2.3384733333333334,2.5807466666666667,2.5172866666666667,2.1962766666666664,2.3356566666666665,2.1763,2.258836666666667,2.29454,2.1774033333333334,2.3862900000000002,2.2960133333333332,2.236083333333333,2.2945766666666665,2.4003033333333335,2.1838966666666666,2.4738233333333333,2.31894,2.449456666666667,2.56629,2.34659,2.0910233333333332,2.36776,2.01064,2.158376666666667,2.2983033333333336
@@ -6461,7 +6463,7 @@ HAPLN1,1.5484766666666667,2.0770766666666667,1.6101999999999999,1.67391666666666
 HAPLN2,3.984675,3.902995,3.95642,3.969685,4.06917,3.835045,3.93578,4.121455,3.847995,3.897565,3.96294,3.913235,3.97794,3.842625,4.013205,3.871235,3.909585,3.86149,3.94661,3.901685,3.8821,3.89445,4.042825,3.98535,4.05901,3.646235,3.83155,3.735965,3.988395,3.882
 HAPLN3,3.967675,4.607485,4.01918,4.21342,3.729535,4.58968,3.91905,4.409685,4.315145,4.611685,3.824325,4.34593,4.003005,4.32873,4.162395,4.439265,4.07326,4.57758,3.990445,4.291825,3.922845,4.34859,3.9206,4.48322,3.8291,4.486125,3.972535,4.39096,3.92483,4.346045
 HAPLN4,3.63379,3.567715,3.66801,3.681345,3.516105,3.564175,3.72192,3.82176,3.509235,3.555525,3.71153,3.827665,3.66598,3.74619,3.81266,3.52459,3.61496,3.72077,3.64732,3.60376,3.840865,3.48749,3.65513,3.791755,3.61243,3.541205,3.654035,3.5229,3.833305,3.740495
-HAPSTR1,5.31835,5.1454,5.33895,5.18065,5.4319,5.21295,5.2237,5.05665,5.2889,5.06755,5.32425,5.1458,5.3081,5.04065,5.24475,5.45235,5.39315,5.0271,5.16335,5.2759,5.19225,5.1845,5.3576,5.31425,5.203,5.44025,5.19695,4.89534,5.33115,5.2751
+HAPSTR1,3.703878333333333,3.6241950000000003,3.7939799999999995,3.6317,3.773105,3.574021666666667,3.6352616666666666,3.6136383333333333,3.715186666666667,3.5526383333333333,3.7311283333333334,3.617375,3.6971799999999995,3.4964233333333334,3.6144966666666667,3.7897616666666667,3.8280683333333334,3.510063333333333,3.542381666666667,3.6030366666666667,3.6787633333333334,3.6063366666666665,3.6825166666666664,3.674458333333334,3.6370233333333335,3.69413,3.6325900000000004,3.4622416666666664,3.73191,3.62437
 HARBI1,3.016955,2.85915,2.94949,2.958665,2.98286,2.89801,2.737645,2.906695,3.140055,3.011655,2.88258,3.05399,3.02705,3.10198,2.88814,3.611945,3.071305,2.922255,2.72926,2.951775,2.823615,3.085875,2.943755,2.98849,2.87564,2.935325,2.997765,2.95209,2.91595,3.04033
 HARS1,6.67939,6.137496666666666,6.675038333333333,6.716796666666667,6.6871833333333335,6.549863333333333,6.659208333333333,6.521541666666667,6.779381666666667,6.7517933333333335,6.599295000000001,6.56529,7.015408333333333,6.843859999999999,6.479655,6.492715,6.7732616666666665,6.621121666666666,6.517195,6.421331666666666,6.470631666666667,6.556625,6.448195,6.601371666666667,6.686495000000001,6.585628333333333,6.540291666666667,6.740715000000001,6.314563333333334,6.627495
 HARS2,0.9687255555555555,0.922478888888889,0.9265988888888889,0.9317722222222221,0.9426788888888888,0.931861111111111,0.8952933333333333,0.9156355555555555,0.9880222222222224,0.9667944444444445,0.9201377777777778,0.9221833333333334,0.9766455555555554,0.9635666666666667,0.91658,0.9944999999999999,0.9787522222222224,0.9261111111111112,0.9434333333333333,0.9497444444444445,0.9183455555555555,0.975098888888889,0.9139166666666667,0.971588888888889,0.933008888888889,0.9581066666666668,0.9730455555555556,0.9984422222222222,0.8996,0.9325333333333332
@@ -7098,7 +7100,7 @@ IGKV2D-23,9.109843964285714,9.210389625,9.087270196428571,8.365920642857143,9.24
 IGKV2D-24,14.722038273809524,15.315149196428571,14.613988958333334,15.163242738095239,13.744427142857143,15.486634464285714,14.002069851190477,15.201741220238095,15.151141398809523,15.442024434523809,13.993539047619048,14.777754940476191,14.419504732142858,15.755719226190477,14.357854970238096,15.404221875000001,14.952970059523809,15.498324821428572,14.38007017857143,15.91608255952381,13.915143392857143,15.98186357142857,14.268703065476192,13.36704,13.646610922619047,15.71681380952381,13.896736904761905,15.071466845238096,14.090083482142857,15.089193779761905
 IGKV2D-26,18.01595277930403,18.904609228479853,18.052527928113552,17.857075476190477,18.22978262820513,18.99035798992674,17.990785391483517,18.436011879578754,18.685276206501833,17.53089480540293,17.9893139514652,18.418988424908424,18.124419608516483,18.609788887362637,18.184111852106227,19.12265982371795,18.359868598901098,18.291439935897436,18.589773965201466,18.522849372710624,18.09483026098901,19.179408456959706,18.23308294642857,18.56471333791209,17.972331169871794,18.85368065018315,18.9073623489011,19.120090064102563,18.278584553571427,18.677880107600732
 IGKV2D-28,56.423606657243084,57.01123372244259,56.5921096285316,56.61477414593413,55.81336293772894,57.88267270228138,57.067527104273175,57.669530038743936,56.964288053151094,56.363744591806885,56.03402073119528,56.42538545533878,57.507733422570894,56.78877527618786,56.66039116713679,57.79297207803634,57.335481568216096,57.76283981179931,55.86875727215758,55.634223060704,56.4446580613243,57.61757529820832,56.457490609222475,56.062184280064706,55.864117279805164,58.13738415247498,56.70039487313504,56.36126718741553,56.46791367729819,57.041856485168424
-IGKV2D-30,116.50930268262049,118.13621947395497,116.1020132964609,115.88461415670115,114.99839893635531,117.55047064827983,114.21793705532212,115.44669136308626,117.71961424830317,118.00060133774151,115.54797644335811,115.33690163743447,115.24503031813366,118.4005884442559,115.01270112932737,118.90844701698629,117.49012047307512,119.98989331822344,115.81800060365941,117.51327990183509,114.10565298911872,119.02337602664656,115.26204628515765,115.22105012370717,114.7874747649034,118.25548399839295,118.28556210885765,120.35905465381025,115.53495548600337,117.66399088580945
+IGKV2D-30,117.66381268262049,119.33714947395497,117.21099996312756,117.12753415670115,116.20673893635531,118.62258731494649,115.45514038865547,116.66595469641959,118.71352091496983,119.03719800440817,116.99243977669144,116.40947163743446,116.53062031813366,119.44368177758925,116.19804112932736,120.01796368365295,118.52832047307513,121.14043331822344,116.91275727032608,118.61948323516843,115.30688298911872,120.38627269331322,116.40933628515765,116.50271345704051,116.0187747649034,119.37755066505962,119.46921877552431,121.6570413204769,116.71449215267005,118.86742088580945
 IGKV2D-38,1.3340249999999998,1.3701283333333334,1.3747966666666667,1.3875466666666665,1.4525249999999998,1.3374366666666668,1.4176283333333333,1.4103416666666666,1.3510866666666665,1.36588,1.4105433333333333,1.3895233333333332,1.3721683333333334,1.3574233333333332,1.3640316666666665,1.3732616666666668,1.4086716666666668,1.40113,1.3922566666666667,1.3458433333333335,1.40831,1.3866100000000001,1.3713616666666668,1.373195,1.3830433333333334,1.4012916666666666,1.3499333333333334,1.32533,1.4064699999999999,1.3569866666666668
 IGKV2D-40,3.3846666666666665,3.472633333333333,3.365766666666667,3.3877333333333333,3.3425,3.328583333333333,3.4003333333333337,3.4069333333333334,3.320653333333333,3.3521,3.3856,3.3836666666666666,3.3291,3.3539999999999996,3.4418666666666664,3.3982666666666668,3.2950233333333334,3.4111,3.429866666666667,3.4073666666666664,3.4923,3.4012333333333333,3.391966666666667,3.4448333333333334,3.385066666666667,3.3283233333333335,3.3679666666666663,3.3711333333333333,3.401566666666667,3.4156666666666666
 IGKV3-31,3.338325,3.085225,3.395575,3.347675,3.39685,3.184375,3.374525,3.281325,3.36625,3.169,3.370725,3.2428,3.446775,3.164525,3.33835,3.0891,3.41565,3.2103,3.3358,2.8796,3.369625,3.17185,3.3312,3.05995,3.373875,3.234675,3.3312,3.029275,3.3809,3.387525
@@ -7231,7 +7233,7 @@ IL36B,3.348505,2.67184,3.53748,3.381775,3.58226,2.576175,3.533965,3.17809,3.9854
 IL36G,5.1504,6.0705,4.108475,6.49085,3.90576,5.158,4.014105,5.20165,4.63144,4.62208,3.67051,4.49816,5.0613,5.95995,3.645265,6.11115,3.961415,5.395,4.22429,4.754825,3.175765,4.64737,3.755655,3.524995,3.85884,6.13185,3.318245,5.0641,3.97124,5.61955
 IL36RN,5.22165,3.88119,5.07445,5.3154,4.978885,4.16197,4.718075,4.778065,5.0368,3.749765,4.35618,4.041775,4.94825,3.778015,4.456115,5.1267,5.2698,4.931035,4.58029,3.89843,4.74447,3.63124,4.396555,3.55998,4.98824,5.075,3.924585,3.254545,4.23486,4.900905
 IL37,2.867265,2.92217,2.818085,2.883955,2.849185,2.82075,2.851885,2.83184,2.74706,2.86168,2.857865,3.004525,2.79051,2.821445,3.083915,2.80623,2.7031,2.93893,2.89583,2.80542,2.940765,2.770855,2.971135,2.93769,3.06386,2.856535,2.79099,2.76016,2.85844,3.194215
-IL4,2.86335,2.777375,2.77486,2.73145,2.857695,2.761565,2.86721,2.7689,2.6382,2.88241,2.69148,2.744825,2.690135,2.71184,2.85891,2.694395,2.88331,2.82586,2.84564,2.643825,2.88504,2.76286,2.73672,2.66846,2.79612,2.775485,2.833575,2.76883,2.89041,2.71439
+IL4,1.9154866666666668,2.0055075,2.0228333333333333,1.9524,1.9222958333333333,1.9348958333333335,1.922425,2.0422883333333335,2.08885,2.027356666666667,1.9235116666666667,1.9930308333333335,2.0128441666666665,1.90242,1.9350266666666665,1.9170725000000002,2.098071666666667,1.9456600000000002,2.012295,1.9603058333333334,1.99082,1.9060783333333333,1.83054,1.8963116666666666,1.9706200000000003,2.3339058333333336,1.8458275000000002,2.0449766666666664,2.043985,1.89465
 IL4I1,1.2112414285714286,1.2768057142857143,1.21153,1.276882857142857,1.164794285714286,1.3199514285714287,1.1567642857142857,1.2412114285714286,1.2236157142857143,1.3337357142857142,1.1935185714285714,1.267737142857143,1.1821985714285714,1.2790942857142855,1.1672228571428571,1.2913142857142856,1.2230785714285715,1.2866771428571429,1.2060057142857143,1.3140657142857144,1.1986085714285715,1.2729300000000001,1.2154214285714284,1.2953314285714286,1.2049414285714286,1.2987471428571429,1.22623,1.3165342857142857,1.167007142857143,1.2313100000000001
 IL4R,5.1853,4.783665,5.02745,5.65665,4.8593,5.3474,4.79963,5.15155,5.323,4.96761,4.922295,5.2822,5.16285,5.084,4.917595,5.32945,5.32445,5.18445,5.053,4.594265,4.793165,5.3861,5.01045,5.25135,4.991415,5.7015,4.95942,4.72733,4.819825,5.1973
 IL5,1.949425,1.988185,1.920625,2.069825,2.01393,1.94271,2.154285,1.945005,2.08751,1.9607,2.15071,1.92599,2.020225,1.929365,2.326275,1.90434,1.882785,1.996155,2.06041,2.070255,1.9398,1.921815,2.045285,1.963645,2.04781,1.75254,2.07655,1.924845,2.01824,1.99865
@@ -8109,7 +8111,7 @@ LAMP5,3.070405,3.456465,2.89437,2.988865,2.905325,3.136235,3.099655,3.101655,3.1
 LAMTOR1,5.09645,5.7105,5.55605,5.6366,5.313,4.99163,5.42355,5.60145,5.4766,5.38505,5.285,5.0528,5.3961,5.405,5.2429,5.10365,5.55495,5.2384,5.1122,5.1518,5.1449,6.00045,5.15195,5.27815,5.1321,5.41115,5.21155,4.90922,5.10815,5.19735
 LAMTOR2,4.639435,4.92323,4.667985,4.74046,4.62721,4.73338,4.62199,4.88179,4.768325,4.642805,4.71434,4.69951,4.650865,4.596755,4.767785,4.91994,4.703075,4.642785,4.801,4.79632,4.669765,4.964805,4.65787,4.688785,4.555775,4.56245,4.86076,4.861365,4.69185,4.70409
 LAMTOR3,3.471766666666667,3.2214233333333335,3.4834333333333336,3.456466666666667,3.4931666666666668,3.3953,3.4675999999999996,3.3094433333333337,3.6234333333333333,3.4748,3.5751000000000004,3.4105333333333334,3.6435333333333335,3.32324,3.5565333333333338,3.502166666666667,3.579833333333333,3.252963333333333,3.5927666666666664,3.4799333333333333,3.5210333333333335,3.3260133333333335,3.427766666666667,3.319036666666667,3.5292333333333334,3.4039333333333333,3.6424333333333334,3.3588,3.5089666666666663,3.5847333333333338
-LAMTOR5,4.029165,3.945395,4.217785,4.21158,4.197135,4.196465,4.238615,4.139985,4.375755,4.450455,4.14357,4.14821,4.27163,4.290545,3.86033,3.95549,4.248645,4.12057,3.755905,3.90191,4.017775,4.259185,3.9008,4.06092,3.920065,3.884635,4.17194,4.003675,3.9474,4.208515
+LAMTOR5,7.451205,7.5100549999999995,8.01025,7.907375,8.059075,7.747665,7.938715,7.48837,8.20568,8.185545,7.92005,7.723955,7.93139,7.983219999999999,7.12895,7.486835,8.03498,7.84089,7.58567,7.70808,7.63804,8.0005,7.4349799999999995,6.958805,7.3144,7.1405650000000005,7.62853,7.657705,7.62622,7.683135
 LANCL1,3.3115133333333335,3.3841,3.27136,3.079003333333333,3.30369,3.1078033333333335,3.1923133333333333,3.0545233333333335,3.4483,3.3273966666666666,3.186246666666667,3.1006666666666667,3.1623933333333336,3.4150666666666667,3.281776666666667,2.9423366666666664,3.3646,3.383766666666667,3.3906333333333336,3.5236,3.264746666666667,3.22319,3.274763333333333,3.198423333333333,3.2062866666666667,3.0686633333333333,3.5723000000000003,3.4995333333333334,3.2505333333333333,3.340066666666667
 LANCL2,2.29679,2.7659,2.2864625,2.2163425,2.285395,2.3476475,2.253825,2.272695,2.4033,2.59745,2.2740725,2.21402,2.1752325,2.47103,2.252115,2.3925075,2.4000425,2.31769,2.3317575,2.906075,2.2152375,2.2635675,2.228515,2.31922,2.20778,2.2355225,2.4245575,2.22695,2.24899,2.37043
 LANCL3,2.4270033333333334,2.1132433333333336,2.44548,2.1275133333333334,2.3693766666666667,2.0833833333333334,2.3348733333333334,2.30077,2.45052,2.471006666666667,2.27753,2.1658166666666667,2.4725966666666666,2.114196666666667,2.26476,2.1973433333333334,2.4605966666666665,2.4160666666666666,2.390006666666667,1.9730033333333334,2.4316866666666668,2.18313,2.3650100000000003,1.98543,2.2893,1.9517166666666668,2.412436666666667,2.272276666666667,2.3383133333333332,2.3653233333333334
@@ -9702,7 +9704,7 @@ MTREX,4.358685,4.41093,4.373165,4.411255,4.33363,4.18411,4.21,4.4094,4.456845,4.
 MTRF1,3.2557,3.706535,3.513975,3.234175,3.238895,3.518825,3.304645,3.439015,3.578935,3.288375,3.35669,3.266805,3.11451,3.37193,3.065695,4.065825,3.488985,3.34429,3.316325,3.5402,2.93725,3.65723,3.34466,3.603915,3.2465,2.627635,3.65513,3.430245,3.12594,3.277085
 MTRF1L,2.28187,2.688325,2.335495,2.853945,2.710215,3.031195,2.975095,2.902585,2.848085,2.825045,2.75306,2.33674,3.046855,2.81124,2.881705,3.417,2.54335,3.08959,2.2354,2.47136,2.69147,2.58094,3.098435,2.690355,2.09346,2.845495,2.629455,2.285555,3.16837,2.555645
 MTRFR,2.95488,3.047005,3.204825,3.05527,2.92902,3.229695,3.05907,3.14685,3.214025,3.369475,3.09699,3.1388,2.820195,3.25952,3.13079,3.114435,3.19608,3.067365,2.920055,3.305105,2.97541,3.29587,3.03742,3.231155,3.027545,3.26233,3.27136,3.512735,2.93486,3.1519
-MTRNR2L1,5.27105,5.07905,5.52375,5.4277,5.27975,5.4263,5.3686,5.3676,5.4697,5.38995,5.26625,5.32,5.5067,5.0712,5.2825,5.3421,5.4141,5.2586,5.26815,5.2233,5.34085,5.85785,5.4637,5.53515,5.2238,5.43385,5.2999,5.5535,5.33385,5.3799
+MTRNR2L1,6.153662499999999,5.96301875,6.40795,6.2994125,6.17168125,6.3165625,6.257043749999999,6.242475000000001,6.352531249999999,6.2654499999999995,6.15181875,6.2145,6.386262500000001,5.9508937500000005,6.15841875,6.22151875,6.30014375,6.134356250000001,6.14375625,6.1114375,6.213025,6.73950625,6.3446,6.398524999999999,6.10029375,6.309774999999999,6.188075,6.449681249999999,6.1958,6.25484375
 MTRR,4.81617,5.21735,4.64337,4.746625,4.5668,5.26085,4.47405,4.79783,4.824375,4.990715,4.444455,4.53585,4.62706,5.1682,4.42552,5.33385,4.764345,4.98803,4.439415,5.22085,4.35671,5.50485,4.28188,5.15775,4.57144,5.3393,4.76132,5.3239,4.180925,4.873335
 MTSS1,5.36515,5.3617,5.20035,5.66275,5.042,5.13705,4.952565,4.880005,5.2237,4.55868,4.97439,4.95872,5.07205,4.93417,4.93856,5.70535,5.25805,5.23925,5.2046,4.58625,4.854275,5.2451,5.31305,4.64638,5.14145,5.3503,5.1898,4.61646,5.0979,5.1653
 MTSS2,4.94426,4.854695,5.19905,4.99131,4.99295,5.02135,5.0226,4.71386,4.922545,4.957325,4.93171,5.0372,5.0326,5.023,5.0591,5.1395,5.00555,4.946715,5.0975,5.0519,5.1327,5.2819,5.03565,5.3197,5.0839,5.06025,5.2725,4.893615,5.12125,5.00875
@@ -12322,6 +12324,7 @@ PRMT9,4.10997,3.72164,3.909945,3.65143,3.925335,3.98714,3.69155,3.448535,4.26978
 PRND,2.833075,2.806425,2.987085,2.94591,2.785455,2.926555,2.9559,2.86961,2.839135,2.892775,3.0509,2.807595,2.694785,2.80559,2.369865,2.786345,2.845885,2.86661,2.92976,2.809505,3.00231,2.9962,2.845865,2.8636,2.73659,2.646745,2.82908,2.84196,3.03864,2.8574
 PRNP,5.70115,6.28965,5.44875,6.2552,5.30385,6.27095,5.3554,5.898,5.708,6.05265,5.4432,5.9071,5.6681,6.1265,5.3928,6.51095,5.60415,5.87885,5.67195,6.245,5.4517,5.89815,5.6491,6.057,5.564,6.5905,5.5929,6.2388,5.2497,5.9863
 PRNT,2.748241666666667,2.6606475,2.65001,2.721645,2.6289783333333334,2.75652,2.5486208333333336,2.486190833333333,2.664225,2.637364166666667,2.7004908333333333,2.6442533333333333,2.6385183333333333,2.6325700000000003,2.6164658333333333,2.7248550000000002,2.7273083333333332,2.6555341666666665,2.5965941666666668,2.7724458333333333,2.5306141666666666,2.80441,2.6504691666666664,2.6839158333333337,2.6083516666666666,2.9271416666666665,2.6769225,2.8106999999999998,2.6330358333333335,2.734776666666667
+PRO2268,0.9578225,0.9405925,0.9798425,1.003645,1.0209525,1.0332825,0.943695,1.0793925,1.0324875,1.02562,1.00124,0.981675,0.9854375,0.98007,1.0706525,1.0469725,1.05949,1.0211825,0.960305,1.01928,1.0320925,1.0761725,1.01361,0.9905425,1.1247175,1.1083425,0.9916275,0.97462,1.091275,1.0517475
 PROC,3.87129,3.99069,3.83711,4.032095,3.82191,4.025855,3.79461,3.980375,3.777005,3.70971,3.877065,3.831435,3.817035,3.91623,4.04786,4.06978,3.65936,3.687085,3.927815,4.03748,3.92853,3.995075,3.844315,4.096365,3.944305,3.93808,3.853125,3.927735,3.84014,3.8697
 PROCA1,2.7414366666666665,2.55062,2.7596166666666666,2.6500833333333333,2.7005466666666664,2.6311466666666665,2.7049466666666664,2.7412266666666665,2.6441133333333333,2.6675866666666668,2.674046666666667,2.7241199999999997,2.6880966666666666,2.6087700000000003,2.75141,2.5991666666666666,2.65028,2.764863333333333,2.6629,2.55886,2.660346666666667,2.5843766666666665,2.689853333333333,2.6486566666666667,2.69562,2.5969366666666667,2.6147933333333335,2.5334266666666667,2.678433333333333,2.66929
 PROCR,3.731865,4.639915,3.649725,4.021675,3.721635,4.44978,3.738925,4.023985,3.75335,4.02499,3.690335,3.98801,3.783225,3.80314,3.77596,4.481515,3.7671,4.81553,3.77158,3.884265,3.87238,4.05682,3.76569,4.52104,3.793275,3.95547,3.83402,4.09108,3.83182,3.96756
@@ -12484,7 +12487,7 @@ PSMD13,4.677635,4.675165,4.59675,4.689005,4.49449,4.72128,4.445675,4.503955,4.74
 PSMD14,2.6025,2.53265,2.45484,2.60415,2.4292025,2.518275,2.41406,2.42522,2.603925,2.611125,2.4755375,2.527975,2.565275,2.648375,2.47614,2.90105,2.55535,2.578925,2.563,2.700675,2.4539075,2.72785,2.4451075,2.7182,2.546375,2.712575,2.58335,2.83685,2.417565,2.69585
 PSMD2,5.41505,5.49355,5.2464,5.81375,5.29135,5.6356,5.27995,5.41345,5.40115,5.24695,5.3999,5.60255,5.6351,6.01055,5.259,5.85325,5.4292,5.45805,5.3315,5.64455,5.16245,5.8526,5.201,5.82025,5.2386,5.7743,5.2829,6.09965,5.12535,5.42395
 PSMD3,5.3699,5.61325,5.3778,5.5129,5.25115,5.83385,5.23595,5.48615,5.41595,5.58735,5.28175,5.5291,5.43355,6.00345,5.19755,5.6062,5.4686,5.4797,5.30165,5.5451,5.25065,5.96645,5.3925,5.59595,5.2618,5.46245,5.35005,5.8307,5.1553,5.51415
-PSMD4,4.381766666666667,4.26361,4.437966666666666,4.633956666666666,4.580751666666666,4.450168333333333,4.5281899999999995,4.723531666666666,4.377868333333334,4.537165,4.42061,4.574381666666667,4.451286666666666,4.435741666666667,4.713045,4.401231666666667,4.458218333333334,4.43265,4.499886666666667,4.374043333333334,4.496685,4.538458333333333,4.600548333333333,4.776206666666667,4.393115,4.591613333333333,4.455655,4.453645,4.619186666666667,4.4718583333333335
+PSMD4,6.28121,6.366743333333334,6.553036666666666,6.75568,6.420025,6.7029716666666666,6.536666666666667,6.915031666666667,6.330735000000001,6.573131666666667,6.39204,6.627808333333333,6.50977,6.667398333333333,6.586991666666666,6.450265,6.652868333333333,6.592463333333333,6.3960566666666665,6.363203333333333,6.209585000000001,6.621831666666667,6.428731666666667,6.924953333333333,6.442828333333333,6.487559999999999,6.198075,6.591754999999999,6.44092,6.466271666666667
 PSMD5,5.1785,5.09185,5.05305,4.75573,4.915675,5.14965,4.76725,4.99373,5.24875,4.783075,4.984985,4.99692,4.946465,4.859675,4.789185,5.23965,5.196,5.24805,4.873955,5.206,4.726545,5.00325,4.988145,5.2107,4.98832,5.31375,5.13195,5.0186,4.88314,5.0455
 PSMD6,6.159159166666667,5.518034166666666,5.871605833333334,5.670170000000001,5.951785833333334,5.624180833333334,5.918584166666667,5.85717,6.3373525,5.9850683333333325,5.950537499999999,5.558449166666667,5.8221475,5.763065,5.745119166666667,6.067354999999999,6.011796666666667,5.940345833333334,6.074183333333333,6.046335,6.1519525,5.91067,5.9027875000000005,5.7187141666666665,5.866265833333333,5.6454450000000005,6.156757499999999,6.03685,5.8560324999999995,5.862695
 PSMD7,5.6707,5.6595,5.46335,5.50465,5.50575,5.739,5.42395,5.5453,5.631,5.94275,5.63245,5.62565,5.4703,5.51095,5.6707,6.2133,5.6303,5.53595,5.75565,5.8265,5.70195,6.2368,5.7629,5.8538,5.53615,5.95655,5.7897,5.5148,5.69175,5.65005
@@ -13324,6 +13327,7 @@ RNPC3,2.2161408333333337,2.1387525000000003,2.2425499999999996,2.165739166666666
 RNPEP,5.46495,5.2897,5.7568,5.2487,5.59085,5.48115,5.5612,5.55715,5.62235,5.3868,5.48095,5.2704,5.5141,5.44465,5.37385,5.45805,5.65025,5.33265,5.3837,5.2162,5.416,5.407,5.43815,5.14445,5.3992,5.145,5.5913,5.42845,5.58855,5.55305
 RNPEPL1,5.14355,4.85278,5.27705,5.1429,5.26825,5.0267,5.265,5.04905,5.2311,4.9659,5.2011,4.957555,5.22425,4.905405,5.2141,4.83557,5.2887,5.1311,5.18625,5.27035,5.26585,5.148,5.1283,5.07955,5.2592,4.89874,5.23335,5.04245,5.31255,5.1714
 RNPS1,3.8609299999999998,3.9606624999999998,3.9908825,4.066262500000001,3.8293600000000003,4.260384999999999,3.718705,3.8919675,4.1816475,4.3862725,3.792735,3.9483675000000003,3.703175,3.999905,3.6230824999999998,4.265969999999999,4.0546875,4.0846975,3.997175,4.441905,3.3966575,4.110469999999999,3.6841725000000003,4.00983,3.6910175,4.0593,3.8792524999999998,4.08198,3.64725,3.8716150000000003
+RNR2,0.8826125,0.88396875,0.8842,0.8717125,0.89193125,0.8902625,0.88844375,0.874875,0.88283125,0.8755,0.88556875,0.8945,0.8795625,0.87969375,0.87591875,0.87941875,0.88604375,0.87575625,0.87560625,0.8881375,0.872175,0.88165625,0.8809,0.863375,0.87649375,0.875925,0.888175,0.89618125,0.86195,0.87494375
 RNU1-1,3.2092533333333333,3.294363333333333,3.2016899999999997,3.2579533333333335,3.171193333333333,3.29754,3.15817,3.2431666666666668,3.1548133333333332,3.302046666666667,3.1729000000000003,3.3505000000000003,3.1603733333333337,3.31652,3.1988233333333334,3.3920666666666666,3.1856033333333333,3.238023333333333,3.259593333333333,3.4431,3.19313,3.306236666666667,3.174656666666667,3.3517333333333332,3.2276433333333334,3.2128366666666666,3.31053,3.3645333333333336,3.1511266666666664,3.2661033333333336
 RNU11,5.678605833333334,5.709906666666667,5.5954775,5.59758,5.406257500000001,5.63757,5.386194166666667,5.704666666666666,5.7729025,6.011525000000001,5.663470833333333,5.781365,5.545636666666667,5.724796666666666,5.2993175,5.900318333333333,5.614860833333333,5.4559125,5.453860833333334,6.092619999999999,5.326714166666666,5.890065,5.520930833333334,5.7041458333333335,5.171871666666666,5.713425833333334,5.765091666666667,5.877646666666667,5.538296666666667,5.8901625
 RNU12-2P,5.678605833333334,5.709906666666667,5.5954775,5.59758,5.406257500000001,5.63757,5.386194166666667,5.704666666666666,5.7729025,6.011525000000001,5.663470833333333,5.781365,5.545636666666667,5.724796666666666,5.2993175,5.900318333333333,5.614860833333333,5.4559125,5.453860833333334,6.092619999999999,5.326714166666666,5.890065,5.520930833333334,5.7041458333333335,5.171871666666666,5.713425833333334,5.765091666666667,5.877646666666667,5.538296666666667,5.8901625
@@ -13462,7 +13466,7 @@ RPS18,1.952102,1.957958,1.964806,1.996686,1.96889,1.9271280000000002,1.909298000
 RPS18P9,1.952102,1.957958,1.964806,1.996686,1.96889,1.9271280000000002,1.9092980000000002,1.973638,1.985592,1.917192,1.956314,1.9146640000000001,1.938476,1.9460000000000002,1.9656600000000002,1.9142219999999999,1.98366,1.9760300000000002,1.973742,1.9566880000000002,1.9514479999999998,1.9545480000000002,1.9061800000000002,1.999428,1.9467780000000001,1.9175280000000001,2.0442799999999997,1.9619039999999999,1.943336,1.9469699999999999
 RPS19,7.6636050000000004,7.121848333333333,7.45002,7.455326666666667,7.527995,6.886003333333333,7.23215,7.584043333333333,7.40456,7.428331666666667,7.33474,7.327398333333333,6.796925,7.31665,7.575388333333334,7.48085,7.298246666666667,7.064361666666667,7.362451666666667,7.010556666666666,7.78312,7.467141666666667,7.042423333333334,7.3226466666666665,7.114958333333334,6.933776666666667,6.993195,7.1071599999999995,6.932548333333333,7.513303333333333
 RPS19BP1,3.1480633333333334,3.2070333333333334,3.099906666666667,3.266306666666667,3.0851433333333333,3.13116,3.1171966666666666,3.184893333333333,3.152003333333333,3.18037,3.172093333333333,3.130186666666667,3.13487,3.322896666666667,3.1962333333333333,3.2940466666666666,3.182096666666667,3.0885599999999998,3.21259,3.1380966666666663,3.157383333333333,3.4116999999999997,3.2759466666666666,3.14795,3.0599333333333334,3.0741300000000003,3.159243333333333,3.12918,3.0861133333333335,3.2543466666666667
-RPS2,5.20079,5.199498333333334,5.219925833333333,5.51417,4.910652499999999,5.202842499999999,4.880106666666666,5.129601666666667,5.288308333333333,5.457140833333333,5.4209233333333335,5.503940833333333,5.235551666666667,5.42136,5.196298333333333,5.196889166666667,5.3413675,5.3401974999999995,5.214483333333334,5.292241666666667,5.194811666666666,5.3381083333333335,5.276043333333334,5.672923333333333,5.3500525,5.62621,5.369488333333333,5.088369166666666,5.053340833333333,5.266961666666667
+RPS2,5.551650833333333,5.557618333333334,5.5643775,5.8704350000000005,5.2663891666666665,5.538391666666667,5.244485,5.473650833333333,5.630546666666667,5.8230025,5.762930000000001,5.86832,5.580004166666667,5.7764375,5.539001666666667,5.5637475,5.673395833333333,5.669225,5.556971666666667,5.667051666666667,5.557654166666667,5.6822325,5.637264166666666,6.040538333333333,5.729008333333334,5.983394166666667,5.713539999999999,5.415473333333333,5.401838333333333,5.6202775
 RPS20,3.4009850000000004,3.5628516666666665,3.428773333333333,3.5341966666666664,3.3115500000000004,3.544391666666667,3.4127433333333332,3.3836549999999996,3.429108333333333,3.496478333333333,3.4559450000000003,3.47807,3.34598,3.5534383333333337,3.4600633333333333,3.4904116666666667,3.406185,3.4157800000000003,3.4644049999999997,3.5622233333333333,3.357945,3.4937683333333336,3.42367,3.5794266666666665,3.4046133333333333,3.3455883333333336,3.4706550000000003,3.321405,3.3047933333333335,3.377796666666667
 RPS20P27,1.7389299999999999,1.8287833333333332,1.7727466666666667,1.8689133333333334,1.70454,1.8065033333333333,1.7496866666666666,1.69715,1.7841766666666665,1.9173166666666666,1.8069300000000001,1.7617,1.75636,1.8801166666666667,1.8001666666666667,1.8151433333333333,1.78585,1.83716,1.88849,1.9381266666666666,1.78745,1.8449766666666667,1.70854,1.9184133333333333,1.8190266666666668,1.8082166666666666,1.88863,1.7908099999999998,1.7069466666666668,1.7467933333333334
 RPS21,2.7497390476190477,2.8643076190476187,2.805715238095238,2.7427238095238096,2.6993252380952377,2.678315238095238,2.6666535714285713,2.7358657142857146,2.783404285714286,2.8426414285714285,2.6871823809523807,2.7554847619047624,2.7498142857142858,2.874892380952381,2.59254,2.834674761904762,2.8054066666666664,2.761729523809524,2.8632995238095242,2.8638209523809524,2.581925,2.7521257142857145,2.55206,2.7827776190476188,2.6178933333333334,2.921690952380952,2.7152904761904764,2.8030914285714283,2.595255238095238,2.674609523809524
@@ -16615,7 +16619,8 @@ TREML5P,0.617092,0.650766,0.633196,0.6207779999999999,0.619531,0.531487,0.645695
 TRERF1,8.155345,7.645054999999999,7.710139999999999,7.535119999999999,8.42146,8.34437,8.165715,7.896625,8.558495,8.582995,8.299745000000001,7.865460000000001,7.66084,7.041,8.360330000000001,7.9605250000000005,8.524415000000001,7.872345000000001,8.85366,8.556035,8.177655,8.623194999999999,8.253615,8.759905,8.497135,7.775895,9.031965,8.807485,8.39767,7.6373299999999995
 TREX1,1.564654,1.600524,1.589142,1.612724,1.596918,1.592274,1.585382,1.597574,1.617298,1.597642,1.543366,1.4993159999999999,1.56549,1.588288,1.612066,1.647898,1.59299,1.601228,1.60883,1.6101400000000001,1.574706,1.60655,1.5916679999999999,1.6163440000000002,1.586208,1.583832,1.6185199999999997,1.6006360000000002,1.599256,1.5841939999999999
 TREX2,0.7308981818181818,0.7445209090909092,0.7402527272727273,0.7929245454545455,0.70843,0.7964936363636365,0.7268609090909091,0.7509690909090909,0.7436309090909091,0.7582336363636363,0.7305472727272728,0.7487881818181819,0.7332418181818181,0.7475272727272727,0.725239090909091,0.78067,0.7474445454545454,0.7711372727272727,0.7294927272727272,0.7367818181818181,0.7169972727272728,0.7420763636363635,0.7286845454545454,0.7978181818181818,0.7363672727272728,0.7564927272727272,0.7261772727272727,0.7295336363636363,0.7341627272727272,0.7257981818181819
-TRGC1,36.001412106060606,36.09426269264069,35.333344045454545,36.02081857142857,35.119083714285715,36.90848450649351,34.81548331818182,35.862167943722945,36.58132206709956,37.2929936038961,34.66253919264069,35.96194977922078,34.758525023809526,35.72994374675324,34.83395291991342,37.598956502164505,35.67056092640693,37.763205251082255,34.994695316017314,36.437298982683984,34.57424210822511,37.59046835714285,35.23902065584416,37.04599165800866,34.78034597186147,36.75341585930736,35.65127388961039,37.494807162337665,34.43665157142857,36.03999542207792
+TRG,2.614895,3.19042,2.68513,2.62316,2.50776,2.9909,2.60276,2.6319,2.746065,2.868645,2.62211,3.077545,2.427945,2.591535,2.520115,3.08587,2.171235,2.82236,2.880905,3.358445,2.36361,2.8538,2.223225,2.639265,2.62613,2.911425,2.50687,3.204925,2.105645,2.853525
+TRGC1,39.93169210606061,40.07110269264069,39.17631904545455,40.149258571428575,39.067863714285714,40.94984950649351,38.72941831818182,39.92454294372294,40.441822067099565,41.0679386038961,38.49300919264069,39.82630977922078,38.530145023809524,39.77799874675325,38.76785791991342,41.560991502164505,39.462190926406926,41.744935251082254,38.96554531601732,40.403968982683985,38.728407108225106,41.68626835714286,39.01083065584415,40.999371658008656,38.71685097186147,40.62643585930736,39.26385388961039,41.50589216233766,38.30387157142857,40.19302542207792
 TRGC2,1.902695111111111,1.7552217777777779,1.942818888888889,1.7632564444444445,1.9178051111111112,1.7416153333333333,1.812846,1.9418562222222224,1.8862306666666666,1.7682408888888888,1.8869551111111111,1.819424,1.802813111111111,1.8095577777777776,1.9154504444444447,1.781872888888889,1.871788,1.8392591111111112,1.783244888888889,1.758105111111111,1.8690204444444443,1.8372928888888889,1.8236451111111112,1.8163355555555554,1.8572884444444444,2.036791555555556,1.93415,1.7417951111111112,1.8882526666666668,1.8162806666666667
 TRGV11,0.6978711111111111,0.6540277777777779,0.6695888888888889,0.6653044444444444,0.6869211111111111,0.6349233333333334,0.64247,0.7014922222222223,0.6555566666666667,0.6403288888888888,0.6908911111111111,0.65253,0.6509411111111111,0.6751777777777778,0.6922544444444445,0.6413588888888889,0.65986,0.6623511111111111,0.6571988888888889,0.5998411111111112,0.6647744444444444,0.6707988888888888,0.6618111111111111,0.6705055555555556,0.6719644444444444,0.7353155555555556,0.66828,0.6028911111111112,0.6744666666666667,0.6458766666666667
 TRGV3,3.14869,2.8815925,2.946225,2.7639924999999996,2.96983,3.041735,2.9285975,3.099065,3.200115,3.2084775,3.1533925,2.87775,2.6973525,2.6892475,2.9450475,3.003815,3.041725,3.0449675000000003,2.9394575,3.1838825,2.8718125,2.9619275,2.97898,2.6886225,3.0569775,2.90825,3.0624425,2.72688,3.0280175,3.0275499999999997
@@ -17633,7 +17638,7 @@ YBX3,4.558333333333334,4.4910000000000005,4.650066666666667,4.732366666666667,4.
 YBX3P1,4.558333333333334,4.4910000000000005,4.650066666666667,4.732366666666667,4.5402,4.468966666666667,4.578366666666667,4.563733333333333,4.621066666666667,4.7215,4.606466666666667,4.5244333333333335,4.609566666666667,4.673,4.5748,4.469433333333334,4.648433333333333,4.573933333333334,4.5517666666666665,4.492366666666666,4.471633333333333,4.498833333333333,4.5774333333333335,4.707933333333333,4.4831666666666665,4.570566666666667,4.5214,4.308466666666667,4.6108,4.533166666666667
 YEATS2,3.086016666666667,3.5121333333333333,3.082076666666667,3.5239333333333334,2.9792666666666663,3.3706,3.0034733333333334,3.0218133333333337,3.1379900000000003,3.1764666666666668,3.0112400000000004,3.3369666666666666,3.1932266666666664,3.8335000000000004,2.9514899999999997,3.5213,3.1585300000000003,3.4347666666666665,3.028723333333333,3.560033333333333,2.9543633333333332,3.389466666666667,3.023903333333333,3.490133333333333,3.1231166666666668,3.5056,3.11895,3.690633333333333,2.8622533333333333,3.2187133333333335
 YEATS4,2.810636666666667,3.1374999999999997,2.82465,2.7925133333333334,2.7023266666666665,3.0602966666666664,2.53832,2.7831333333333332,3.0812633333333337,3.183733333333333,2.743126666666667,2.9571066666666668,2.53236,2.989036666666667,2.85551,3.082686666666667,2.8530033333333336,2.88205,2.8681300000000003,3.2291233333333333,2.71381,3.245433333333333,2.7841466666666665,3.14061,2.73175,2.9105600000000003,2.9547933333333334,3.24842,2.753213333333333,2.966396666666667
-YES1,3.024589166666667,2.997335,2.9684133333333333,3.0153458333333334,2.880844166666667,3.1370649999999998,2.8558616666666667,2.9562150000000003,3.0882166666666664,3.2844900000000004,2.9487491666666665,3.0334616666666667,2.9690683333333334,3.1243408333333336,2.7664524999999998,3.0823058333333333,3.050289166666667,3.1122425,2.865915833333333,3.145211666666667,2.8877858333333335,3.0373783333333333,2.947095,3.1187275,2.9086233333333333,3.1489374999999997,3.0357525,3.3574733333333335,2.83178,3.116083333333333
+YES1,3.3493431666666664,3.310661,3.3050213333333334,3.3494018333333333,3.214196166666667,3.4611029999999996,3.195133666666667,3.289517,3.4737166666666663,3.6099930000000002,3.2388461666666664,3.3602796666666666,3.3257513333333333,3.4455918333333337,3.1067845,3.4088928333333333,3.394214166666667,3.3812794999999998,3.1668998333333334,3.435532666666667,3.1747268333333336,3.3493353333333333,3.208249,3.4329565,3.4094773333333332,3.5298165,3.3599645000000002,3.7233473333333333,3.1559600000000003,3.432812333333333
 YIF1A,4.91462,5.07335,4.905475,5.6345,4.70928,4.924405,4.828665,5.1725,4.964025,5.21535,4.72085,5.125,4.82797,4.98524,4.86551,5.1702,4.92208,4.90122,4.939525,4.87221,4.847695,5.28955,4.94891,5.30585,4.97468,5.35095,5.04455,4.91716,4.84349,5.01675
 YIF1B,9.7531,9.85324,9.783395,10.1130475,9.545815,9.46011,9.7991025,9.915992500000002,9.715285000000002,9.04843,9.732420000000001,9.7422675,9.8725875,9.3496225,9.795277500000001,9.4989225,9.79166,9.8309675,9.52915,9.498625,9.7150675,9.691815,9.839329999999999,9.5650725,9.5406975,9.7698325,9.677335,9.331822500000001,9.7026775,9.7032025
 YIPF1,4.240655,4.04969,4.2606,4.246555,4.356325,4.35324,4.132145,4.175075,4.555835,4.36505,4.22527,4.0106,4.3851,4.119965,4.25452,4.60271,4.49865,4.312545,4.219365,4.317215,4.170155,4.535835,4.22816,4.309205,4.13395,4.050955,4.438495,4.333365,4.37496,4.57249

output/preprocess/Essential_Thrombocythemia/clinical_data/GSE103237.csv

@@ -1,3 +1,3 @@
 ,GSM2758679,GSM2758680,GSM2758681,GSM2758682,GSM2758683,GSM2758684,GSM2758685,GSM2758686,GSM2758687,GSM2758688,GSM2758689,GSM2758690,GSM2758691,GSM2758692,GSM2758693,GSM2758694,GSM2758695,GSM2758696,GSM2758697,GSM2758698,GSM2758699,GSM2758700,GSM2758701,GSM2758702,GSM2758703,GSM2758704,GSM2758705,GSM2758706,GSM2758707,GSM2758708,GSM2758709,GSM2758710,GSM2758711,GSM2758712,GSM2758713,GSM2758714,GSM2758715,GSM2758716,GSM2758717,GSM2758718,GSM2758719,GSM2758720,GSM2758721,GSM2758722,GSM2758723,GSM2758724,GSM2758725,GSM2758726,GSM2758727,GSM2758728,GSM2758729,GSM2758730,GSM2758731,GSM2758732,GSM2758733,GSM2758734,GSM2758735,GSM2758736,GSM2758737,GSM2758738,GSM2758739,GSM2758740,GSM2758741,GSM2758742,GSM2758743
-Essential_Thrombocythemia,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Essential_Thrombocythemia,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
 Gender,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,,,,,,,,,,,,,,,

output/preprocess/Essential_Thrombocythemia/clinical_data/GSE159514.csv

@@ -1,4 +1,2 @@
-""
-Essential_Thrombocythemia
-Age
-Gender
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+Essential_Thrombocythemia,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Essential_Thrombocythemia/clinical_data/GSE61629.csv

@@ -1,2 +1,2 @@
 ,GSM1388566,GSM1388567,GSM1388568,GSM1388569,GSM1388570,GSM1388571,GSM1388577,GSM1388579,GSM1388582,GSM1388584,GSM1388585,GSM1388587,GSM1388590,GSM1388591,GSM1388592,GSM1388593,GSM1388594,GSM1388595,GSM1388596,GSM1388598,GSM1388599,GSM1388600,GSM1388601,GSM1388603,GSM1388604,GSM1388605,GSM1388606,GSM1388607,GSM1388608,GSM1388614,GSM1388616,GSM1388623,GSM1388624,GSM1509517,GSM1509518,GSM1509519,GSM1509520,GSM1509521,GSM1509522,GSM1509523,GSM1509524,GSM1509525,GSM1509526,GSM1509527,GSM1509528,GSM1509529,GSM1509530,GSM1509531,GSM1509532,GSM1509533,GSM1509534,GSM1509535,GSM1509536,GSM1509537
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+Essential_Thrombocythemia,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Essential_Thrombocythemia/code/GSE103176.py

@@ -0,0 +1,364 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Essential_Thrombocythemia"
+cohort = "GSE103176"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
+in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE103176"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/GSE103176.csv"
+out_gene_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/gene_data/GSE103176.csv"
+out_clinical_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/clinical_data/GSE103176.csv"
+json_path = "./output/z3/preprocess/Essential_Thrombocythemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Title indicates both gene and miRNA expression; gene expression likely available.
+
+# 2) Variable availability
+trait_row = 3    # disease: PV / ET / healthy control
+age_row = None   # No age field found in provided characteristics
+gender_row = 1   # Sex: M / F / not provided
+
+# 2) Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    # Positive (Essential Thrombocythemia)
+    if v_low in {"et", "essential thrombocythemia"}:
+        return 1
+    # Negative: other diseases or controls
+    if v_low in {"pv", "polycythemia vera", "healthy control", "control", "ctr", "normal", "healthy"}:
+        return 0
+    # Heuristic: if the word 'thrombocythemia' appears, consider ET
+    if "thrombocythemia" in v_low:
+        return 1
+    # Otherwise treat as non-ET if explicitly non-ET MPN (e.g., pv)
+    if "polycythemia" in v_low or "vera" in v_low:
+        return 0
+    return None
+
+def convert_age(x):
+    # Not used (age_row is None), but provided for completeness
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if any(tok in v_low for tok in ["not provided", "na", "n/a", "unknown"]):
+        return None
+    # extract first number (years)
+    m = re.search(r"(\d+(\.\d+)?)", v_low)
+    if m:
+        try:
+            val = float(m.group(1))
+            return val
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    if v_low in {"m", "male"}:
+        return 1
+    if v_low in {"f", "female"}:
+        return 0
+    if "not provided" in v_low or v_low in {"na", "n/a", "unknown"}:
+        return None
+    # Heuristic: start with 'm' or 'f'
+    if v_low.startswith("m"):
+        return 1
+    if v_low.startswith("f"):
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (if available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Based on the observed identifiers like '14qI-1_st' and suffixes '_st'/'_x_st',
+# these are probe/set identifiers (e.g., Affymetrix-style), not human gene symbols.
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import os
+import pandas as pd
+
+# Helper to evaluate candidate ID and symbol columns for a given annotation dataframe
+def choose_id_and_symbol_cols(annotation_df: pd.DataFrame, expr_index: pd.Index):
+    expr_ids = pd.Index(expr_index.astype(str))
+    obj_cols = [c for c in annotation_df.columns if annotation_df[c].dtype == 'object']
+
+    # Candidate gene symbol columns in order of preference
+    symbol_candidates = [
+        'Gene Symbol', 'Gene symbol', 'GeneSymbol', 'Gene Symbols', 'Symbol', 'SYMBOL',
+        'HGNC symbol', 'Approved Symbol', 'gene_assignment'
+    ]
+    gene_symbol_col = None
+    for c in symbol_candidates:
+        if c in annotation_df.columns:
+            gene_symbol_col = c
+            break
+    if gene_symbol_col is None and obj_cols:
+        gene_symbol_col = obj_cols[0]
+
+    # Identify best ID column by overlap with expression IDs, with tie-break on ST-like suffix rate
+    best = {'col': None, 'overlap': -1, 'st_rate': -1.0}
+    for col in obj_cols:
+        col_vals = annotation_df[col].astype(str).str.strip()
+        overlap = col_vals.isin(expr_ids).sum()
+        st_rate = (col_vals.str.endswith('_st') | col_vals.str.endswith('_x_st') | col_vals.str.endswith('_s_st')).mean()
+        # Prefer higher overlap; if tie, prefer higher st_rate
+        if (overlap > best['overlap']) or (overlap == best['overlap'] and st_rate > best['st_rate']):
+            best = {'col': col, 'overlap': overlap, 'st_rate': float(st_rate)}
+
+    return best['col'], gene_symbol_col, best['overlap'], best['st_rate']
+
+
+# Iterate over all SOFT files in the cohort directory and pick the best-matching annotation
+soft_files = [os.path.join(in_cohort_dir, f) for f in os.listdir(in_cohort_dir) if 'soft' in f.lower()]
+
+best_choice = {
+    'file': None, 'id_col': None, 'sym_col': None,
+    'matched_ann': 0, 'total_ann': 0, 'matched_expr': 0, 'total_expr': len(gene_data.index), 'st_rate': -1.0,
+    'mapping_df': None
+}
+
+for sf in soft_files:
+    try:
+        ann_df = get_gene_annotation(sf)
+        if ann_df is None or len(ann_df) == 0:
+            continue
+
+        id_col, sym_col, raw_overlap, st_rate = choose_id_and_symbol_cols(ann_df, gene_data.index)
+        if id_col is None or sym_col is None:
+            continue
+
+        # Build mapping and compute precise overlaps
+        candidate_mapping = get_gene_mapping(ann_df, prob_col=id_col, gene_col=sym_col)
+        if candidate_mapping is None or len(candidate_mapping) == 0:
+            continue
+
+        # Normalize ID column to string/stripped
+        candidate_mapping['ID'] = candidate_mapping['ID'].astype(str).str.strip()
+        expr_ids = pd.Index(gene_data.index.astype(str))
+        matched_ann = candidate_mapping['ID'].isin(expr_ids).sum()
+        total_ann = len(candidate_mapping)
+        matched_expr = expr_ids.isin(set(candidate_mapping['ID'])).sum()
+        total_expr = len(expr_ids)
+
+        # Diagnostics for this SOFT file
+        ann_rate = matched_ann / total_ann if total_ann > 0 else 0.0
+        expr_rate = matched_expr / total_expr if total_expr > 0 else 0.0
+        print(f"[Annotation scan] File: {os.path.basename(sf)}")
+        print(f"  Chosen ID column: {id_col} | Gene Symbol column: {sym_col}")
+        print(f"  ST-like ID rate in chosen column: {st_rate:.2%}")
+        print(f"  Matched annotation IDs: {matched_ann} / {total_ann} ({ann_rate:.2%})")
+        print(f"  Matched expression IDs: {matched_expr} / {total_expr} ({expr_rate:.2%})")
+
+        # Update best choice: prioritize matched_ann, then matched_expr, then st_rate
+        better = False
+        if matched_ann > best_choice['matched_ann']:
+            better = True
+        elif matched_ann == best_choice['matched_ann']:
+            if matched_expr > best_choice['matched_expr']:
+                better = True
+            elif matched_expr == best_choice['matched_expr'] and st_rate > best_choice['st_rate']:
+                better = True
+
+        if better:
+            best_choice.update({
+                'file': sf, 'id_col': id_col, 'sym_col': sym_col,
+                'matched_ann': matched_ann, 'total_ann': total_ann,
+                'matched_expr': matched_expr, 'total_expr': total_expr,
+                'st_rate': st_rate, 'mapping_df': candidate_mapping
+            })
+
+    except Exception as e:
+        print(f"[Annotation scan] Skipped {os.path.basename(sf)} due to error: {e}")
+        continue
+
+# Apply the best mapping if overlap is non-zero; else warn and keep original gene_data unchanged
+if best_choice['mapping_df'] is not None and best_choice['matched_ann'] > 0:
+    print("\n[Mapping selection]")
+    print(f"Selected annotation file: {os.path.basename(best_choice['file'])}")
+    ann_rate = best_choice['matched_ann'] / best_choice['total_ann'] if best_choice['total_ann'] > 0 else 0.0
+    expr_rate = best_choice['matched_expr'] / best_choice['total_expr'] if best_choice['total_expr'] > 0 else 0.0
+    print(f"  Using ID column: {best_choice['id_col']} | Gene Symbol column: {best_choice['sym_col']}")
+    print(f"  Matched annotation IDs: {best_choice['matched_ann']} / {best_choice['total_ann']} ({ann_rate:.2%})")
+    print(f"  Matched expression IDs: {best_choice['matched_expr']} / {best_choice['total_expr']} ({expr_rate:.2%})")
+
+    # Restrict mapping to IDs present in expression to speed up apply
+    mapping_df = best_choice['mapping_df']
+    mapping_df = mapping_df[mapping_df['ID'].isin(gene_data.index.astype(str))].copy()
+
+    # Apply mapping to convert probe-level expression to gene-level expression
+    mapped_gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+    # Guard against accidental empty result
+    if mapped_gene_data is not None and mapped_gene_data.shape[0] > 0 and mapped_gene_data.shape[1] > 0:
+        gene_data = mapped_gene_data
+        print(f"[Mapping] Gene-level data shape: {gene_data.shape[0]} genes x {gene_data.shape[1]} samples")
+    else:
+        print("[Warning] Mapping produced empty gene-level data. Keeping original probe-level data.")
+else:
+    print("\n[Warning] No usable annotation mapping found (zero overlap). Keeping original probe-level data without mapping.")
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols
+# Guard: gene_data should exist from previous steps
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Ensure output directory exists and save normalized gene data (may be empty if mapping failed)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Ensure clinical features are available (load from CSV if variable not in scope)
+try:
+    selected_clinical_df
+except NameError:
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2. Link clinical and genetic data
+is_gene_available = normalized_gene_data.shape[0] > 0
+is_trait_available = trait in selected_clinical_df.index
+
+if is_gene_available:
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+else:
+    # Fallback linking with clinical-only data to allow downstream processing/validation
+    linked_data = selected_clinical_df.T.copy()
+
+# 3. Handle missing values
+covariate_cols = [trait, 'Age', 'Gender']
+gene_cols_present = [c for c in linked_data.columns if c not in covariate_cols]
+
+if len(gene_cols_present) > 0:
+    processed_linked = handle_missing_values(linked_data, trait_col=trait)
+else:
+    # No gene features present: perform covariate-only missing handling
+    df = linked_data.copy()
+    # Drop samples with missing trait
+    df = df.dropna(subset=[trait])
+    # Impute Age with mean if present
+    if 'Age' in df.columns:
+        df['Age'] = pd.to_numeric(df['Age'], errors='coerce')
+        df['Age'] = df['Age'].fillna(df['Age'].mean())
+    # Impute Gender with mode if present
+    if 'Gender' in df.columns:
+        mode_result = df['Gender'].mode()
+        if len(mode_result) > 0:
+            df['Gender'] = df['Gender'].fillna(mode_result[0])
+        else:
+            df = df.drop(columns=['Gender'])
+    processed_linked = df
+
+# 4. Bias check and removal of biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(processed_linked, trait)
+
+# 5. Final validation and cohort info saving
+if is_gene_available:
+    note = "INFO: Gene-level data normalization completed."
+else:
+    note = ("ERROR: Gene identifier mapping/normalization failed. Probe/set IDs (e.g., '14qI-*_st') "
+            "did not map to human gene symbols in the available SOFT annotation; normalization removed all gene rows. "
+            "Gene expression unavailable for analysis.")
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Essential_Thrombocythemia/code/GSE103237.py

@@ -0,0 +1,219 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Essential_Thrombocythemia"
+cohort = "GSE103237"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
+in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE103237"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/GSE103237.csv"
+out_gene_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/gene_data/GSE103237.csv"
+out_clinical_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/clinical_data/GSE103237.csv"
+json_path = "./output/z3/preprocess/Essential_Thrombocythemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+import re
+
+# 1) Gene Expression Data Availability
+# Series states: "This series includes only the GEP samples." -> gene expression available
+is_gene_available = True
+
+# 2) Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+# Trait (Essential Thrombocythemia) can be derived from disease field
+trait_row = 3  # 'disease: PV', 'disease: ET', 'disease: healthy control'
+
+# No explicit age field in the sample characteristics dictionary
+age_row = None
+
+# Gender field present
+gender_row = 1  # 'Sex: M', 'Sex: F', 'Sex: not provided'
+
+# 2.2 Data Type Conversion
+def _after_colon(val):
+    if val is None:
+        return None
+    if not isinstance(val, str):
+        return str(val)
+    parts = val.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else val.strip()
+
+def convert_trait(x):
+    """
+    Map ET to 1, others (PV, healthy/control) to 0. Unknown -> None.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_norm = v.strip().lower()
+    # Normalize common forms
+    if v_norm in {"et", "essential thrombocythemia", "essential thrombocythaemia"}:
+        return 1
+    if v_norm in {"pv", "polycythemia vera", "polycythaemia vera", "healthy control", "control", "ctr", "healthy"}:
+        return 0
+    # Heuristics
+    if "thrombocythem" in v_norm and "essential" in v_norm:
+        return 1
+    if "polycythem" in v_norm or "healthy" in v_norm or "control" in v_norm:
+        return 0
+    return None
+
+def convert_age(x):
+    """
+    Return age as float if available, else None.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v = v.strip()
+    # Remove common units or text
+    v = re.sub(r"[^\d\.]", " ", v)
+    v = re.sub(r"\s+", " ", v).strip()
+    try:
+        return float(v)
+    except:
+        return None
+
+def convert_gender(x):
+    """
+    Female -> 0, Male -> 1, Unknown/Not provided -> None.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_norm = v.strip().lower()
+    if v_norm in {"m", "male"}:
+        return 1
+    if v_norm in {"f", "female"}:
+        return 0
+    if "not provided" in v_norm or v_norm in {"na", "n/a", "unknown", ""}:
+        return None
+    # Heuristic
+    if v_norm.startswith("m"):
+        return 1
+    if v_norm.startswith("f"):
+        return 0
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if clinical data is available)
+if trait_row is not None:
+    # Extract clinical features
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview and save
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Map probe IDs to gene symbols using annotation columns 'ID' and 'Gene Symbol'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Optional sanity check
+print(f"Normalized gene data shape: {normalized_gene_data.shape}")
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+print(f"Linked data shape (before missing handling): {linked_data.shape}")
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+print(f"Linked data shape (after missing handling): {linked_data.shape}")
+
+# 4. Bias check and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Gene symbols normalized via NCBI synonyms; probe-to-gene mapped with equal-split; missing values handled per protocol."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Essential_Thrombocythemia/code/GSE12295.py

@@ -0,0 +1,211 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Essential_Thrombocythemia"
+cohort = "GSE12295"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
+in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE12295"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/GSE12295.csv"
+out_gene_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/gene_data/GSE12295.csv"
+out_clinical_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/clinical_data/GSE12295.csv"
+json_path = "./output/z3/preprocess/Essential_Thrombocythemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Platelet-focused spotted oligonucleotide arrays -> mRNA expression
+
+# 2) Variable availability and conversion functions
+
+# Keys in the sample characteristics dictionary
+trait_row = 0
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower().strip()
+    # Normalize
+    v_clean = re.sub(r'[^a-z\s]', ' ', v_low)
+    v_clean = re.sub(r'\s+', ' ', v_clean).strip()
+
+    # Binary: ET = 1, others (Normal/RT) = 0
+    if 'essential thromb' in v_clean:
+        return 1
+    if 'reactive thrombocytosis' in v_clean or 'reactive thrombocythaemia' in v_clean or v_clean == 'rt':
+        return 0
+    if 'normal' in v_clean or 'control' in v_clean:
+        return 0
+    return None
+
+def convert_age(x):
+    # Not available in this cohort; robust converter provided for completeness
+    v = _extract_value(x)
+    if v is None:
+        return None
+    # Extract first number that looks like age
+    m = re.search(r'(\d+(\.\d+)?)', str(v))
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+        # Filter out impossible ages
+        if 0 < age < 120:
+            return age
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    # Not available in this cohort; robust converter provided for completeness
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower().strip()
+    # Handle common encodings
+    if v_low in {'male', 'm', 'man', 'boy'}:
+        return 1
+    if v_low in {'female', 'f', 'woman', 'girl'}:
+        return 0
+    # Token-based fallback
+    if 'male' in v_low and 'female' not in v_low:
+        return 1
+    if 'female' in v_low and 'male' not in v_low:
+        return 0
+    return None
+
+# 3) Save metadata: initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction, preview, and save
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Selected clinical features preview:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide the columns: probe IDs match 'ID' in annotation; gene symbols in 'Gene Symbol'
+id_col, gene_col = 'ID', 'Gene Symbol'
+assert id_col in gene_annotation.columns and gene_col in gene_annotation.columns
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+# Ensure native Python bools to avoid JSON serialization issues
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+trait_col_exists = bool(trait in unbiased_linked_data.columns)
+trait_has_non_na = bool(unbiased_linked_data[trait].notna().any()) if trait_col_exists else False
+is_trait_available = bool(trait_col_exists and trait_has_non_na)
+is_trait_biased_bool = bool(is_trait_biased)
+
+note = "INFO: Age and Gender not available; cohort mixes ET, RT, and normal controls; trait binarized as ET vs others."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased_bool,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Essential_Thrombocythemia/code/GSE159514.py

@@ -0,0 +1,215 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Essential_Thrombocythemia"
+cohort = "GSE159514"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
+in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE159514"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/GSE159514.csv"
+out_gene_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/gene_data/GSE159514.csv"
+out_clinical_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/clinical_data/GSE159514.csv"
+json_path = "./output/z3/preprocess/Essential_Thrombocythemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression data availability (Affymetrix GEP per background)
+is_gene_available = True
+
+# 2) Variable availability and converters
+# From the sample characteristics:
+# 0: disease: Overt-PMF, Pre-PMF, PET, PPV
+# 1: driver mutation: ...
+trait_row = 0  # disease info available; we will infer ET trait from 'PET' (post-ET MF)
+age_row = None
+gender_row = None
+
+def _extract_after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    if not isinstance(x, str):
+        try:
+            x = str(x)
+        except Exception:
+            return ""
+    parts = x.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    """
+    Map disease to ET-related (1) if post-ET myelofibrosis (PET), else 0.
+    Unknown -> None.
+    """
+    val = _extract_after_colon(x).lower()
+    if not val:
+        return None
+    # direct known labels
+    if val in {"pet"}:
+        return 1
+    if val in {"ppv", "overt-pmf", "pre-pmf"}:
+        return 0
+    # heuristic fallbacks
+    if "post-et" in val or "post essential thrombocythemia" in val or "post et" in val:
+        return 1
+    if "post pv" in val or "pmf" in val:
+        return 0
+    return None
+
+def convert_age(x):
+    """
+    Convert age to continuous float if present; else None.
+    Not used here since age_row is None.
+    """
+    val = _extract_after_colon(x)
+    if not val:
+        return None
+    # remove non-digit except dot
+    m = re.findall(r"[\d.]+", val)
+    if not m:
+        return None
+    try:
+        return float(m[0])
+    except Exception:
+        return None
+
+def convert_gender(x):
+    """
+    Convert gender to binary: female->0, male->1; else None.
+    Not used here since gender_row is None.
+    """
+    val = _extract_after_colon(x).lower()
+    if not val:
+        return None
+    if val in {"f", "female", "woman", "women"}:
+        return 0
+    if val in {"m", "male", "man", "men"}:
+        return 1
+    return None
+
+# 3) Initial filtering and metadata saving
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if clinical trait data available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print({"clinical_preview": clinical_preview})
+
+    # Ensure output directory exists and save
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Affymetrix probe set IDs detected (e.g., '11715100_at'); mapping to human gene symbols is required.
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1) Identify the appropriate columns for probe IDs and gene symbols in the annotation
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# 2) Build the probe-to-gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3) Apply the mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene matrix
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+# Use in-memory clinical df if available; otherwise load from saved file
+try:
+    clinical_df_to_use = selected_clinical_df
+except NameError:
+    clinical_df_to_use = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(clinical_df_to_use, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias checks and removal of biased covariates
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+note = "INFO: Trait defined as PET (post-ET MF) vs other MF subtypes. Age and Gender not available in series matrix."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Essential_Thrombocythemia/code/GSE174060.py

@@ -0,0 +1,199 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Essential_Thrombocythemia"
+cohort = "GSE174060"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
+in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE174060"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/GSE174060.csv"
+out_gene_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/gene_data/GSE174060.csv"
+out_clinical_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/clinical_data/GSE174060.csv"
+json_path = "./output/z3/preprocess/Essential_Thrombocythemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Gene expression availability (based on series description)
+is_gene_available = True
+
+# 2) Variable availability
+trait_row = 4     # diagnosis
+age_row = 2       # age
+gender_row = 3    # Sex
+
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    parts = s.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+# 2.2 Converters
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    v_low = v.lower().strip()
+    # Positive ET definitions
+    et_pos = {"et", "essential thrombocythemia", "essential thrombocythaemia"}
+    # Explicit negatives seen in this dataset and common variants
+    negatives = {
+        "healthy control", "control",
+        "pv", "polycythemia vera",
+        "pmf", "primary myelofibrosis",
+        "ppv-mf", "pet-mf", "smf", "secondary mf",
+        "post-pv-mf", "post-et-mf"
+    }
+    if v_low in et_pos:
+        return 1
+    if v_low in negatives:
+        return 0
+    # Heuristics: avoid mapping post-ET-MF (contains et but is not ET)
+    if v_low.startswith("post-et") or "post-et" in v_low or "p et" in v_low or "pet-mf" in v_low:
+        return 0
+    # If it contains 'polycythemia' or 'myelofibrosis' it is not ET
+    if "polycythemia" in v_low or "myelofibrosis" in v_low:
+        return 0
+    # Unknown diagnosis -> None
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    v = v.strip()
+    if v.lower() in {"na", "n/a", "unknown"}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    v_low = v.lower().strip()
+    if v_low in {"f", "female"}:
+        return 0
+    if v_low in {"m", "male"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait_row available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for mapping:
+# - Probe/ID column matches the expression data index: 'ID'
+# - Gene symbol information is embedded in 'gene_assignment'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias checking and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Affymetrix transcript-cluster IDs mapped via 'gene_assignment'; trait is ET vs others; included Age and Gender."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Essential_Thrombocythemia/code/GSE55976.py

@@ -0,0 +1,194 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Essential_Thrombocythemia"
+cohort = "GSE55976"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
+in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE55976"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/GSE55976.csv"
+out_gene_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/gene_data/GSE55976.csv"
+out_clinical_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/clinical_data/GSE55976.csv"
+json_path = "./output/z3/preprocess/Essential_Thrombocythemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene Expression Data Availability
+is_gene_available = True  # cDNA microarray gene expression profiling per series summary
+
+# 2) Variable Availability and Converters
+# From the sample characteristics dictionary:
+# 0: subject condition (contains ET and non-ET labels)
+# 1: cell type (not needed for this step)
+trait_row = 0
+age_row = None
+gender_row = None
+
+def convert_trait(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    val = s.strip().lower()
+    # Map ET (both JAK2+/-) to 1, other conditions (including healthy) to 0
+    if ('essential thrombocythemia' in val) or ('essential thrombocytosis' in val) or re.search(r'\bet\b', val):
+        return 1
+    known_non_et_terms = [
+        'healthy', 'polycythemia vera', 'pv', 'primary myelofibrosis', 'pmf',
+        'chronic myelogenous leukemia', 'cml'
+    ]
+    if any(k in val for k in known_non_et_terms):
+        return 0
+    return None
+
+def convert_age(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip()
+    m = re.search(r'[-+]?\d*\.?\d+', s)
+    return float(m.group()) if m else None
+
+def convert_gender(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    val = s.strip().lower()
+    if val in ['female', 'f', 'woman', 'women', 'girl']:
+        return 0
+    if val in ['male', 'm', 'man', 'men', 'boy']:
+        return 1
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Selected clinical features preview:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# The provided identifiers are numeric strings (e.g., '6590728'), not standard human gene symbols.
+requires_gene_mapping = True
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Map probe IDs to gene symbols based on annotation preview:
+# Probe identifier column: 'ID' (matches numeric strings like '6590728')
+# Gene symbol column: 'GENE SYMBOL'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE SYMBOL')
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1) Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3) Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4) Assess bias and drop biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5) Final validation and metadata saving
+# Ensure native Python types for JSON serialization
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available = bool((trait in linked_data.columns) and (linked_data[trait].notna().sum() > 0))
+is_trait_biased = bool(is_trait_biased)
+note = str("INFO: Trait derived from 'subject condition' (ET vs non-ET). "
+           "No age/gender fields available in annotations. "
+           "Samples include mixed cell types (CD34+ and granulocytes).")
+
+# Ensure the metadata directory exists (file creation is handled inside the function)
+os.makedirs(os.path.dirname(json_path), exist_ok=True)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6) Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Essential_Thrombocythemia/code/GSE57793.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Essential_Thrombocythemia"
+cohort = "GSE57793"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
+in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE57793"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/GSE57793.csv"
+out_gene_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/gene_data/GSE57793.csv"
+out_clinical_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/clinical_data/GSE57793.csv"
+json_path = "./output/z3/preprocess/Essential_Thrombocythemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability (based on series description: microarray gene expression)
+is_gene_available = True
+
+# 2) Variable availability and conversion functions
+
+# Trait (Essential Thrombocythemia) is determined from "disease state" at row 0
+trait_row = 0
+
+# Age and Gender are not present in the sample characteristics dictionary
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    if not isinstance(x, str):
+        x = str(x)
+    parts = x.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip() if val is not None else None
+
+def convert_trait(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    # Normalize common spellings
+    v = v.replace('thrombocythaemia', 'thrombocythemia')
+    # Map ET to 1, others (PV, PMF) to 0
+    if re.search(r'\bet\b', v) or 'essential thrombocythemia' in v:
+        return 1
+    if re.search(r'\bpv\b', v) or 'polycythemia vera' in v:
+        return 0
+    if re.search(r'\bpmf\b', v) or 'primary myelofibrosis' in v or 'myelofibrosis' in v:
+        return 0
+    return None
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    # Extract first number as age
+    m = re.search(r'(\d+(\.\d+)?)', val)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in ['male', 'm', 'man']:
+        return 1
+    if v in ['female', 'f', 'woman']:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=None,
+        gender_row=gender_row,
+        convert_gender=None
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print("Clinical data preview:", clinical_preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for probe IDs and gene symbols based on the annotation preview
+probe_id_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# 2. Build the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+# Ensure clinical data is available in the current session; if not, reload it
+try:
+    selected_clinical_df
+except NameError:
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and remove biased demographic features if necessary
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note="INFO: ET vs non-ET across MPN subtypes; no age/gender available."
+)
+
+# 6. Conditionally save linked data
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Essential_Thrombocythemia/code/GSE61629.py

@@ -0,0 +1,200 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Essential_Thrombocythemia"
+cohort = "GSE61629"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
+in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE61629"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/GSE61629.csv"
+out_gene_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/gene_data/GSE61629.csv"
+out_clinical_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/clinical_data/GSE61629.csv"
+json_path = "./output/z3/preprocess/Essential_Thrombocythemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Microarray gene expression per background info
+
+# 2) Variable availability and data type conversion
+
+# From Sample Characteristics Dictionary:
+# {0: ['disease state: control', 'disease state: PV', 'disease state: ET', 'disease state: PMF'],
+#  1: ['treatment: untreated', 'tissue: blood'],
+#  2: ['tissue: Whole blood']}
+
+# Availability
+trait_row = 0
+age_row = None
+gender_row = None
+
+# Converters
+def convert_trait(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        part = x.split(":", 1)[-1].strip().lower() if ":" in x else x.strip().lower()
+        # Map ET vs control; PV/PMF treated as not our target (None)
+        if part in ["et", "essential thrombocythemia", "essential thrombocythaemia"]:
+            return 1
+        if part in ["control", "healthy", "normal"]:
+            return 0
+        if part in ["pv", "polycythemia vera", "polycythaemia vera", "pmf", "primary myelofibrosis"]:
+            return None
+        # Token-based fallback
+        tokens = re.split(r"[^\w]+", part)
+        if "et" in tokens:
+            return 1
+        if "control" in tokens or "healthy" in tokens or "normal" in tokens:
+            return 0
+    return None
+
+def convert_age(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        part = x.split(":", 1)[-1] if ":" in x else x
+        nums = re.findall(r"\d+\.?\d*", part)
+        if nums:
+            try:
+                return float(nums[0])
+            except:
+                return None
+    if isinstance(x, (int, float)):
+        return float(x)
+    return None
+
+def convert_gender(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        part = x.split(":", 1)[-1].strip().lower() if ":" in x else x.strip().lower()
+        if part in ["male", "m", "man"]:
+            return 1
+        if part in ["female", "f", "woman", "women"]:
+            return 0
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait data available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+
+    # Preview and save
+    preview = preview_df(selected_clinical_df)
+    print("Clinical features preview:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify columns for mapping: probe IDs in gene_data index match 'ID' in annotation; gene symbols are in 'Gene Symbol'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply mapping: convert probe-level data to gene-level data
+probe_level_data = gene_data
+gene_data = apply_gene_mapping(expression_df=probe_level_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Prepare flags for final validation (ensure pure Python bools for JSON serialization)
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in selected_clinical_df.index) and bool(selected_clinical_df.loc[trait].notna().any()))
+is_trait_biased_flag = bool(is_trait_biased)
+
+note = "INFO: Non-ET disease states (PV/PMF) were treated as missing trait and excluded during linking/cleanup."
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased_flag,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Essential_Thrombocythemia/code/GSE65161.py

@@ -0,0 +1,119 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Essential_Thrombocythemia"
+cohort = "GSE65161"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
+in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE65161"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/GSE65161.csv"
+out_gene_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/gene_data/GSE65161.csv"
+out_clinical_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/clinical_data/GSE65161.csv"
+json_path = "./output/z3/preprocess/Essential_Thrombocythemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Determine data availability based on provided sample characteristics and background info
+# Given cell lines (AML/CML) and treatments, there is no Essential Thrombocythemia phenotype variation in this dataset.
+
+# 1) Gene expression data availability (non-miRNA/methylation, likely expression profiling given the study focus)
+is_gene_available = True
+
+# 2) Variable availability
+trait_row = None   # No ET-related trait variation present in sample characteristics (all AML/CML cell lines)
+age_row = None     # No age information for cell lines
+gender_row = None  # No gender information for cell lines
+
+# 2.2) Conversion functions
+def _after_colon(val):
+    if val is None:
+        return None
+    if isinstance(val, (int, float)):
+        return val
+    s = str(val)
+    parts = s.split(":", 1)
+    s = parts[1] if len(parts) == 2 else parts[0]
+    return s.strip()
+
+def convert_trait(x):
+    # Binary: 1 = Essential Thrombocythemia, 0 = Non-ET; unknown -> None
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Positive mappings
+    if any(k in v_low for k in ["essential thrombocythemia", "essential-thrombocythemia", "et (essential thrombocythemia)"]):
+        return 1
+    # Negative mappings (common myeloid malignancies distinct from ET)
+    if any(k in v_low for k in ["aml", "acute myeloid", "cml", "chronic myelogenous", "ml-l af9", "mll-af9", "mll-af4"]):
+        return 0
+    # If explicitly says control/healthy and the trait is ET, treat as 0
+    if any(k in v_low for k in ["control", "healthy", "normal"]):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous age in years; extract numeric value if present, else None
+    v = _after_colon(x)
+    if v is None:
+        return None
+    import re
+    nums = re.findall(r"[0-9]+\.?[0-9]*", str(v))
+    if not nums:
+        return None
+    try:
+        return float(nums[0])
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female=0, male=1; unknown -> None
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = str(v).strip().lower()
+    if v_low in ["male", "m", "man", "boy"]:
+        return 1
+    if v_low in ["female", "f", "woman", "girl"]:
+        return 0
+    # Sometimes values like '1'/'0' are used
+    if v_low == "1":
+        return 1
+    if v_low == "0":
+        return 0
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction is skipped because trait_row is None (no clinical trait data available for ET).

output/preprocess/Essential_Thrombocythemia/code/TCGA.py

@@ -0,0 +1,68 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Essential_Thrombocythemia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Essential_Thrombocythemia/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Essential_Thrombocythemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Review subdirectories and select the best match for Essential Thrombocythemia (ET)
+provided_subdirs = [
+    'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)', 'TCGA_Uterine_Carcinosarcoma_(UCS)',
+    'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)', 'TCGA_Testicular_Cancer_(TGCT)',
+    'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)', 'TCGA_Rectal_Cancer_(READ)',
+    'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)',
+    'TCGA_Head_and_Neck_Cancer_(HNSC)', 'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)',
+    'TCGA_Endometrioid_Cancer_(UCEC)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)',
+    'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)', 'TCGA_Breast_Cancer_(BRCA)',
+    'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)', 'TCGA_Adrenocortical_Cancer_(ACC)',
+    'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+# Define trait-related keywords for ET; avoid broad terms that could incorrectly match unrelated cohorts
+keywords = ['essential thrombocythemia', 'thrombocythemia', 'myeloproliferative', 'mpn', 'polycythemia', 'myelofibrosis']
+
+selected_subdirs = [d for d in provided_subdirs if any(k in d.lower() for k in keywords)]
+selected_subdir = selected_subdirs[0] if selected_subdirs else None
+
+clinical_df = None
+genetic_df = None
+
+if selected_subdir is None:
+    # No suitable TCGA cohort for Essential Thrombocythemia; record skip and finish
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print("No suitable TCGA cohort found for Essential Thrombocythemia. Skipping.")
+else:
+    # Step 2: Identify clinical and genetic file paths
+    cohort_dir = os.path.join(tcga_root_dir, selected_subdir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as DataFrames
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)
+
+    # Step 4: Print clinical columns
+    print(clinical_df.columns.tolist())

output/preprocess/Essential_Thrombocythemia/cohort_info.json

@@ -1,102 +1 @@
-{
-  "GSE65161": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 24
-  },
-  "GSE61629": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 54
-  },
-  "GSE57793": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 66
-  },
-  "GSE55976": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 39
-  },
-  "GSE174060": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 36
-  },
-  "GSE159514": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE12295": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 95
-  },
-  "GSE103237": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": true,
-    "sample_size": 39
-  },
-  "GSE103176": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "TCGA": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 79
-  }
-}
+{"GSE65161": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE61629": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 29, "note": "INFO: Non-ET disease states (PV/PMF) were treated as missing trait and excluded during linking/cleanup."}, "GSE57793": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 66, "note": "INFO: ET vs non-ET across MPN subtypes; no age/gender available."}, "GSE55976": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 39, "note": "INFO: Trait derived from 'subject condition' (ET vs non-ET). No age/gender fields available in annotations. Samples include mixed cell types (CD34+ and granulocytes)."}, "GSE174060": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 36, "note": "INFO: Affymetrix transcript-cluster IDs mapped via 'gene_assignment'; trait is ET vs others; included Age and Gender."}, "GSE159514": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 114, "note": "INFO: Trait defined as PET (post-ET MF) vs other MF subtypes. Age and Gender not available in series matrix."}, "GSE12295": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 95, "note": "INFO: Age and Gender not available; cohort mixes ET, RT, and normal controls; trait binarized as ET vs others."}, "GSE103237": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 65, "note": "INFO: Gene symbols normalized via NCBI synonyms; probe-to-gene mapped with equal-split; missing values handled per protocol."}, "GSE103176": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "ERROR: Gene identifier mapping/normalization failed. Probe/set IDs (e.g., '14qI-*_st') did not map to human gene symbols in the available SOFT annotation; normalization removed all gene rows. Gene expression unavailable for analysis."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Gastroesophageal_reflux_disease_(GERD)/clinical_data/GSE43580.csv

@@ -1,4 +1,4 @@
-GSM1065725,GSM1065726,GSM1065727,GSM1065728,GSM1065729,GSM1065730,GSM1065731,GSM1065732,GSM1065733,GSM1065734,GSM1065735,GSM1065736,GSM1065737,GSM1065738,GSM1065739,GSM1065740,GSM1065741,GSM1065742,GSM1065743,GSM1065744,GSM1065745,GSM1065746,GSM1065747,GSM1065748,GSM1065749,GSM1065750,GSM1065751,GSM1065752,GSM1065753,GSM1065754,GSM1065755,GSM1065756,GSM1065757,GSM1065758,GSM1065759,GSM1065760,GSM1065761,GSM1065762,GSM1065763,GSM1065764,GSM1065765,GSM1065766,GSM1065767,GSM1065768,GSM1065769,GSM1065770,GSM1065771,GSM1065772,GSM1065773,GSM1065774,GSM1065775,GSM1065776,GSM1065777,GSM1065778,GSM1065779,GSM1065780,GSM1065781,GSM1065782,GSM1065783,GSM1065784,GSM1065785,GSM1065786,GSM1065787,GSM1065788,GSM1065789,GSM1065790,GSM1065791,GSM1065792,GSM1065793,GSM1065794,GSM1065795,GSM1065796,GSM1065797,GSM1065798,GSM1065799,GSM1065800,GSM1065801,GSM1065802,GSM1065803,GSM1065804,GSM1065805,GSM1065806,GSM1065807,GSM1065808,GSM1065809,GSM1065810,GSM1065811,GSM1065812,GSM1065813,GSM1065814,GSM1065815,GSM1065816,GSM1065817,GSM1065818,GSM1065819,GSM1065820,GSM1065821,GSM1065822,GSM1065823,GSM1065824,GSM1065825,GSM1065826,GSM1065827,GSM1065828,GSM1065829,GSM1065830,GSM1065831,GSM1065832,GSM1065833,GSM1065834,GSM1065835,GSM1065836,GSM1065837,GSM1065838,GSM1065839,GSM1065840,GSM1065841,GSM1065842,GSM1065843,GSM1065844,GSM1065845,GSM1065846,GSM1065847,GSM1065848,GSM1065849,GSM1065850,GSM1065851,GSM1065852,GSM1065853,GSM1065854,GSM1065855,GSM1065856,GSM1065857,GSM1065858,GSM1065859,GSM1065860,GSM1065861,GSM1065862,GSM1065863,GSM1065864,GSM1065865,GSM1065866,GSM1065867,GSM1065868,GSM1065869,GSM1065870,GSM1065871,GSM1065872,GSM1065873,GSM1065874
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output/preprocess/Gastroesophageal_reflux_disease_(GERD)/clinical_data/GSE77563.csv

@@ -1,4 +1,4 @@
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output/preprocess/Gastroesophageal_reflux_disease_(GERD)/code/GSE28302.py

@@ -0,0 +1,355 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Gastroesophageal_reflux_disease_(GERD)"
+cohort = "GSE28302"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Gastroesophageal_reflux_disease_(GERD)"
+in_cohort_dir = "../DATA/GEO/Gastroesophageal_reflux_disease_(GERD)/GSE28302"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/GSE28302.csv"
+out_gene_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/gene_data/GSE28302.csv"
+out_clinical_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/clinical_data/GSE28302.csv"
+json_path = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Illumina whole-genome expression profiling on total RNA (gene expression microarray)
+
+# 2) Variable Availability and Data Type Conversion
+
+# Trait (GERD) is not explicitly available; tissue type (row 0) does not reliably indicate GERD status.
+trait_row = None
+
+def convert_trait(x):
+    # Trait not available for this cohort
+    return None
+
+# Age
+age_row = 4
+def convert_age(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip()
+    m = re.search(r'[-+]?\d+(\.\d+)?', s)
+    if not m:
+        return None
+    try:
+        val = float(m.group(0))
+        return val
+    except Exception:
+        return None
+
+# Gender
+gender_row = 3
+def convert_gender(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip().lower()
+    if s in ['female', 'f']:
+        return 0
+    if s in ['male', 'm']:
+        return 1
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Heuristic selection of identifier and gene columns with robust fallback to RefSeq accessions when symbols are unavailable.
+
+import re
+
+# Preserve original expression data
+expr_df = gene_data.copy()
+
+# 1) Decide identifier column by maximal overlap with probe IDs in expr_df
+id_overlap_counts = {}
+for col in gene_annotation.columns:
+    try:
+        overlap = gene_annotation[col].astype(str).isin(expr_df.index).sum()
+    except Exception:
+        overlap = 0
+    id_overlap_counts[col] = overlap
+
+id_col = max(id_overlap_counts, key=id_overlap_counts.get)
+
+# 2) Decide gene symbol column by maximizing the proportion of cells from which human gene symbols can be extracted
+candidate_gene_cols = [c for c in gene_annotation.columns if c != id_col]
+
+def column_symbol_coverage(series, sample_n=2000):
+    s = series.astype(str)
+    if len(s) > sample_n:
+        s = s.sample(sample_n, random_state=1)
+    parsed = s.map(lambda x: len(extract_human_gene_symbols(x)) > 0)
+    return float(parsed.mean())
+
+coverage_scores = {}
+for col in candidate_gene_cols:
+    try:
+        coverage_scores[col] = column_symbol_coverage(gene_annotation[col])
+    except Exception:
+        coverage_scores[col] = 0.0
+
+preferred_order = sorted(coverage_scores.items(), key=lambda x: x[1], reverse=True)
+gene_col = preferred_order[0][0] if preferred_order else None
+
+# If symbol coverage is very low, prefer explicit gene symbol-like column names; else we'll likely fallback later
+if gene_col is None or coverage_scores.get(gene_col, 0.0) < 0.05:
+    for cand in ['Gene Symbol', 'Symbol', 'SYMBOL', 'GeneSymbol', 'GENE_SYMBOL', 'Gene Symbols', 'GENE_SYMBOLS', 'Gene']:
+        if cand in candidate_gene_cols:
+            gene_col = cand
+            break
+# If still not found, default to GB_ACC (RefSeq accessions) as a fallback gene identifier
+if gene_col is None and 'GB_ACC' in candidate_gene_cols:
+    gene_col = 'GB_ACC'
+elif gene_col is None and candidate_gene_cols:
+    gene_col = candidate_gene_cols[0]
+elif gene_col is None:
+    gene_col = id_col  # extreme fallback
+
+print(f"Selected id_col: {id_col}")
+print(f"Selected gene_col (initial): {gene_col}")
+
+# 3) Build mapping
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)
+print(f"Initial mapping_df rows: {len(mapping_df)}")
+
+# 4) Try mapping to HGNC symbols first
+gene_data_symbol = None
+try:
+    gene_data_symbol = apply_gene_mapping(expression_df=expr_df, mapping_df=mapping_df)
+except Exception as e:
+    print(f"WARNING: Symbol mapping failed with error: {e}")
+    gene_data_symbol = None
+
+# 5) If symbol mapping is empty or failed, fallback to RefSeq accession mapping (using GB_ACC)
+def build_refseq_expression_from_gbacc(annotation_df, prob_col, gbacc_col, expression_df):
+    # Parse GB_ACC into list of base RefSeq accessions (version-stripped), allow multiple per probe
+    def parse_refseqs(val: str):
+        if val is None:
+            return []
+        s = str(val)
+        # Normalize common delimiters
+        s = s.replace('///', ';').replace(',', ';')
+        parts = re.split(r'[;\s]+', s)
+        parts = [p for p in parts if p]  # remove empty
+        # Keep plausible RefSeq-like tokens and strip version
+        keep = []
+        for p in parts:
+            # Accept common RefSeq prefixes
+            if re.match(r'^(?:N[MRP]|X[MR])_\d+(\.\d+)?$', p):
+                p_base = p.split('.', 1)[0]
+                keep.append(p_base)
+        # Deduplicate
+        return list(dict.fromkeys(keep))
+
+    ref_map = annotation_df.loc[:, [prob_col, gbacc_col]].dropna()
+    if ref_map.empty:
+        return pd.DataFrame()
+
+    ref_map = ref_map.rename(columns={prob_col: 'ID', gbacc_col: 'RefSeq'})
+    ref_map['ID'] = ref_map['ID'].astype(str).str.strip()
+    ref_map = ref_map[ref_map['ID'].isin(expression_df.index)]
+    if ref_map.empty:
+        return pd.DataFrame()
+
+    ref_map['RefSeq'] = ref_map['RefSeq'].map(parse_refseqs)
+    ref_map['num_genes'] = ref_map['RefSeq'].map(lambda x: len(x) if isinstance(x, list) else 0)
+    ref_map = ref_map[ref_map['num_genes'] > 0]
+    if ref_map.empty:
+        return pd.DataFrame()
+
+    ref_map = ref_map.explode('RefSeq').dropna(subset=['RefSeq'])
+    ref_map.set_index('ID', inplace=True)
+
+    merged = ref_map.join(expression_df, how='inner')
+    if merged.empty:
+        return pd.DataFrame()
+
+    expr_cols = [c for c in merged.columns if c not in ['RefSeq', 'num_genes']]
+    merged[expr_cols] = merged[expr_cols].div(merged['num_genes'].replace(0, 1), axis=0)
+    refseq_expr = merged.groupby('RefSeq')[expr_cols].sum()
+    return refseq_expr
+
+use_symbol_mapping = False
+if gene_data_symbol is not None and len(gene_data_symbol) > 0:
+    gene_data = gene_data_symbol
+    use_symbol_mapping = True
+else:
+    # Fallback to GB_ACC-based features
+    if 'GB_ACC' in gene_annotation.columns:
+        gene_data_refseq = build_refseq_expression_from_gbacc(
+            annotation_df=gene_annotation,
+            prob_col=id_col,
+            gbacc_col='GB_ACC',
+            expression_df=expr_df
+        )
+        if len(gene_data_refseq) > 0:
+            gene_data = gene_data_refseq
+            print("WARNING: Symbol mapping unavailable; using RefSeq (GB_ACC, version-stripped) as features.")
+        else:
+            # Last resort: keep probe-level features
+            gene_data = expr_df
+            print("WARNING: Mapping to symbols and RefSeq failed; using probe IDs as features.")
+    else:
+        gene_data = expr_df
+        print("WARNING: No GB_ACC column; using probe IDs as features.")
+
+print(f"Final gene_data shape: {gene_data.shape} | Mapped to symbols: {use_symbol_mapping}")
+
+# Step 7: Data Normalization and Linking
+# Step 7: Data Normalization and Linking with correct metadata handling for missing trait
+
+import os
+import re
+
+# 1) Normalize only if we truly have HGNC symbols; otherwise keep RefSeq/probe features
+def _is_refseq_index(idx) -> bool:
+    arr = list(idx[:1000]) if len(idx) > 1000 else list(idx)
+    if len(arr) == 0:
+        return False
+    ref_count = 0
+    for s in arr:
+        s = str(s)
+        if re.match(r'^(?:N[MRP]|X[MR])_\d+(\.\d+)?$', s):
+            ref_count += 1
+    return (ref_count / len(arr)) >= 0.5
+
+use_symbol_mapping_inferred = False
+try:
+    use_symbol_mapping_inferred = bool(use_symbol_mapping)
+except NameError:
+    use_symbol_mapping_inferred = not _is_refseq_index(gene_data.index)
+
+if use_symbol_mapping_inferred:
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+else:
+    normalized_gene_data = gene_data.copy()
+
+# Ensure output directory exists and save gene matrix
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6) Proceed only if trait is available; otherwise, record initial metadata and skip linking
+if 'trait_row' in globals() and trait_row is not None:
+    # Prepare clinical features if not already done
+    if 'selected_clinical_data' not in globals():
+        selected_clinical_data = geo_select_clinical_features(
+            clinical_df=clinical_data,
+            trait=trait,
+            trait_row=trait_row,
+            convert_trait=convert_trait,
+            age_row=age_row if 'age_row' in globals() else None,
+            convert_age=convert_age if 'convert_age' in globals() else None,
+            gender_row=gender_row if 'gender_row' in globals() else None,
+            convert_gender=convert_gender if 'convert_gender' in globals() else None
+        )
+
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # Bias assessment
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # Final validation and metadata
+    note = f"INFO: Trait available; mapped_to_symbols={use_symbol_mapping_inferred}; genes={normalized_gene_data.shape[0]}"
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note=note
+    )
+
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # Trait unavailable: record initial filtering outcome; do not trigger final validation on empty df
+    print("INFO: Trait variable unavailable; linking skipped. Saved gene matrix only.")
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Gastroesophageal_reflux_disease_(GERD)/code/GSE43580.py

@@ -0,0 +1,330 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Gastroesophageal_reflux_disease_(GERD)"
+cohort = "GSE43580"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Gastroesophageal_reflux_disease_(GERD)"
+in_cohort_dir = "../DATA/GEO/Gastroesophageal_reflux_disease_(GERD)/GSE43580"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/GSE43580.csv"
+out_gene_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/gene_data/GSE43580.csv"
+out_clinical_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/clinical_data/GSE43580.csv"
+json_path = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file, prioritizing files containing the cohort accession
+files = os.listdir(in_cohort_dir)
+soft_candidates = [f for f in files if ('soft' in f.lower()) and (cohort in f)]
+matrix_candidates = [f for f in files if ('matrix' in f.lower()) and (cohort in f)]
+
+if not soft_candidates:
+    soft_candidates = [f for f in files if 'soft' in f.lower()]
+if not matrix_candidates:
+    matrix_candidates = [f for f in files if 'matrix' in f.lower()]
+
+assert len(soft_candidates) > 0 and len(matrix_candidates) > 0
+soft_file = os.path.join(in_cohort_dir, soft_candidates[0])
+matrix_file = os.path.join(in_cohort_dir, matrix_candidates[0])
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Create an informative clinical features dictionary with feature names as keys
+clinical_df = clinical_data.copy()
+
+# Keep only characteristics rows and drop the identifier column
+if '!Sample_geo_accession' in clinical_df.columns:
+    characteristics_mask = clinical_df['!Sample_geo_accession'].astype(str).str.contains('!Sample_characteristics_ch1', na=False)
+    clinical_df = clinical_df[characteristics_mask].copy()
+    clinical_df = clinical_df.drop(columns=['!Sample_geo_accession'], errors='ignore')
+
+# Derive feature names from the text before the colon in the first non-null value per row
+new_index = []
+seen = {}
+for i, row in clinical_df.iterrows():
+    vals = [v for v in row.tolist() if pd.notnull(v)]
+    label = None
+    for v in vals:
+        if isinstance(v, str) and (':' in v):
+            label = v.split(':', 1)[0].strip()
+            break
+    if label is None:
+        label = f"feature_{i}"
+    count = seen.get(label, 0)
+    final_label = f"{label}#{count+1}" if count > 0 else label
+    seen[label] = count + 1
+    new_index.append(final_label)
+
+clinical_df.index = new_index
+
+# Limit number of unique values per feature explicitly
+sample_characteristics_dict = get_unique_values_by_row(clinical_df, max_len=30)
+
+# 4. Print background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+import numpy as np
+
+# 1) Determine gene expression availability from background info
+is_gene_available = True  # Series explicitly states "Gene Expression Profiles"
+
+# Helper: extract header (text before colon) in a cell
+def _extract_header_from_cell(x: str) -> str:
+    if not isinstance(x, str):
+        return None
+    if ':' in x:
+        return x.split(':', 1)[0].strip().lower()
+    return None
+
+# Build a map from row index to header (by inspecting the first valid cell in each row)
+row_header_map = {}
+for ridx, row in clinical_data.iterrows():
+    header = None
+    for v in row.values:
+        header = _extract_header_from_cell(v)
+        if header:
+            break
+    row_header_map[ridx] = header
+
+# Utility: find row by exact header or by predicate
+def find_row_by_header(targets_exact=None, contains_any=None):
+    targets_exact = [t.lower() for t in (targets_exact or [])]
+    contains_any = [t.lower() for t in (contains_any or [])]
+    # exact match first
+    for ridx, header in row_header_map.items():
+        if header and header in targets_exact:
+            return ridx
+    # then contains_any
+    for ridx, header in row_header_map.items():
+        if header and any(tok in header for tok in contains_any):
+            return ridx
+    return None
+
+# 2) Identify rows for trait (GERD), age, gender
+
+# Trait row: look for 'clinical diagnosis patient' or similar, then ensure GERD is representable
+trait_row = find_row_by_header(
+    targets_exact=['clinical diagnosis patient'],
+    contains_any=['diagnosis', 'diagnoses']
+)
+
+# Age row: prioritize 'age at excision (years)', otherwise fall back to typical age headers
+age_row = None
+preferred_age_headers = [
+    'age at excision (years)', 'age at diagnosis (years)', 'age at collection (years)',
+    'age (years)', 'age'
+]
+age_row = find_row_by_header(targets_exact=preferred_age_headers, contains_any=['age'])
+
+# Gender row: look for 'gender' or 'sex'
+gender_row = find_row_by_header(targets_exact=['gender', 'sex'], contains_any=['gender', 'sex'])
+
+# 2.2 Converters
+
+def _after_colon(value):
+    if not isinstance(value, str):
+        return None
+    if ':' in value:
+        return value.split(':', 1)[1].strip()
+    return value.strip() if value.strip() else None
+
+def convert_trait(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    s = val.lower()
+    # Treat explicit unknowns as None
+    if s in {'na', 'n/a', 'not available', 'unknown', ''}:
+        return None
+    # Positive if GERD explicitly present
+    if ('gastroesophageal reflux disease' in s) or ('gastro-oesophageal reflux disease' in s) or ('gerd' in s):
+        return 1
+    # Otherwise negative (no GERD mentioned among diagnoses)
+    return 0
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    s = val.lower()
+    if s in {'na', 'n/a', 'not available', 'unknown', ''}:
+        return None
+    try:
+        f = float(val)
+        # filter out implausible placeholders
+        if f <= 0 or f > 120:
+            return None
+        return f
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    s = val.lower()
+    if s in {'female', 'f'}:
+        return 0
+    if s in {'male', 'm'}:
+        return 1
+    if s in {'na', 'n/a', 'unknown', 'not available', ''}:
+        return None
+    return None
+
+# 2.1 Validate availability by checking variation (constant features are useless)
+def check_variation(row_idx, convert_fn):
+    if row_idx is None:
+        return None, set()
+    series = clinical_data.loc[row_idx]
+    converted = [convert_fn(v) for v in series.values]
+    uniq = {v for v in converted if v is not None}
+    if len(uniq) <= 1:
+        return None, uniq  # treat as unavailable if constant or all None
+    return row_idx, uniq
+
+# If trait_row candidate exists, ensure GERD signal is present (some 1s)
+if trait_row is not None:
+    # Ensure at least one positive exists; else treat as unavailable
+    converted_vals = [convert_trait(v) for v in clinical_data.loc[trait_row].values]
+    uniq_trait = {v for v in converted_vals if v is not None}
+    if uniq_trait == {0} or len(uniq_trait) <= 1:
+        trait_row = None  # no variation or no positives
+
+age_row, uniq_age = check_variation(age_row, convert_age)
+gender_row, uniq_gender = check_variation(gender_row, convert_gender)
+
+# 3) Initial filtering metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    # Extract and save
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+
+    # Preview and save
+    preview = preview_df(selected_clinical_df, n=5)
+    print("Preview of selected clinical features:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+else:
+    print("Trait data not available; skipping clinical feature extraction.")
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify mapping columns from annotation:
+# - Probe/ID column matches expression row IDs: 'ID'
+# - Gene symbol column: 'Gene Symbol'
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping: convert probe-level data to gene-level data
+probe_level_df = gene_data  # keep original probe-level data
+gene_data = apply_gene_mapping(probe_level_df, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Ensure clinical dataframe is available; load from disk if not in memory
+try:
+    selected_clinical_df
+except NameError:
+    if os.path.exists(out_clinical_data_file):
+        selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    else:
+        raise FileNotFoundError("Clinical data not found in memory or on disk.")
+
+# Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Derive availability flags from actual data; cast to built-in bool to avoid numpy.bool_ in JSON
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+is_trait_biased = bool(is_trait_biased)
+
+# 5. Final validation and save cohort info
+note = ("INFO: Dataset is a lung cancer cohort; GERD appears as a comorbidity in a small subset. "
+        "Trait may be imbalanced after filtering/imputation.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+is_usable = bool(is_usable)
+
+# 6. Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Gastroesophageal_reflux_disease_(GERD)/code/GSE68698.py

@@ -0,0 +1,125 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Gastroesophageal_reflux_disease_(GERD)"
+cohort = "GSE68698"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Gastroesophageal_reflux_disease_(GERD)"
+in_cohort_dir = "../DATA/GEO/Gastroesophageal_reflux_disease_(GERD)/GSE68698"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/GSE68698.csv"
+out_gene_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/gene_data/GSE68698.csv"
+out_clinical_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/clinical_data/GSE68698.csv"
+json_path = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Series summary explicitly states "Gene expression was measured"
+
+# 2) Variable availability and conversion functions
+# From the provided Sample Characteristics Dictionary:
+# 0: case/control (for SSc, not GERD), 1: tissue (constant), 2: biopsy site, 3: batch, 4: SSc subtype, 5: patient/control id
+# No GERD status, no age, no gender available.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(x).strip()
+
+def convert_trait(x):
+    # Binary: 1 = GERD present, 0 = GERD absent
+    # Conservative mapping for GERD; do NOT map generic "case" because in this dataset "case" = SSc, not GERD.
+    val = _after_colon(x).lower()
+    if val in {"na", "nan", "", "unknown", "undetermined"}:
+        return None
+    positives = [
+        "gerd", "gastroesophageal reflux", "gastro-oesophageal reflux", "reflux disease",
+        "gastroesophageal reflux disease", "gastro-oesophageal reflux disease", "ger"
+    ]
+    negatives = ["control", "healthy", "no gerd", "none", "no"]
+    if any(p in val for p in positives):
+        return 1
+    if any(n == val for n in negatives):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous: extract numeric age in years
+    val = _after_colon(x)
+    if val.lower() in {"na", "nan", "", "unknown"}:
+        return None
+    m = re.search(r"(\d+(\.\d+)?)", val)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    # Binary: female=0, male=1
+    val = _after_colon(x).lower()
+    if val in {"na", "nan", "", "unknown"}:
+        return None
+    if val in {"male", "m"}:
+        return 1
+    if val in {"female", "f"}:
+        return 0
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait data not available)
+# If trait_row were available:
+if trait_row is not None:
+    selected = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected.to_csv(out_clinical_data_file, index=True)

output/preprocess/Gastroesophageal_reflux_disease_(GERD)/code/GSE77563.py

@@ -0,0 +1,235 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Gastroesophageal_reflux_disease_(GERD)"
+cohort = "GSE77563"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Gastroesophageal_reflux_disease_(GERD)"
+in_cohort_dir = "../DATA/GEO/Gastroesophageal_reflux_disease_(GERD)/GSE77563"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/GSE77563.csv"
+out_gene_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/gene_data/GSE77563.csv"
+out_clinical_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/clinical_data/GSE77563.csv"
+json_path = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability (Affymetrix Human Gene 2.1 ST arrays => gene expression microarray)
+is_gene_available = True
+
+# 2) Variable availability
+trait_row = 5   # 'gastroesophageal reflux disease (gerd) status: GERD' / 'No GERD'
+age_row = 1     # 'age (yr): <num>'
+gender_row = 2  # 'gender: male' / 'gender: female'
+
+# 2.2) Converters
+def _after_colon(x):
+    if x is None:
+        return None
+    try:
+        s = str(x)
+        # Take the substring after the last colon to be safe
+        if ':' in s:
+            s = s.split(':', 1)[1]
+        return s.strip()
+    except Exception:
+        return None
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_lower = v.lower()
+    # Map GERD presence to 1, absence to 0
+    if any(tok in v_lower for tok in ['unknown', 'na', 'n/a']):
+        return None
+    # Explicit negatives
+    if 'no gerd' in v_lower or v_lower in ['no', 'none', 'control']:
+        return 0
+    # Positives
+    if 'gerd' in v_lower or v_lower in ['yes', 'case']:
+        # Guard against explicit "no gerd" handled above
+        return 1
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    # Extract numeric age
+    m = re.search(r'[-+]?\d*\.?\d+', v)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_lower = v.lower()
+    if any(tok in v_lower for tok in ['unknown', 'na', 'n/a']):
+        return None
+    # Female -> 0, Male -> 1
+    if v_lower in ['female', 'f', 'woman', 'women']:
+        return 0
+    if v_lower in ['male', 'm', 'man', 'men']:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait data available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# The observed identifiers are numeric probe IDs (e.g., Illumina ProbeID), not human gene symbols.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the appropriate columns for mapping
+probe_col = 'ID'  # Matches the probe IDs in the expression data
+gene_col = 'gene_assignment'  # Contains gene information including symbols
+
+# Build mapping dataframe (Probe ID -> Gene annotation text)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Normalize gene symbols and save normalized gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Ensure clinical features are available in this scope; reconstruct robustly
+if 'selected_clinical_df' not in globals():
+    rebuilt = False
+    try:
+        # Prefer reconstructing directly from clinical_data if available
+        if 'clinical_data' in globals():
+            selected_clinical_df = geo_select_clinical_features(
+                clinical_df=clinical_data,
+                trait=trait,
+                trait_row=trait_row,
+                convert_trait=convert_trait,
+                age_row=age_row,
+                convert_age=convert_age,
+                gender_row=gender_row,
+                convert_gender=convert_gender
+            )
+            rebuilt = True
+    except Exception:
+        rebuilt = False
+
+    if not rebuilt:
+        # Fallback: load from CSV saved in step 2 and assign expected index
+        selected_clinical_df = pd.read_csv(out_clinical_data_file)
+        # Assign the expected feature index in the correct order
+        expected_idx = [trait, 'Age', 'Gender']
+        selected_clinical_df.index = expected_idx[: len(selected_clinical_df)]
+
+# 3) Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 4) Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5) Assess bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6) Determine availability flags using native Python bool
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+trait_in_index = bool(trait in selected_clinical_df.index)
+trait_has_data = False
+if trait_in_index:
+    # Ensure Python bool, not numpy.bool_
+    trait_has_data = bool(selected_clinical_df.loc[trait].notna().any())
+is_trait_available = bool(trait_in_index and trait_has_data)
+
+# 7) Final validation and save cohort info
+note = "INFO: Affymetrix probe IDs mapped to gene symbols; symbols normalized via NCBI synonym list."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=bool(is_gene_available),
+    is_trait_available=bool(is_trait_available),
+    is_biased=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 8) Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Gastroesophageal_reflux_disease_(GERD)/code/TCGA.py

@@ -0,0 +1,346 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Gastroesophageal_reflux_disease_(GERD)"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Gastroesophageal_reflux_disease_(GERD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# 1) Select the most relevant TCGA cohort directory for GERD (organ/phenotype overlap)
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+lower_map = {d: d.lower() for d in subdirs}
+
+# Define matching keywords with simple weighting to prefer esophageal-related cohorts
+high_priority = ['gastroesophageal', 'gerd', 'reflux', 'esophag', 'esophageal']
+secondary = ['stomach', 'gastric']
+
+scores = {}
+for d, name in lower_map.items():
+    score = 0
+    score += sum(2 for k in high_priority if k in name)
+    score += sum(1 for k in secondary if k in name)
+    scores[d] = score
+
+# Pick the best match (expect ESCA to be selected)
+selected_dir_name = max(scores, key=lambda k: scores[k]) if scores else None
+if selected_dir_name is None or scores[selected_dir_name] == 0:
+    # No suitable TCGA cohort found; mark as skipped for this trait
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    # Print an empty list for columns to adhere to the instruction
+    print([])
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir_name)
+
+    # 2) Identify clinical and genetic file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3) Load both files
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+
+    # 4) Print clinical column names
+    print(list(clinical_df.columns))
+
+# Step 2: Find Candidate Demographic Features
+import re
+
+# Column names from the previous step
+columns = ['CDE_ID_3226963', '_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'additional_treatment_completion_success_outcome', 'age_at_initial_pathologic_diagnosis', 'age_began_smoking_in_years', 'alcohol_history_documented', 'amount_of_alcohol_consumption_per_day', 'antireflux_treatment_type', 'barretts_esophagus', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'city_of_procurement', 'clinical_M', 'clinical_N', 'clinical_T', 'clinical_stage', 'columnar_metaplasia_present', 'columnar_mucosa_dysplasia', 'columnar_mucosa_goblet_cell_present', 'country_of_birth', 'country_of_procurement', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_additional_surgery_procedure', 'days_to_new_tumor_event_after_initial_treatment', 'eastern_cancer_oncology_group', 'esophageal_tumor_cental_location', 'esophageal_tumor_involvement_site', 'form_completion_date', 'frequency_of_alcohol_consumption', 'gender', 'goblet_cells_present', 'h_pylori_infection', 'height', 'histological_type', 'history_of_esophageal_cancer', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'init_pathology_dx_method_other', 'initial_diagnosis_by', 'initial_pathologic_diagnosis_method', 'initial_weight', 'is_ffpe', 'karnofsky_performance_score', 'lost_follow_up', 'lymph_node_examined_count', 'lymph_node_metastasis_radiographic_evidence', 'neoplasm_histologic_grade', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type', 'new_neoplasm_occurrence_anatomic_site_text', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'number_of_lymphnodes_positive_by_he', 'number_of_lymphnodes_positive_by_ihc', 'number_of_relatives_diagnosed', 'number_pack_years_smoked', 'oct_embedded', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'person_neoplasm_cancer_status', 'planned_surgery_status', 'postoperative_rx_tx', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'progression_determined_by', 'radiation_therapy', 'reflux_history', 'residual_tumor', 'sample_type', 'sample_type_id', 'state_province_of_procurement', 'stopped_smoking_year', 'system_version', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tobacco_smoking_history', 'treatment_prior_to_surgery', 'tumor_tissue_site', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_ESCA_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_ESCA_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_ESCA_mutation_bcm_gene', '_GENOMIC_ID_data/public/TCGA/ESCA/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_ESCA_exp_HiSeq_exon', '_GENOMIC_ID_TCGA_ESCA_PDMRNAseq', '_GENOMIC_ID_TCGA_ESCA_hMethyl450', '_GENOMIC_ID_TCGA_ESCA_RPPA', '_GENOMIC_ID_TCGA_ESCA_exp_HiSeqV2', '_GENOMIC_ID_TCGA_ESCA_exp_HiSeq', '_GENOMIC_ID_TCGA_ESCA_miRNA_HiSeq', '_GENOMIC_ID_TCGA_ESCA_mutation_ucsc_maf_gene', '_GENOMIC_ID_TCGA_ESCA_gistic2', '_GENOMIC_ID_TCGA_ESCA_gistic2thd', '_GENOMIC_ID_TCGA_ESCA_mutation_broad_gene', '_GENOMIC_ID_TCGA_ESCA_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_ESCA_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_ESCA_PDMRNAseqCNV']
+
+def find_candidate_age_cols(cols):
+    res = []
+    for c in cols:
+        l = c.lower()
+        if (
+            'days_to_birth' in l or
+            l.startswith('age') or
+            'age_' in l or
+            l.endswith('_age') or
+            l == 'age'
+        ):
+            res.append(c)
+    return res
+
+def find_candidate_gender_cols(cols):
+    res = []
+    for c in cols:
+        l = c.lower()
+        if ('gender' in l) or re.search(r'(^|[_\W])sex([_\W]|$)', l):
+            res.append(c)
+    return res
+
+candidate_age_cols = find_candidate_age_cols(columns)
+candidate_gender_cols = find_candidate_gender_cols(columns)
+
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Preview extracted data if clinical_df exists and columns are present
+try:
+    _ = clinical_df  # check existence
+    age_cols_present = [c for c in candidate_age_cols if c in clinical_df.columns]
+    gender_cols_present = [c for c in candidate_gender_cols if c in clinical_df.columns]
+
+    if age_cols_present:
+        age_preview = preview_df(clinical_df[age_cols_present])
+        print(age_preview)
+    if gender_cols_present:
+        gender_preview = preview_df(clinical_df[gender_cols_present])
+        print(gender_preview)
+except NameError:
+    pass
+
+# Step 3: Select Demographic Features
+import math
+
+# Use previously created preview dicts if they exist; otherwise, set to None
+try:
+    age_preview = age_preview_dict if isinstance(age_preview_dict, dict) else None
+except NameError:
+    age_preview = None
+
+try:
+    gender_preview = gender_preview_dict if isinstance(gender_preview_dict, dict) else None
+except NameError:
+    gender_preview = None
+
+def is_missing(val):
+    if val is None:
+        return True
+    if isinstance(val, float):
+        try:
+            return math.isnan(val)
+        except Exception:
+            return False
+    if isinstance(val, str) and val.strip() == "":
+        return True
+    return False
+
+def fraction_missing(samples):
+    if not samples:
+        return 1.0
+    missing = sum(1 for v in samples if is_missing(v))
+    return missing / len(samples)
+
+# Determine if previews are usable (non-empty and contain at least one sample list with items)
+def preview_available(preview_dict):
+    if not isinstance(preview_dict, dict):
+        return False
+    for v in preview_dict.values():
+        if isinstance(v, list) and len(v) > 0:
+            return True
+    return False
+
+age_col = None
+gender_col = None
+
+# AGE: Prefer 'age_at_initial_pathologic_diagnosis' over 'days_to_birth' if both present
+preferred_age_cols = [c for c in ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] if c in candidate_age_cols]
+
+if preview_available(age_preview):
+    # Evaluate preferred candidates with simple plausibility checks
+    for col in preferred_age_cols:
+        samples = age_preview.get(col, [])
+        miss_frac = fraction_missing(samples)
+
+        if col == 'age_at_initial_pathologic_diagnosis':
+            valid_vals = []
+            for v in samples:
+                if is_missing(v):
+                    continue
+                try:
+                    fv = float(v)
+                    valid_vals.append(fv)
+                except Exception:
+                    pass
+            plaus_count = sum(1 for fv in valid_vals if 0 <= fv <= 120)
+            if miss_frac < 0.6 and plaus_count >= max(1, len(samples) // 2):
+                age_col = col
+                break
+
+        elif col == 'days_to_birth':
+            valid_vals = []
+            for v in samples:
+                if is_missing(v):
+                    continue
+                try:
+                    fv = float(v)
+                    valid_vals.append(fv)
+                except Exception:
+                    pass
+            # Typical days_to_birth are negative with magnitude of years in days
+            plaus_count = sum(1 for fv in valid_vals if fv <= -3650)  # at least ~10 years in magnitude
+            if miss_frac < 0.6 and plaus_count >= max(1, len(samples) // 2):
+                age_col = col
+                break
+
+    # If still None, try any other candidate that looks like a plausible age (0-120)
+    if age_col is None:
+        for col in candidate_age_cols:
+            if col in preferred_age_cols:
+                continue
+            samples = age_preview.get(col, [])
+            miss_frac = fraction_missing(samples)
+            valid_vals = []
+            for v in samples:
+                if is_missing(v):
+                    continue
+                try:
+                    fv = float(v)
+                    valid_vals.append(fv)
+                except Exception:
+                    pass
+            plaus_count = sum(1 for fv in valid_vals if 0 <= fv <= 120)
+            if miss_frac < 0.6 and plaus_count >= max(1, len(samples) // 2):
+                age_col = col
+                break
+else:
+    # Robust fallback when previews are unavailable
+    if 'age_at_initial_pathologic_diagnosis' in candidate_age_cols:
+        age_col = 'age_at_initial_pathologic_diagnosis'
+    elif 'days_to_birth' in candidate_age_cols:
+        age_col = 'days_to_birth'
+    else:
+        age_col = None
+
+# GENDER: Prefer 'gender'
+if preview_available(gender_preview):
+    # Validate 'gender' if present; otherwise, scan other candidates
+    scan_cols = ['gender'] if 'gender' in candidate_gender_cols else list(candidate_gender_cols)
+    for col in scan_cols:
+        samples = gender_preview.get(col, [])
+        miss_frac = fraction_missing(samples)
+        if miss_frac >= 0.6:
+            continue
+        valid_mapped = 0
+        for v in samples:
+            mapped = tcga_convert_gender(v)
+            if mapped in (0, 1):
+                valid_mapped += 1
+        if valid_mapped >= max(1, len(samples) // 2):
+            gender_col = col
+            break
+else:
+    # Robust fallback when previews are unavailable
+    if 'gender' in candidate_gender_cols:
+        gender_col = 'gender'
+    else:
+        gender_col = None
+
+print(f"Selected age_col: {age_col}, samples: {age_preview.get(age_col) if (age_col and preview_available(age_preview)) else None}")
+print(f"Selected gender_col: {gender_col}, samples: {gender_preview.get(gender_col) if (gender_col and preview_available(gender_preview)) else None}")
+
+# Step 4: Feature Engineering and Validation
+import os
+import pandas as pd
+
+# 1) Extract and standardize clinical features (trait, Age, Gender)
+# Fall back if previous step variables are not in scope or invalid
+try:
+    _ = age_col
+except NameError:
+    age_col = None
+try:
+    _ = gender_col
+except NameError:
+    gender_col = None
+
+if age_col not in clinical_df.columns:
+    age_col = None
+if gender_col not in clinical_df.columns:
+    gender_col = None
+
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2) Normalize gene symbols in gene expression data and save
+gene_expr = genetic_df.copy()
+
+def frac_startswith_tcga(x):
+    if len(x) == 0:
+        return 0.0
+    return sum(1 for v in x if isinstance(v, str) and v.startswith("TCGA-")) / len(x)
+
+# Ensure orientation: rows=genes, cols=samples
+cols_tcga_frac = frac_startswith_tcga(gene_expr.columns)
+idx_tcga_frac = frac_startswith_tcga(gene_expr.index)
+
+if idx_tcga_frac > cols_tcga_frac:
+    # Likely samples are in index and genes in columns; transpose
+    gene_expr = gene_expr.T
+
+# Now, rows are genes, columns are TCGA sample barcodes
+# Make sure values are numeric where possible
+gene_expr = gene_expr.apply(pd.to_numeric, errors='coerce')
+
+# Normalize gene symbols and aggregate duplicates
+gene_expr_norm = normalize_gene_symbols_in_index(gene_expr)
+
+# Save normalized gene expression (genes x samples)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_expr_norm.to_csv(out_gene_data_file)
+
+# 3) Link clinical and gene data on sample IDs
+common_samples = selected_clinical_df.index.intersection(gene_expr_norm.columns)
+linked_data = pd.concat(
+    [
+        selected_clinical_df.loc[common_samples],
+        gene_expr_norm.T.loc[common_samples]
+    ],
+    axis=1
+)
+
+# 4) Handle missing values systematically
+processed_df = handle_missing_values(linked_data, trait_col=trait)
+
+# 5) Determine bias and remove biased demographic features if needed
+trait_biased, processed_df = judge_and_remove_biased_features(processed_df, trait)
+trait_biased = bool(trait_biased)  # ensure native bool
+
+# 6) Final validation and save cohort info
+is_gene_available = bool((gene_expr_norm.shape[0] > 0) and (gene_expr_norm.shape[1] > 0))
+is_trait_available = bool((trait in selected_clinical_df.columns) and bool(selected_clinical_df[trait].notna().any()))
+
+note = (
+    "INFO: Cohort selected is TCGA ESCA. Trait encoded as tumor(1)/normal(0) via TCGA sample type. "
+    f"Age source: {age_col if age_col else 'None'}, Gender source: {gender_col if gender_col else 'None'}. "
+    "Gene symbols normalized using NCBI synonyms; duplicates aggregated by mean."
+)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=trait_biased,
+    df=processed_df,
+    note=note
+)
+
+# 7) Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    processed_df.to_csv(out_data_file)

output/preprocess/Gastroesophageal_reflux_disease_(GERD)/cohort_info.json

@@ -1,42 +1 @@
-{
-  "GSE77563": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 40
-  },
-  "GSE68698": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 46
-  },
-  "GSE43580": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 150
-  },
-  "TCGA": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 450
-  }
-}
+{"GSE77563": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 40, "note": "INFO: Affymetrix probe IDs mapped to gene symbols; symbols normalized via NCBI synonym list."}, "GSE68698": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE43580": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 149, "note": "INFO: Dataset is a lung cancer cohort; GERD appears as a comorbidity in a small subset. Trait may be imbalanced after filtering/imputation."}, "GSE28302": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 196, "note": "INFO: Cohort selected is TCGA ESCA. Trait encoded as tumor(1)/normal(0) via TCGA sample type. Age source: age_at_initial_pathologic_diagnosis, Gender source: gender. Gene symbols normalized using NCBI synonyms; duplicates aggregated by mean."}}

output/preprocess/Gaucher_Disease/clinical_data/GSE124283.csv

@@ -1,3 +1,3 @@
-0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29
-0.0,,,,,,,1.0,1.0,,,,,,,,,,,,,,,,,,,,,
-1.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,GSM3526881,GSM3526882,GSM3526883,GSM3526884,GSM3526885,GSM3526886,GSM3526887,GSM3526888,GSM3526889,GSM3526890,GSM3526891,GSM3526892,GSM3526893,GSM3526894,GSM3526895,GSM3526896,GSM3526897,GSM3526898,GSM3526899,GSM3526900,GSM3526901,GSM3526902,GSM3526903,GSM3526904,GSM3526905,GSM3526906,GSM3526907,GSM3526908,GSM3526909,GSM3526910,GSM3526911,GSM3526912,GSM3526913,GSM3526914,GSM3526915,GSM3526916,GSM3526917,GSM3526918,GSM3526919,GSM3526920,GSM3526921,GSM3526922,GSM3526923,GSM3526924,GSM3526925,GSM3526926,GSM3526927,GSM3526928,GSM3526929,GSM3526930,GSM3526931,GSM3526932,GSM3526933,GSM3526934,GSM3526935,GSM3526936,GSM3526937,GSM3526938,GSM3526939,GSM3526940,GSM3526941,GSM3526942,GSM3526943,GSM3526944,GSM3526945,GSM3526946,GSM3526947,GSM3526948,GSM3526949,GSM3526950,GSM3526951,GSM3526952,GSM3526953,GSM3526954,GSM3526955,GSM3526956,GSM3526957,GSM3526958,GSM3526959,GSM3526960,GSM3526961,GSM3526962,GSM3526963,GSM3526964,GSM3526965,GSM3526966,GSM3526967,GSM3526968,GSM3526969,GSM3526970,GSM3526971,GSM3526972,GSM3526973,GSM3526974,GSM3526975,GSM3526976,GSM3526977,GSM3526978,GSM3526979,GSM3526980,GSM3526981,GSM3526982,GSM3526983,GSM3526984,GSM3526985,GSM3526986,GSM3526987,GSM3526988,GSM3526989,GSM3526990,GSM3526991,GSM3526992,GSM3526993,GSM3526994,GSM3526995,GSM3526996,GSM3526997,GSM3526998,GSM3526999,GSM3527000,GSM3527001,GSM3527002,GSM3527003,GSM3527004,GSM3527005,GSM3527006,GSM3527007,GSM3527008,GSM3527009,GSM3527010,GSM3527011,GSM3527012,GSM3527013,GSM3527014,GSM3527015,GSM3527016,GSM3527017,GSM3527018,GSM3527019,GSM3527020,GSM3527021,GSM3527022,GSM3527023,GSM3527024
+Gaucher_Disease,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Gender,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Gaucher_Disease/code/GSE124283.py

@@ -0,0 +1,183 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Gaucher_Disease"
+cohort = "GSE124283"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Gaucher_Disease"
+in_cohort_dir = "../DATA/GEO/Gaucher_Disease/GSE124283"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Gaucher_Disease/GSE124283.csv"
+out_gene_data_file = "./output/z3/preprocess/Gaucher_Disease/gene_data/GSE124283.csv"
+out_clinical_data_file = "./output/z3/preprocess/Gaucher_Disease/clinical_data/GSE124283.csv"
+json_path = "./output/z3/preprocess/Gaucher_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Illumina whole-genome microarray on fibroblasts -> gene expression data is available
+
+# 2) Variable availability
+trait_row = 2  # 'condition' contains Gaucher vs others
+age_row = None  # No age field present in the characteristics
+gender_row = 3  # 'gender' field
+
+# 2.2) Converters
+def _extract_value(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if 'gaucher' in v:
+        return 1
+    if v in {'n/a', 'na', ''}:
+        return None
+    # All other specified conditions here are non-Gaucher (e.g., Control, NPC variants)
+    return 0
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None or val.lower() in {'n/a', 'na', ''}:
+        return None
+    # Extract the first number that looks like age
+    m = re.search(r'[-+]?\d*\.?\d+', val)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+        if 0 <= age <= 120:
+            return age
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    # Map female to 0, male to 1. 'K' (Polish 'kobieta') -> female.
+    if v in {'female', 'f', 'kobieta', 'k'}:
+        return 0
+    if v in {'male', 'm', 'mężczyzna', 'mezczyzna'}:
+        return 1
+    if v in {'n/a', 'na', '', 'unknown'}:
+        return None
+    # Fallback heuristics
+    if v.startswith('f'):
+        return 0
+    if v.startswith('m'):
+        return 1
+    return None
+
+# 3) Initial filtering metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction and save
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = False
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = (
+    f"INFO: Genes before/after normalization: {gene_data.shape[0]}/{normalized_gene_data.shape[0]}; "
+    f"Samples after filtering: {unbiased_linked_data.shape[0]}; "
+    f"Features after filtering: {unbiased_linked_data.shape[1]}."
+)
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Gaucher_Disease/code/TCGA.py

@@ -0,0 +1,65 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Gaucher_Disease"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Gaucher_Disease/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Gaucher_Disease/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Gaucher_Disease/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Gaucher_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Identify the most relevant TCGA cohort for Gaucher disease (likely none)
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Synonyms/related terms for Gaucher disease; conservative to avoid false matches
+synonyms = [
+    "gaucher", "lysosomal", "sphingolipid", "glucocerebrosid", "gba", "glucosylceramide", "acid beta-glucosidase"
+]
+
+def score_dir(name: str, keys):
+    lname = name.lower()
+    return sum(1 for k in keys if k in lname)
+
+scored = [(d, score_dir(d, synonyms)) for d in subdirs]
+scored.sort(key=lambda x: x[1], reverse=True)
+
+selected_tcga_subdir = None
+if scored and scored[0][1] > 0:
+    selected_tcga_subdir = scored[0][0]
+
+clinical_df = None
+genetic_df = None
+
+if selected_tcga_subdir is None:
+    print(f"No suitable TCGA cohort found for trait '{trait}'. Skipping this trait.")
+    # Record metadata as not available and mark completed
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_tcga_subdir)
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    def read_any(path):
+        compression = 'gzip' if path.endswith('.gz') else None
+        return pd.read_csv(path, sep='\t', index_col=0, low_memory=False, compression=compression)
+
+    clinical_df = read_any(clinical_path)
+    genetic_df = read_any(genetic_path)
+
+    print("Clinical data columns:")
+    print(clinical_df.columns.tolist())

output/preprocess/Gaucher_Disease/cohort_info.json

@@ -1,22 +1 @@
-{
-  "GSE124283": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "TCGA": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 423
-  }
-}
+{"GSE124283": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 143, "note": "INFO: Genes before/after normalization: 31424/20747; Samples after filtering: 143; Features after filtering: 20749."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Generalized_Anxiety_Disorder/clinical_data/GSE61672.csv

@@ -1,4 +1,4 @@
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output/preprocess/Generalized_Anxiety_Disorder/code/GSE61672.py

@@ -0,0 +1,199 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Generalized_Anxiety_Disorder"
+cohort = "GSE61672"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Generalized_Anxiety_Disorder"
+in_cohort_dir = "../DATA/GEO/Generalized_Anxiety_Disorder/GSE61672"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Generalized_Anxiety_Disorder/GSE61672.csv"
+out_gene_data_file = "./output/z3/preprocess/Generalized_Anxiety_Disorder/gene_data/GSE61672.csv"
+out_clinical_data_file = "./output/z3/preprocess/Generalized_Anxiety_Disorder/clinical_data/GSE61672.csv"
+json_path = "./output/z3/preprocess/Generalized_Anxiety_Disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1) Gene expression availability
+is_gene_available = True  # Based on series title/summary: blood genome-wide gene expression
+
+# 2) Identify rows in the Sample Characteristics Dictionary
+trait_row = 4   # contains 'anxiety case/control: case/control' and gad7 scores
+age_row = 0     # contains 'age: <number>'
+gender_row = 1  # contains 'Sex: F/M'
+
+# 2.2) Converters
+def _after_colon(x):
+    if x is None:
+        return None
+    try:
+        parts = str(x).split(":", 1)
+        if len(parts) == 2:
+            return parts[0].strip().lower(), parts[1].strip()
+        else:
+            return "", str(x).strip()
+    except Exception:
+        return "", None
+
+def convert_trait(x):
+    key, val = _after_colon(x)
+    if val is None or val in {"", ".", "na", "n/a", "nan", "none", "unknown"}:
+        return None
+    vlow = val.strip().lower()
+    # Prefer explicit case/control if present
+    if "anxiety case/control" in key:
+        if vlow in {"case", "cases", "gad", "anxiety", "patient"}:
+            return 1
+        if vlow in {"control", "controls", "healthy", "non-anxiety", "non anxiety", "no anxiety"}:
+            return 0
+        return None
+    # If only GAD7 score is present in this row, infer binary using a common clinical threshold (>=10 -> likely GAD)
+    if "gad7" in key:
+        try:
+            score = float(vlow)
+            return 1 if score >= 10 else 0
+        except Exception:
+            return None
+    return None
+
+def convert_age(x):
+    key, val = _after_colon(x)
+    if val is None:
+        return None
+    if "age" in key:
+        v = val.replace("years", "").replace("year", "").strip()
+        try:
+            return float(v)
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    key, val = _after_colon(x)
+    if val is None:
+        return None
+    if "sex" in key or "gender" in key:
+        v = val.strip().lower()
+        if v in {"f", "female", "woman", "girl"}:
+            return 0
+        if v in {"m", "male", "man", "boy"}:
+            return 1
+        return None
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if clinical data available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print("Preview of selected clinical features:", preview)
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# Illumina probe IDs (e.g., ILMN_1343291) are not human gene symbols and require mapping.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify appropriate columns for mapping
+# Probe IDs in expression data are 'ILMN_...' and match the 'ID' column in annotation.
+# Gene symbols are stored in the 'Symbol' column.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Apply mapping to convert probe-level data to gene-level expression
+probe_data = gene_data  # from previous step
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+# Load clinical data from saved file to ensure availability
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available_flag = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_flag = (trait in linked_data.columns) and (not linked_data[trait].isna().all())
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_flag,
+    is_trait_available=is_trait_available_flag,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note="INFO: Illumina probe IDs mapped to gene symbols; trait from case/control with GAD7>=10 fallback."
+)
+
+# 6. Save linked dataset if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)