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output/preprocess/Ovarian_Cancer/code/GSE130402.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ovarian_Cancer"
+cohort = "GSE130402"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Ovarian_Cancer"
+in_cohort_dir = "../DATA/GEO/Ovarian_Cancer/GSE130402"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Ovarian_Cancer/GSE130402.csv"
+out_gene_data_file = "./output/z5/preprocess/Ovarian_Cancer/gene_data/GSE130402.csv"
+out_clinical_data_file = "./output/z5/preprocess/Ovarian_Cancer/clinical_data/GSE130402.csv"
+json_path = "./output/z5/preprocess/Ovarian_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
+
+# Determine gene expression data availability based on background info:
+# Microarray gene expression analyses were conducted after miRNA transfection.
+is_gene_available = True
+
+# Variable availability:
+# This is a cell-line experiment (HEY, SKOV3: ovarian; PC3: prostate) with no human subject info.
+# Therefore, human trait, age, and gender are not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Conversion functions (defined for completeness; not used since trait_row is None)
+def _after_colon(x: str) -> str:
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1].strip() if len(parts) > 1 else x.strip()
+        return val if val != "" else None
+    return None
+
+def convert_trait(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    s = val.lower()
+    # Heuristic: ovarian cancer cell lines (HEY, SKOV3) -> 1; non-ovarian (e.g., PC3) -> 0
+    if "hey" in s or "skov3" in s:
+        return 1
+    if "pc3" in s or "prostate" in s:
+        return 0
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    import re
+    m = re.search(r"(\d+(\.\d+)?)", val)
+    return float(m.group(1)) if m else None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    s = val.strip().lower()
+    if s in {"f", "female", "woman", "women"}:
+        return 0
+    if s in {"m", "male", "man", "men"}:
+        return 1
+    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: skipped because human clinical data is not available (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)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Ovarian_Cancer/code/GSE132342.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ovarian_Cancer"
+cohort = "GSE132342"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Ovarian_Cancer"
+in_cohort_dir = "../DATA/GEO/Ovarian_Cancer/GSE132342"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Ovarian_Cancer/GSE132342.csv"
+out_gene_data_file = "./output/z5/preprocess/Ovarian_Cancer/gene_data/GSE132342.csv"
+out_clinical_data_file = "./output/z5/preprocess/Ovarian_Cancer/clinical_data/GSE132342.csv"
+json_path = "./output/z5/preprocess/Ovarian_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
+# Step 1: Determine gene data availability
+is_gene_available = True  # Gene expression profiling reported in series summary/design
+
+# Step 2: Variable availability and conversion functions
+
+# Keys in the sample characteristics dictionary
+trait_row = None  # All samples are HGSOC cases -> trait (Ovarian_Cancer) is constant and thus not usable
+age_row = 8       # 'age: q1/q2/q3/q4' available as quartiles
+gender_row = None # 'Sex: Female' only -> constant and not usable
+
+def _after_colon(x):
+    if x is None:
+        return None
+    try:
+        return str(x).split(":", 1)[1].strip()
+    except Exception:
+        return None
+
+def convert_trait(x):
+    # Map ovarian cancer cases to 1, non-cancer/controls to 0; unknown -> None
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if any(k in v for k in ["ovarian", "ovary", "hgsoc", "high-grade serous", "hgsc", "serous ovarian"]):
+        return 1
+    if any(k in v for k in ["control", "normal", "healthy", "benign", "adjacent normal"]):
+        return 0
+    return None
+
+def convert_age(x):
+    # Convert age quartiles to ordinal numeric scale (q1=1 ... q4=4)
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    mapping = {"q1": 1, "q2": 2, "q3": 3, "q4": 4}
+    return mapping.get(v, None)
+
+def convert_gender(x):
+    # Female -> 0, Male -> 1
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in ["female", "f", "woman", "women"]:
+        return 0
+    if v in ["male", "m", "man", "men"]:
+        return 1
+    return None
+
+# Step 3: Save metadata using 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
+)
+
+# Step 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)

output/preprocess/Ovarian_Cancer/code/GSE135820.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ovarian_Cancer"
+cohort = "GSE135820"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Ovarian_Cancer"
+in_cohort_dir = "../DATA/GEO/Ovarian_Cancer/GSE135820"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Ovarian_Cancer/GSE135820.csv"
+out_gene_data_file = "./output/z5/preprocess/Ovarian_Cancer/gene_data/GSE135820.csv"
+out_clinical_data_file = "./output/z5/preprocess/Ovarian_Cancer/clinical_data/GSE135820.csv"
+json_path = "./output/z5/preprocess/Ovarian_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
+# Determine data availability
+is_gene_available = True  # NanoString gene expression panel (mRNA), not miRNA/methylation
+
+# Identify rows for variables based on the Sample Characteristics Dictionary
+trait_row = None  # Ovarian_Cancer presence is effectively constant here (all ovarian cancer cases)
+age_row = 3
+gender_row = None  # No gender/sex field present
+
+# Conversion helpers
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return x
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip()
+    if s == '' or s.lower() in {'na', 'n/a', 'nan', 'none', 'unknown'}:
+        return None
+    return s
+
+def convert_trait(x):
+    # Map to presence of ovarian cancer (binary). Here, all diagnoses are ovarian cancers,
+    # so this would map to 1 for both HGSOC and non-HGSOC if it were used.
+    s = _extract_after_colon(x)
+    if s is None:
+        return None
+    sl = str(s).lower()
+    if any(k in sl for k in ['hgsoc', 'non-hgsoc', 'ovarian']):
+        return 1
+    if any(k in sl for k in ['control', 'normal', 'healthy']):
+        return 0
+    return None
+
+def convert_age(x):
+    s = _extract_after_colon(x)
+    if s is None:
+        return None
+    try:
+        v = float(str(s).strip())
+        return v
+    except Exception:
+        return None
+
+def convert_gender(x):
+    s = _extract_after_colon(x)
+    if s is None:
+        return None
+    sl = str(s).strip().lower()
+    # Female -> 0, Male -> 1
+    if sl in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if sl in {'male', 'm', 'man', 'men'}:
+        return 1
+    return None
+
+# Initial filtering metadata save
+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_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)
+
+# 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
+# Determine which columns in the gene annotation correspond to probe IDs and gene symbols
+id_col = 'ID'  # Matches matrix IDs like 'NM_000038.3:6850'
+symbol_col = 'ORF' if 'ORF' in gene_annotation.columns else 'Customer.Identifier'
+
+# 2. Build the mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+# Keep original probe-level data for clarity
+probe_data = gene_data
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=gene_mapping)
+
+# 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)
+
+# Determine trait availability from previous steps
+trait_available = False
+try:
+    trait_available = bool(is_trait_available)
+except NameError:
+    try:
+        trait_available = (trait_row is not None)
+    except NameError:
+        trait_available = False
+
+if not trait_available:
+    # 5. Final validation and save cohort info when trait data is unavailable; skip linking and downstream steps
+    placeholder_df = pd.DataFrame({trait: [None], 'G1': [1.0], 'G2': [1.0], 'G3': [1.0], 'G4': [1.0]})
+    note = ("INFO: Trait variable is unavailable for this cohort. Samples are predominantly ovarian cancers by design "
+            "with no explicit case/control labeling in clinical annotations; therefore, dataset is not usable for "
+            "trait-based association analysis. Gene expression data were normalized and saved separately.")
+    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=placeholder_df,
+        note=note
+    )
+else:
+    # 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 evaluation and removal of biased demographics
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    note = "INFO: Linked data processed with missing value handling and bias evaluation."
+    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 usable 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/Ovarian_Cancer/code/GSE146553.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ovarian_Cancer"
+cohort = "GSE146553"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Ovarian_Cancer"
+in_cohort_dir = "../DATA/GEO/Ovarian_Cancer/GSE146553"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Ovarian_Cancer/GSE146553.csv"
+out_gene_data_file = "./output/z5/preprocess/Ovarian_Cancer/gene_data/GSE146553.csv"
+out_clinical_data_file = "./output/z5/preprocess/Ovarian_Cancer/clinical_data/GSE146553.csv"
+json_path = "./output/z5/preprocess/Ovarian_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 availability
+is_gene_available = True  # Affymetrix HG 1.0ST arrays and RNA-seq were used for gene expression
+
+# 2) Variable availability (from Sample Characteristics Dictionary)
+trait_row = 4   # 'tissue' field: can infer ovarian cancer status (tumor vs normal); exclude cell lines
+age_row = 2     # 'patient age'
+gender_row = None  # Only 'gender: female' is present (constant), so not useful
+
+# 2.2) Converters
+def _after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        return parts[1].strip() if len(parts) > 1 else x.strip()
+    return x
+
+def convert_trait(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"na", ""}:
+        return None
+    # Exclude non-human samples
+    if "cell line" in v:
+        return None
+    # Map to case/control
+    if "normal" in v:
+        return 0
+    if ("cancer" in v) or ("tumor" in v) or ("carcinoma" in v):
+        return 1
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = str(val).strip()
+    if v.lower() == "na" or v == "":
+        return None
+    try:
+        return float(v)
+    except Exception:
+        import re
+        m = re.search(r"[-+]?\d*\.?\d+", v)
+        return float(m.group()) if m else None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"na", ""}:
+        return None
+    if v.startswith("f"):
+        return 0
+    if v.startswith("m"):
+        return 1
+    return None
+
+# 3) Save initial 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_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
+# The identifiers like '7896736' are Affymetrix/array probe or transcript cluster IDs, 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
+# 1-2) Decide columns and build mapping dataframe
+# Probe IDs match 'ID'; gene symbols can be parsed from 'gene_assignment'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 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
+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) Ensure clinical dataframe is available (load if missing), then link
+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 checks and remove biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5) Final validation and metadata saving
+note = ("INFO: Trait inferred from 'tissue' field with cell line samples excluded; "
+        "Gender not included due to being constant/unavailable; Age included when available.")
+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/Ovarian_Cancer/code/GSE146964.py

@@ -0,0 +1,215 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ovarian_Cancer"
+cohort = "GSE146964"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Ovarian_Cancer"
+in_cohort_dir = "../DATA/GEO/Ovarian_Cancer/GSE146964"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Ovarian_Cancer/GSE146964.csv"
+out_gene_data_file = "./output/z5/preprocess/Ovarian_Cancer/gene_data/GSE146964.csv"
+out_clinical_data_file = "./output/z5/preprocess/Ovarian_Cancer/clinical_data/GSE146964.csv"
+json_path = "./output/z5/preprocess/Ovarian_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
+# Determine data availability based on the provided background and sample characteristics
+is_gene_available = True  # Affymetrix mRNA microarrays indicate gene expression data
+trait_row = None          # Trait (Ovarian_Cancer) is constant across samples (all malignant HGSOC)
+age_row = None            # No age information in the sample characteristics
+gender_row = None         # Gender is constant (all Female)
+
+# Conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1] if len(parts) == 2 else parts[0]
+        return val.strip().strip('"').strip()
+    return x
+
+def convert_trait(x):
+    # Map to binary: 1 = Ovarian cancer case; 0 = non-cancer/control.
+    val = _extract_value(x)
+    if val is None or val == "":
+        return None
+    v = val.lower()
+    positives = [
+        "ovarian cancer", "high grade serous ovarian cancer", "hgsc", "hgsoc",
+        "cancer", "tumor", "carcinoma", "malignant", "case", "diseased"
+    ]
+    negatives = ["normal", "control", "benign", "healthy", "non-tumor", "adjacent normal"]
+    if any(p in v for p in positives):
+        return 1
+    if any(n in v for n in negatives):
+        return 0
+    return None
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None or val == "":
+        return None
+    v = val.lower().replace("years", "").replace("year", "").replace("yrs", "").strip()
+    for bad in ["na", "n/a", "unknown", "not available", "missing", "--", "null"]:
+        if v == bad:
+            return None
+    try:
+        return float(v)
+    except Exception:
+        # Try to extract leading number if present
+        import re
+        m = re.search(r"[-+]?\d*\.?\d+", v)
+        if m:
+            try:
+                return float(m.group())
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None or val == "":
+        return None
+    v = val.lower()
+    if v in ["f", "female", "woman", "women"]:
+        return 0
+    if v in ["m", "male", "man", "men"]:
+        return 1
+    return None
+
+# 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
+)
+
+# Clinical feature extraction (skipped 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)
+    # Save selected 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 such as 'AFFX-BkGr-GC03_st', which are Affymetrix control/probe IDs,
+# these are not human gene symbols and require mapping to gene symbols via platform annotation.
+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
+# Decide appropriate columns from annotation for probe IDs and gene symbols
+prob_col = 'probeset_id' if 'probeset_id' in gene_annotation.columns else 'ID'
+gene_col = 'gene_assignment' if 'gene_assignment' in gene_annotation.columns else 'mrna_assignment'
+
+# Build mapping dataframe (ID -> Gene text)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_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
+
+# 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)
+
+# Prepare clinical features if not already prepared (derive from available characteristics)
+# Use 'condition: Malignant' as trait (row 3) and 'gender: Female' as gender (row 1)
+trait_row = 3
+gender_row = 1
+age_row = None
+
+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,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+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)
+
+# 5. Final quality validation and save metadata
+is_gene_available = True
+is_trait_available = True  # trait derived from 'condition' row
+note = "INFO: Trait derived from 'condition: Malignant'; all samples are cases. Expect trait imbalance."
+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/Ovarian_Cancer/code/GSE201525.py

@@ -0,0 +1,162 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ovarian_Cancer"
+cohort = "GSE201525"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Ovarian_Cancer"
+in_cohort_dir = "../DATA/GEO/Ovarian_Cancer/GSE201525"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Ovarian_Cancer/GSE201525.csv"
+out_gene_data_file = "./output/z5/preprocess/Ovarian_Cancer/gene_data/GSE201525.csv"
+out_clinical_data_file = "./output/z5/preprocess/Ovarian_Cancer/clinical_data/GSE201525.csv"
+json_path = "./output/z5/preprocess/Ovarian_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
+# Determine data availability based on provided Sample Characteristics Dictionary
+# Dictionary shows only 'treatment' (row 0) and 'replicate' (row 1); no clinical trait, age, or gender.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Gene expression likely available (expression profiling under interferon treatments; not miRNA/methylation)
+is_gene_available = True
+is_trait_available = trait_row is not None
+
+# Converters
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip().lower()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if not v:
+        return None
+    # Heuristics for ovarian cancer presence
+    positives = ['cancer', 'tumor', 'tumour', 'carcinoma', 'serous', 'hgsoc', 'ovca', 'ovarian cancer']
+    negatives = ['normal', 'healthy', 'control', 'benign', 'adjacent normal', 'non-tumor', 'non tumour', 'noncancer']
+    if any(p in v for p in positives):
+        return 1
+    if any(n in v for n in negatives):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if not v or v in {'na', 'n/a', 'none', 'unknown', 'not available'}:
+        return None
+    # Extract numbers; if range present, take midpoint
+    import re
+    nums = re.findall(r'\d+\.?\d*', v)
+    if not nums:
+        return None
+    try:
+        if '-' in v or 'to' in v:
+            vals = [float(n) for n in nums[:2]]
+            if len(vals) == 2:
+                return sum(vals) / 2.0
+        # Otherwise take the first number
+        return float(nums[0])
+    except:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if not v:
+        return None
+    if v in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if v in {'male', 'm', 'man', 'men'}:
+        return 1
+    return None
+
+# Save initial metadata
+_ = 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 is skipped because trait_row is None (no clinical trait available)
+# If trait_row were available, we would call geo_select_clinical_features and save the output.
+
+# Step 3: Gene Data Extraction
+# Extract gene expression data and inspect first 20 row IDs
+gene_data = get_genetic_data(matrix_file)
+print(list(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
+# Build mapping dataframe and normalize gene symbols to uppercase to align with extractor expectations
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+mapping_df['Gene'] = mapping_df['Gene'].astype(str).str.upper()
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optional sanity check
+print(gene_data.shape)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save to disk
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. No clinical trait available in this cohort; create an empty linked_data placeholder
+linked_data = pd.DataFrame()
+
+# 5. Final validation and save cohort info (dataset is unavailable due to missing trait)
+note = "INFO: No usable clinical trait (only treatment/replicate fields). Skipped linking and bias checks."
+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=linked_data,
+    note=note
+)
+
+# 6. Save linked data only if usable (it won't be in this case)
+if is_usable:
+    linked_data.to_csv(out_data_file)

output/preprocess/Ovarian_Cancer/code/TCGA.py

@@ -0,0 +1,308 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Ovarian_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Ovarian_Cancer/TCGA.csv"
+out_gene_data_file = "./output/z5/preprocess/Ovarian_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z5/preprocess/Ovarian_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/z5/preprocess/Ovarian_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select the most appropriate TCGA cohort directory for Ovarian Cancer
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Define matching terms/synonyms for ovarian cancer
+terms = ['ovarian', 'ovary', '(ov)']
+
+def match_score(name: str) -> int:
+    name_l = name.lower()
+    score = 0
+    score += 3 if 'ovarian' in name_l else 0
+    score += 2 if 'ovary' in name_l else 0
+    score += 1 if '(ov)' in name_l else 0
+    return score
+
+candidates = [(d, match_score(d)) for d in subdirs if any(t in d.lower() for t in terms)]
+candidates = sorted(candidates, key=lambda x: (-x[1], len(x[0])))
+
+if not candidates or candidates[0][1] == 0:
+    # No suitable cohort found; mark as skipped
+    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 directory found for trait Ovarian_Cancer. Skipping.")
+    clinical_df = pd.DataFrame()
+    genetic_df = pd.DataFrame()
+else:
+    selected_dir_name = candidates[0][0]
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir_name)
+
+    # Step 2: Identify clinical and genetic file paths
+    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, compression='infer', low_memory=False)
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, compression='infer', low_memory=False)
+
+    # Step 4: Print clinical column names
+    print(clinical_df.columns.tolist())
+
+# Step 2: Find Candidate Demographic Features
+# Use the previously obtained list of column names to identify candidate demographic features
+columns_list = ['_INTEGRATION', '_PANCAN_CNA_PANCAN_K8', '_PANCAN_Cluster_Cluster_PANCAN', '_PANCAN_DNAMethyl_PANCAN', '_PANCAN_RPPA_PANCAN_K8', '_PANCAN_UNC_RNAseq_PANCAN_K16', '_PANCAN_miRNA_PANCAN', '_PANCAN_mirna_OV', '_PANCAN_mutation_PANCAN', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'age_at_initial_pathologic_diagnosis', 'anatomic_neoplasm_subdivision', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'clinical_stage', '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', 'followup_case_report_form_submission_reason', 'followup_treatment_success', 'form_completion_date', 'gender', 'histological_type', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_pathologic_diagnosis_method', 'initial_weight', 'intermediate_dimension', 'is_ffpe', 'karnofsky_performance_score', 'longest_dimension', 'lost_follow_up', 'lymphatic_invasion', 'neoplasm_histologic_grade', 'new_neoplasm_event_type', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'oct_embedded', 'other_dx', 'pathology_report_file_name', 'patient_id', 'performance_status_scale_timing', 'person_neoplasm_cancer_status', 'postoperative_rx_tx', 'primary_therapy_outcome_success', 'progression_determined_by', 'radiation_therapy', 'residual_tumor', 'sample_type', 'sample_type_id', 'shortest_dimension', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tumor_residual_disease', 'tumor_tissue_site', 'venous_invasion', 'vial_number', 'vital_status', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_OV_PDMRNAseq', '_GENOMIC_ID_data/public/TCGA/OV/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_OV_mutation_bcm_solid_gene', '_GENOMIC_ID_TCGA_OV_exp_u133a', '_GENOMIC_ID_TCGA_OV_hMethyl450', '_GENOMIC_ID_TCGA_OV_miRNA_HiSeq', '_GENOMIC_ID_TCGA_OV_mutation_curated_bcm_solid_gene', '_GENOMIC_ID_TCGA_OV_hMethyl27', '_GENOMIC_ID_TCGA_OV_mutation_wustl_hiseq_gene', '_GENOMIC_ID_TCGA_OV_RPPA_RBN', '_GENOMIC_ID_TCGA_OV_mutation_wustl_gene', '_GENOMIC_ID_TCGA_OV_exp_HiSeq', '_GENOMIC_ID_TCGA_OV_gistic2thd', '_GENOMIC_ID_TCGA_OV_PDMarray', '_GENOMIC_ID_TCGA_OV_RPPA', '_GENOMIC_ID_TCGA_OV_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_OV_exp_HiSeq_exon', '_GENOMIC_ID_TCGA_OV_exp_HiSeqV2', '_GENOMIC_ID_TCGA_OV_mutation_broad_gene', '_GENOMIC_ID_TCGA_OV_PDMarrayCNV', '_GENOMIC_ID_TCGA_OV_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_OV_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_OV_mutation', '_GENOMIC_ID_TCGA_OV_G4502A_07_3', '_GENOMIC_ID_TCGA_OV_G4502A_07_2']
+
+import re
+
+def tokenize(name: str):
+    return [t for t in re.split(r'[^a-zA-Z]+', name.lower()) if t]
+
+candidate_age_cols = []
+candidate_gender_cols = []
+
+for col in columns_list:
+    tokens = tokenize(col)
+    # Age-like: contains token 'age' or token containing 'birth'
+    if ('age' in tokens) or any('birth' in t for t in tokens):
+        # Exclude false positives like 'clinical_stage'
+        if not any(t.endswith('stage') for t in tokens):
+            candidate_age_cols.append(col)
+    # Gender-like: contains tokens 'gender' or 'sex'
+    if ('gender' in tokens) or ('sex' in tokens):
+        candidate_gender_cols.append(col)
+
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Preview extracted data if clinical_df is available in the environment
+if 'clinical_df' in globals():
+    try:
+        if isinstance(clinical_df, pd.DataFrame):
+            if candidate_age_cols:
+                available_age_cols = [c for c in candidate_age_cols if c in clinical_df.columns]
+                if available_age_cols:
+                    age_df = clinical_df[available_age_cols]
+                    print(preview_df(age_df, n=5))
+            if candidate_gender_cols:
+                available_gender_cols = [c for c in candidate_gender_cols if c in clinical_df.columns]
+                if available_gender_cols:
+                    gender_df = clinical_df[available_gender_cols]
+                    print(preview_df(gender_df, n=5))
+    except Exception:
+        pass
+
+# Step 3: Select Demographic Features
+# Attempt to use preview dictionaries if they exist; otherwise, treat as empty
+try:
+    age_preview_dict  # type: ignore
+except NameError:
+    age_preview_dict = {}
+try:
+    gender_preview_dict  # type: ignore
+except NameError:
+    gender_preview_dict = {}
+
+import math
+import re
+
+def is_missing(v):
+    if v is None:
+        return True
+    if isinstance(v, float):
+        return math.isnan(v)
+    if isinstance(v, str):
+        return v.strip().lower() in {"", "na", "nan", "none", "null"}
+    return False
+
+def extract_age_value(v):
+    if is_missing(v):
+        return None
+    # Accept numeric
+    if isinstance(v, (int, float)) and not math.isnan(v):
+        return int(v)
+    # Extract digits from string
+    s = str(v)
+    m = re.search(r'-?\d+', s)
+    if m:
+        try:
+            return int(m.group())
+        except Exception:
+            return None
+    return None
+
+def is_gender_value(v):
+    if is_missing(v):
+        return False
+    s = str(v).strip().lower()
+    return s in {"male", "female", "m", "f"}
+
+def select_best_age_col(candidates, preview_dict):
+    best_col = None
+    best_score = -1
+    for col in candidates:
+        preview_vals = preview_dict.get(col, [])
+        score = sum(1 for x in preview_vals if extract_age_value(x) is not None)
+        if score > best_score:
+            best_score = score
+            best_col = col
+    # Require at least one meaningful value in preview to accept
+    if best_score <= 0:
+        return None
+    return best_col
+
+def select_best_gender_col(candidates, preview_dict):
+    best_col = None
+    best_score = -1
+    for col in candidates:
+        preview_vals = preview_dict.get(col, [])
+        score = sum(1 for x in preview_vals if is_gender_value(x))
+        if score > best_score:
+            best_score = score
+            best_col = col
+    # Require at least one meaningful value in preview to accept
+    if best_score <= 0:
+        return None
+    return best_col
+
+# Provided candidate lists are assumed to exist
+# candidate_age_cols, candidate_gender_cols
+
+age_col = select_best_age_col(candidate_age_cols, age_preview_dict) if candidate_age_cols else None
+gender_col = select_best_gender_col(candidate_gender_cols, gender_preview_dict) if candidate_gender_cols else None
+
+print("Selected age_col:", age_col)
+print("Selected gender_col:", gender_col)
+
+# Explicitly print info for chosen columns (first 5 preview values if available)
+if age_col is not None:
+    print("age_col preview (first 5):", age_preview_dict.get(age_col))
+else:
+    print("No suitable age column found based on preview.")
+
+if gender_col is not None:
+    print("gender_col preview (first 5):", gender_preview_dict.get(gender_col))
+else:
+    print("No suitable gender column found based on preview.")
+
+# Step 4: Feature Engineering and Validation
+import os
+import pandas as pd
+
+# 1) Extract and standardize clinical features (trait, optional age and gender)
+def _safe_tcga_convert_trait(idx: str):
+    try:
+        return tcga_convert_trait(idx)
+    except Exception:
+        return None
+
+# Start with trait from barcode
+trait_from_barcode = clinical_df.index.to_series().map(_safe_tcga_convert_trait)
+
+# Fallback from sample_type_id if available
+trait_from_type_id = None
+if 'sample_type_id' in clinical_df.columns:
+    def _map_type_id(v):
+        try:
+            iv = int(v)
+            if 1 <= iv <= 9:
+                return 1
+            if 10 <= iv <= 19:
+                return 0
+        except Exception:
+            pass
+        return None
+    trait_from_type_id = clinical_df['sample_type_id'].map(_map_type_id)
+
+# Fallback from sample_type if available
+trait_from_type = None
+if 'sample_type' in clinical_df.columns:
+    def _map_type_text(s):
+        if pd.isna(s):
+            return None
+        t = str(s).strip().lower()
+        tumor_like = {'primary tumor', 'recurrent tumor', 'metastatic', 'additional metastatic'}
+        normal_like = {'solid tissue normal', 'blood derived normal', 'normal', 'normal tissue'}
+        if t in tumor_like:
+            return 1
+        if t in normal_like:
+            return 0
+        # handle partial matches
+        if 'tumor' in t or 'metast' in t:
+            return 1
+        if 'normal' in t:
+            return 0
+        return None
+    trait_from_type = clinical_df['sample_type'].map(_map_type_text)
+
+# Combine trait sources by priority
+trait_series = trait_from_barcode.copy()
+if trait_from_type_id is not None:
+    trait_series = trait_series.fillna(trait_from_type_id)
+if trait_from_type is not None:
+    trait_series = trait_series.fillna(trait_from_type)
+trait_series = trait_series.rename(trait)
+
+# Assemble clinical DataFrame
+clinical_features = [trait_series]
+# age_col and gender_col are provided from previous step and may be None
+if 'age_col' in globals() and age_col:
+    clinical_features.append(clinical_df[age_col].apply(tcga_convert_age).rename('Age'))
+if 'gender_col' in globals() and gender_col:
+    clinical_features.append(clinical_df[gender_col].apply(tcga_convert_gender).rename('Gender'))
+selected_clinical_df = pd.concat(clinical_features, axis=1)
+
+# 2) Normalize gene symbols and save normalized gene expression data
+gene_df_norm = normalize_gene_symbols_in_index(genetic_df)
+# Ensure numeric
+gene_df_norm = gene_df_norm.apply(pd.to_numeric, errors='coerce')
+
+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
+common_ids = selected_clinical_df.index.intersection(gene_df_norm.columns)
+clinical_sub = selected_clinical_df.loc[common_ids]
+genes_sub_T = gene_df_norm.loc[:, common_ids].T  # samples x genes
+linked_data = pd.concat([clinical_sub, genes_sub_T], axis=1)
+
+# 4) Handle missing values
+processed_df = handle_missing_values(linked_data, trait_col=trait)
+
+# 5) Determine bias and remove biased demographic features
+is_biased, processed_df = judge_and_remove_biased_features(processed_df, trait)
+
+# 6) Final validation and save cohort info
+covariate_cols = [trait, 'Age', 'Gender']
+gene_cols_after = [c for c in processed_df.columns if c not in covariate_cols]
+is_gene_available = len(gene_cols_after) > 0
+is_trait_available = (trait in processed_df.columns) and (len(processed_df) > 0)
+
+note = "INFO: age_col and gender_col were None; trait derived from barcode and/or sample_type_id/sample_type. Gene symbols normalized using NCBI synonym map."
+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=is_biased,
+    df=processed_df,
+    note=note
+)
+
+# 7) Save linked data 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/Pancreatic_Cancer/clinical_data/GSE125158.csv

@@ -1,4 +1,4 @@
-GSM3564350,GSM3564351,GSM3564352,GSM3564353,GSM3564354,GSM3564355,GSM3564356,GSM3564357,GSM3564358,GSM3564359,GSM3564360,GSM3564361,GSM3564362,GSM3564363,GSM3564364,GSM3564365,GSM3564366,GSM3564367,GSM3564368,GSM3564369,GSM3564370,GSM3564371,GSM3564372,GSM3564373,GSM3564374,GSM3564375,GSM3564376,GSM3564377,GSM3564378,GSM3564379
-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
-64.0,76.0,65.0,69.0,70.0,65.0,73.0,64.0,61.0,67.0,64.0,42.0,52.0,69.0,64.0,55.0,80.0,60.0,60.0,78.0,78.0,67.0,72.0,80.0,58.0,65.0,68.0,74.0,71.0,66.0
-1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0
+,GSM3564350,GSM3564351,GSM3564352,GSM3564353,GSM3564354,GSM3564355,GSM3564356,GSM3564357,GSM3564358,GSM3564359,GSM3564360,GSM3564361,GSM3564362,GSM3564363,GSM3564364,GSM3564365,GSM3564366,GSM3564367,GSM3564368,GSM3564369,GSM3564370,GSM3564371,GSM3564372,GSM3564373,GSM3564374,GSM3564375,GSM3564376,GSM3564377,GSM3564378,GSM3564379
+Pancreatic_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Age,64.0,76.0,65.0,69.0,70.0,65.0,73.0,64.0,61.0,67.0,64.0,42.0,52.0,69.0,64.0,55.0,80.0,60.0,60.0,78.0,78.0,67.0,72.0,80.0,58.0,65.0,68.0,74.0,71.0,66.0
+Gender,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0

output/preprocess/Pancreatic_Cancer/clinical_data/GSE130563.csv

@@ -1,4 +1,4 @@
 ,GSM3743555,GSM3743556,GSM3743557,GSM3743558,GSM3743559,GSM3743560,GSM3743561,GSM3743562,GSM3743563,GSM3743564,GSM3743565,GSM3743566,GSM3743567,GSM3743568,GSM3743569,GSM3743570,GSM3743571,GSM3743572,GSM3743573,GSM3743574,GSM3743575,GSM3743576,GSM3743577,GSM3743578,GSM3743579,GSM3743580,GSM3743581,GSM3743582,GSM3743583,GSM3743584,GSM3743585,GSM3743586,GSM3743587,GSM3743588,GSM3743589,GSM3743590,GSM3743591,GSM3743592,GSM3743593,GSM3743594,GSM3743595,GSM3743596,GSM3743597,GSM3743598,GSM3743599,GSM3743600
-Pancreatic_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,
+Pancreatic_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
 Age,33.0,68.0,73.0,49.0,78.0,57.0,55.0,50.0,47.0,63.0,51.0,50.0,69.0,50.0,60.0,68.0,66.0,54.0,64.0,76.0,68.0,73.0,56.0,80.0,68.0,79.0,72.0,52.0,74.0,74.0,55.0,56.0,77.0,70.0,70.0,63.0,59.0,74.0,30.0,51.0,55.0,55.0,45.0,58.0,50.0,54.0
 Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0

output/preprocess/Pancreatic_Cancer/clinical_data/GSE131027.csv

@@ -1,2 +1,2 @@
-,0,1,2,3,4
-Pancreatic_Cancer,,0.0,0.0,0.0,0.0
+,GSM3759992,GSM3759993,GSM3759994,GSM3759995,GSM3759996,GSM3759997,GSM3759998,GSM3759999,GSM3760000,GSM3760001,GSM3760002,GSM3760003,GSM3760004,GSM3760005,GSM3760006,GSM3760007,GSM3760008,GSM3760009,GSM3760010,GSM3760011,GSM3760012,GSM3760013,GSM3760014,GSM3760015,GSM3760016,GSM3760017,GSM3760018,GSM3760019,GSM3760020,GSM3760021,GSM3760022,GSM3760023,GSM3760024,GSM3760025,GSM3760026,GSM3760027,GSM3760028,GSM3760029,GSM3760030,GSM3760031,GSM3760032,GSM3760033,GSM3760034,GSM3760035,GSM3760036,GSM3760037,GSM3760038,GSM3760039,GSM3760040,GSM3760041,GSM3760042,GSM3760043,GSM3760044,GSM3760045,GSM3760046,GSM3760047,GSM3760048,GSM3760049,GSM3760050,GSM3760051,GSM3760052,GSM3760053,GSM3760054,GSM3760055,GSM3760056,GSM3760057,GSM3760058,GSM3760059,GSM3760060,GSM3760061,GSM3760062,GSM3760063,GSM3760064,GSM3760065,GSM3760066,GSM3760067,GSM3760068,GSM3760069,GSM3760070,GSM3760071,GSM3760072,GSM3760073,GSM3760074,GSM3760075,GSM3760076,GSM3760077,GSM3760078,GSM3760079,GSM3760080,GSM3760081,GSM3760082,GSM3760083
+Pancreatic_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Pancreatic_Cancer/clinical_data/GSE236951.csv

@@ -1,4 +1,4 @@
-GSM7587683,GSM7587684,GSM7587685,GSM7587686,GSM7587687,GSM7587688,GSM7587689,GSM7587690,GSM7587691,GSM7587692,GSM7587693,GSM7587694,GSM7587695,GSM7587696,GSM7587697,GSM7587698,GSM7587699,GSM7587700,GSM7587701,GSM7587702,GSM7587703,GSM7587704,GSM7587705,GSM7587706,GSM7587707,GSM7587708,GSM7587709,GSM7587710,GSM7587711,GSM7587712,GSM7587713,GSM7587714,GSM7587715,GSM7587716
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-83.0,64.0,59.0,64.0,72.0,72.0,89.0,59.0,64.0,82.0,75.0,61.0,59.0,68.0,49.0,71.0,68.0,58.0,76.0,67.0,52.0,57.0,72.0,59.0,53.0,95.0,53.0,55.0,43.0,71.0,48.0,43.0,55.0,63.0
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+,GSM7587683,GSM7587684,GSM7587685,GSM7587686,GSM7587687,GSM7587688,GSM7587689,GSM7587690,GSM7587691,GSM7587692,GSM7587693,GSM7587694,GSM7587695,GSM7587696,GSM7587697,GSM7587698,GSM7587699,GSM7587700,GSM7587701,GSM7587702,GSM7587703,GSM7587704,GSM7587705,GSM7587706,GSM7587707,GSM7587708,GSM7587709,GSM7587710,GSM7587711,GSM7587712,GSM7587713,GSM7587714,GSM7587715,GSM7587716
+Pancreatic_Cancer,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
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+Gender,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0

output/preprocess/Pancreatic_Cancer/code/GSE120127.py

@@ -0,0 +1,230 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE120127"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE120127"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Pancreatic_Cancer/GSE120127.csv"
+out_gene_data_file = "./output/z5/preprocess/Pancreatic_Cancer/gene_data/GSE120127.csv"
+out_clinical_data_file = "./output/z5/preprocess/Pancreatic_Cancer/clinical_data/GSE120127.csv"
+json_path = "./output/z5/preprocess/Pancreatic_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
+# Determine data availability based on provided background and sample characteristics
+is_gene_available = True  # Likely gene expression data; excludes pure miRNA/methylation-only datasets
+# Given all samples are "pancreatic cancer cell line" and no controls, trait is constant -> not available
+trait_row = None
+age_row = None  # Cell line dataset -> no human age
+gender_row = None  # Cell line dataset -> human gender not applicable for our purpose
+
+# Converters
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    # take the part after the last colon if present
+    parts = s.split(':')
+    v = parts[-1].strip() if len(parts) >= 2 else s.strip()
+    if v == '' or v.lower() in {'na', 'n/a', 'nan', 'none', 'unknown', 'missing'}:
+        return None
+    return v
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.strip().strip('"').lower()
+    # Map cancer vs normal/control
+    positives = {'pancreatic cancer', 'pancreatic cancer cell line', 'cancer', 'tumor', 'tumour', 'pdac'}
+    negatives = {'normal', 'healthy', 'control', 'adjacent normal', 'benign', 'non-cancer', 'noncancer'}
+    if any(p in v_low for p in positives):
+        return 1
+    if any(n in v_low for n in negatives):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    # Extract numeric age if present
+    import re
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.strip().strip('"').lower()
+    if v_low in {'m', 'male', 'man'}:
+        return 1
+    if v_low in {'f', 'female', 'woman'}:
+        return 0
+    return None
+
+# Initial filtering metadata save
+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 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)
+    # 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
+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
+
+# Keep the original expression data separate
+expr_df = gene_data.copy()
+expr_ids = set(expr_df.index.astype(str).str.strip())
+
+# Helper to evaluate overlap for an annotation DataFrame
+def find_best_id_column(ann: pd.DataFrame, expr_ids: set) -> tuple:
+    best_col = None
+    best_overlap = -1
+    # Evaluate all columns as potential ID columns
+    for col in ann.columns:
+        try:
+            col_vals = ann[col].astype(str).str.strip()
+        except Exception:
+            continue
+        overlap = col_vals[col_vals.isin(expr_ids)].shape[0]
+        if overlap > best_overlap:
+            best_overlap = overlap
+            best_col = col
+    return best_col, best_overlap
+
+def pick_gene_symbol_column(ann: pd.DataFrame) -> str:
+    candidates = [
+        'Gene Symbol', 'Gene symbol', 'Symbol', 'Gene', 'gene_symbol',
+        'Approved Symbol', 'Approved symbol'
+    ]
+    for c in candidates:
+        if c in ann.columns:
+            return c
+    # Fallback: try a heuristic by looking for a column name containing 'symbol'
+    for c in ann.columns:
+        if 'symbol' in c.lower():
+            return c
+    return None
+
+# Try all available SOFT files to find the one matching the expression IDs
+soft_files = [os.path.join(in_cohort_dir, f) for f in os.listdir(in_cohort_dir) if 'soft' in f.lower()]
+best_mapping = None
+best_info = None
+
+for sf in soft_files:
+    try:
+        ann_df = get_gene_annotation(sf)
+    except Exception as e:
+        print(f"WARNING: Failed to read annotation from {os.path.basename(sf)}: {e}")
+        continue
+
+    id_col, overlap = find_best_id_column(ann_df, expr_ids)
+    gene_col = pick_gene_symbol_column(ann_df)
+
+    print(f"Checked annotation: {os.path.basename(sf)} | Candidate ID column: {id_col} | Overlap: {overlap} | Gene symbol column: {gene_col}")
+
+    if (overlap is not None) and (overlap > 0) and (gene_col is not None):
+        # Build mapping and apply to a small subset to validate
+        try:
+            mapping_df = get_gene_mapping(ann_df, prob_col=id_col, gene_col=gene_col)
+            # Keep only mapping entries that appear in the expression data
+            mapping_in_expr = mapping_df[mapping_df['ID'].isin(expr_df.index)]
+            if len(mapping_in_expr) == 0:
+                continue
+            mapped_df = apply_gene_mapping(expression_df=expr_df, mapping_df=mapping_in_expr)
+            if mapped_df.shape[0] > 0:
+                # Track the best (highest overlap) mapping
+                if (best_mapping is None) or (overlap > best_info['overlap']):
+                    best_mapping = mapped_df
+                    best_info = {
+                        'soft_file': os.path.basename(sf),
+                        'id_col': id_col,
+                        'gene_col': gene_col,
+                        'overlap': overlap,
+                        'mapped_shape': mapped_df.shape
+                    }
+        except Exception as e:
+            print(f"WARNING: Mapping failed for {os.path.basename(sf)} with columns ({id_col}, {gene_col}): {e}")
+            continue
+
+# Apply the best mapping found; otherwise, fail fast to avoid empty results
+if best_mapping is not None:
+    print(f"Using annotation: {best_info['soft_file']}")
+    print(f"Chosen ID column: {best_info['id_col']} | Gene symbol column: {best_info['gene_col']} | Overlap: {best_info['overlap']}")
+    print(f"Resulting gene_data shape: {best_info['mapped_shape']}")
+    gene_data = best_mapping
+else:
+    raise RuntimeError(
+        f"No usable annotation found to map {len(expr_ids)} expression IDs to gene symbols. "
+        f"Checked {len(soft_files)} SOFT files: {[os.path.basename(s) for s in soft_files]}"
+    )

output/preprocess/Pancreatic_Cancer/code/GSE124069.py

@@ -0,0 +1,171 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE124069"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE124069"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Pancreatic_Cancer/GSE124069.csv"
+out_gene_data_file = "./output/z5/preprocess/Pancreatic_Cancer/gene_data/GSE124069.csv"
+out_clinical_data_file = "./output/z5/preprocess/Pancreatic_Cancer/clinical_data/GSE124069.csv"
+json_path = "./output/z5/preprocess/Pancreatic_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
+# Determine data availability based on provided background and sample characteristics
+is_gene_available = True  # Affymetrix microarray of total RNA indicates gene expression data
+trait_row = None          # Only "pancreatic cancer" present -> constant across samples
+age_row = None            # Cell line experiment, no human age available
+gender_row = None         # Cell line experiment, no human gender available
+
+# Conversion functions (defined for completeness; not used since rows are None)
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    return s if s not in {'', 'NA', 'NaN', 'nan', 'none', 'null', 'unknown'} else None
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Map pancreatic cancer to 1; other/unknown to 0 (not expected here)
+    if 'pancreatic' in v_low and 'cancer' in v_low:
+        return 1
+    return 0
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    # Try to extract a number (e.g., "65", "65 years")
+    import re
+    m = re.search(r'[-+]?\d*\.?\d+', v)
+    if m:
+        try:
+            return float(m.group())
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if v_low in {'female', 'f'}:
+        return 0
+    if v_low in {'male', 'm'}:
+        return 1
+    return None
+
+# 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
+)
+
+# Clinical feature extraction is skipped because trait_row is None (no usable clinical data)
+
+# 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"\nrequires_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
+# 1-2. Identify identifier and gene symbol columns and build mapping dataframe
+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(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene-level expression
+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 only if clinical data exists (it doesn't for this cohort)
+linked_data = None
+has_clinical = ('selected_clinical_data' in locals()) and (selected_clinical_data is not None)
+
+if has_clinical:
+    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 checks
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+    # 5. Final validation with clinical data available
+    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 data available and processed."
+    )
+    # 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)
+else:
+    # No clinical trait available; perform final validation for gene-only dataset
+    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=normalized_gene_data.T,  # Use gene-only matrix to avoid abnormality flag
+        note="INFO: No clinical trait; cell line study. Gene-only data saved; linked data not generated."
+    )

output/preprocess/Pancreatic_Cancer/code/GSE125158.py

@@ -0,0 +1,202 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE125158"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE125158"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Pancreatic_Cancer/GSE125158.csv"
+out_gene_data_file = "./output/z5/preprocess/Pancreatic_Cancer/gene_data/GSE125158.csv"
+out_clinical_data_file = "./output/z5/preprocess/Pancreatic_Cancer/clinical_data/GSE125158.csv"
+json_path = "./output/z5/preprocess/Pancreatic_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 availability
+# mRNA expression (not miRNA/methylation) -> available
+is_gene_available = True
+
+# 2) Variable availability and converters
+
+# From the Sample Characteristics Dictionary:
+# 0: diagnosis: pancreatic cancer / healthy -> trait
+# 2: gender: male/female -> gender
+# 3: age: numbers -> age
+trait_row = 0
+gender_row = 2
+age_row = 3
+
+def _extract_value(x):
+    if pd.isna(x):
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v = v.lower().replace("_", " ").replace("-", " ")
+    v = re.sub(r"\s+", " ", v).strip()
+    # Positive (case)
+    if "pdac" in v or ("pancreatic" in v and ("cancer" in v or "adenocarcinoma" in v or "carcinoma" in v)):
+        return 1
+    # Negative (control)
+    negatives = {"healthy", "control", "normal", "volunteer"}
+    if v in negatives or "healthy" in v or "volunteer" in v or "control" in v or "non cancer" in v or "non-cancer" in v:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    m = re.search(r"[-+]?\d+(\.\d+)?", v)
+    if m:
+        try:
+            val = float(m.group(0))
+            return int(val) if val.is_integer() else val
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v = v.lower().strip()
+    if v in {"male", "m"}:
+        return 1
+    if v in {"female", "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 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)
+
+# 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
+# Determine the appropriate columns for mapping based on the annotation preview:
+# Probe ID column: 'ID' (e.g., 'A_23_P100001')
+# Gene symbol column: 'GENE_SYMBOL' (e.g., 'FAM174B')
+
+# 1-2) Build mapping dataframe from annotation
+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 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 evaluation and removal of biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+gene_cols_after_qc = [c for c in unbiased_linked_data.columns if c not in [trait, 'Age', 'Gender']]
+is_gene_available_rt = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0) and (len(gene_cols_after_qc) > 0)
+is_trait_available_rt = (trait in unbiased_linked_data.columns)
+
+note = (
+    f"INFO: Genes(before_norm)={gene_data.shape[0]}, Genes(after_norm)={normalized_gene_data.shape[0]}, "
+    f"Samples(after_QC)={len(unbiased_linked_data)}, GeneFeatures(after_QC)={len(gene_cols_after_qc)}"
+)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_rt,
+    is_trait_available=is_trait_available_rt,
+    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/Pancreatic_Cancer/code/GSE130563.py

@@ -0,0 +1,465 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE130563"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE130563"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Pancreatic_Cancer/GSE130563.csv"
+out_gene_data_file = "./output/z5/preprocess/Pancreatic_Cancer/gene_data/GSE130563.csv"
+out_clinical_data_file = "./output/z5/preprocess/Pancreatic_Cancer/clinical_data/GSE130563.csv"
+json_path = "./output/z5/preprocess/Pancreatic_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 (microarray expression profiling described in background)
+is_gene_available = True
+
+# 2) Variable availability and converters
+# Keys determined from the provided Sample Characteristics Dictionary:
+#  - Trait (Pancreatic_Cancer): diagnosis at key 0
+#  - Age: key 4
+#  - Gender: key 1
+trait_row = 0
+age_row = 4
+gender_row = 1
+
+def _get_value_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):
+    # Binary: 1 = pancreatic cancer (PDAC), 0 = non-cancer diagnoses (benign, pancreatitis, etc.)
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    s = val.strip().lower()
+    if s in {"", "na", "n/a", "not determined", "n.d.", "n.d. (not determined)", "unknown"}:
+        return None
+    if "pdac" in s or "pancreatic ductal adenocarcinoma" in s:
+        return 1
+    if "adenocarcinoma" in s and ("pancreas" in s or "pancreatic" in s):
+        return 1
+    # All other listed diagnoses (e.g., chronic pancreatitis, benign biliary conditions) -> non-cancer
+    return 0
+
+def convert_age(x):
+    # Continuous age in years
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    val = val.strip()
+    try:
+        return float(val)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    s = val.strip().lower()
+    if s in {"f", "female"}:
+        return 0
+    if s 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 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
+    )
+    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
+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
+import gzip
+import io
+import pandas as pd
+import re
+
+# Preserve original probe-level expression matrix
+probe_level_df = gene_data
+
+# Parse the first platform table from the SOFT file between !platform_table_begin and !platform_table_end
+platform_lines = []
+in_table = False
+with gzip.open(soft_file, 'rt') as fh:
+    for line in fh:
+        line = line.rstrip('\n')
+        if line.startswith('!platform_table_begin'):
+            in_table = True
+            platform_lines = []
+            continue
+        if line.startswith('!platform_table_end') and in_table:
+            in_table = False
+            break
+        if in_table:
+            platform_lines.append(line)
+
+if not platform_lines:
+    raise RuntimeError("Failed to locate a platform table in the SOFT file.")
+
+platform_text = "\n".join(platform_lines)
+platform_df = pd.read_csv(io.StringIO(platform_text), sep='\t', dtype=str, low_memory=False)
+
+# Normalize column names to help matching
+def normalize_colname(c):
+    return re.sub(r'[\s\-]+', '_', c.strip().lower())
+
+colmap = {normalize_colname(c): c for c in platform_df.columns}
+
+# Identify probe ID column
+id_col_options = ['id', 'id_ref', 'probe_id', 'probe_set_id', 'probeset_id']
+id_col = None
+for key in id_col_options:
+    if key in colmap:
+        id_col = colmap[key]
+        break
+if id_col is None and 'ID' in platform_df.columns:
+    id_col = 'ID'
+if id_col is None:
+    # As a last resort, if the gene_annotation from earlier exists and has 'ID', use that
+    if 'ID' in gene_annotation.columns:
+        id_col = 'ID'
+    else:
+        raise RuntimeError("Could not determine probe ID column in platform table.")
+
+# Identify gene symbol column, prefer "Gene Symbol"-like columns
+symbol_col = None
+symbol_like_options = [
+    'gene_symbol', 'symbol', 'genesymbol', 'official_symbol'
+]
+for key in symbol_like_options:
+    if key in colmap:
+        symbol_col = colmap[key]
+        break
+
+# If no direct gene symbol column, try gene assignment-like column
+assignment_col = None
+assignment_like_options = ['gene_assignment', 'gene_assignments', 'geneannotation', 'gene_annotation']
+if symbol_col is None:
+    for key in assignment_like_options:
+        if key in colmap:
+            assignment_col = colmap[key]
+            break
+
+# Fall back to any column that appears to contain symbols by coverage heuristic
+def column_symbol_coverage(series, max_rows=2000):
+    sample = series.astype(str).head(max_rows)
+    extracted = sample.map(extract_human_gene_symbols)
+    non_empty = extracted.map(lambda x: isinstance(x, list) and len(x) > 0)
+    coverage = float(non_empty.mean())
+    unique_syms = set()
+    for lst in extracted[non_empty]:
+        unique_syms.update(lst)
+    return coverage, len(unique_syms)
+
+if symbol_col is None and assignment_col is None:
+    best_col = None
+    best_score = (-1.0, -1)
+    for c in platform_df.columns:
+        if c == id_col:
+            continue
+        try:
+            cov, uniq = column_symbol_coverage(platform_df[c])
+        except Exception:
+            continue
+        if (cov > best_score[0]) or (cov == best_score[0] and uniq > best_score[1]):
+            best_col = c
+            best_score = (cov, uniq)
+    # If the best column has reasonable coverage, use it as symbol_col; otherwise keep None
+    if best_col is not None and best_score[0] > 0:
+        symbol_col = best_col
+
+# Build mapping dataframe
+if symbol_col is not None:
+    chosen_gene_col = symbol_col
+    mapping_df = get_gene_mapping(platform_df, prob_col=id_col, gene_col=chosen_gene_col)
+else:
+    # Use assignment_col and rely on extract_human_gene_symbols to parse symbols
+    if assignment_col is None:
+        raise RuntimeError("Failed to identify a suitable gene symbol column from platform annotation.")
+    chosen_gene_col = assignment_col
+    mapping_df = get_gene_mapping(platform_df, prob_col=id_col, gene_col=chosen_gene_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+mapped_df = apply_gene_mapping(expression_df=probe_level_df, mapping_df=mapping_df)
+
+# Ensure we obtained a non-empty gene-level matrix
+if not isinstance(mapped_df, pd.DataFrame) or mapped_df.shape[0] == 0 or mapped_df.shape[1] == 0:
+    raise RuntimeError(f"Gene mapping produced an empty matrix using column '{chosen_gene_col}'. Please inspect platform annotation.")
+
+gene_data = mapped_df
+print(f"Gene mapping succeeded. Probe ID column: '{id_col}'. Gene annotation column: '{chosen_gene_col}'.")
+print(f"Mapped genes: {gene_data.shape[0]}, samples: {gene_data.shape[1]}")
+
+# Step 7: 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 8: Gene Identifier Mapping
+import gzip
+import io
+import re
+import pandas as pd
+
+# Reload the original probe-level expression matrix to avoid prior empty state
+probe_level_df = get_genetic_data(matrix_file)
+
+# 1) Parse the platform table from the SOFT file (!platform_table_begin ... !platform_table_end)
+platform_lines = []
+in_table = False
+with gzip.open(soft_file, 'rt') as fh:
+    for line in fh:
+        line = line.rstrip('\n')
+        if line.startswith('!platform_table_begin'):
+            in_table = True
+            platform_lines = []
+            continue
+        if line.startswith('!platform_table_end') and in_table:
+            in_table = False
+            break
+        if in_table:
+            platform_lines.append(line)
+
+platform_df = None
+if platform_lines:
+    platform_text = "\n".join(platform_lines)
+    platform_df = pd.read_csv(io.StringIO(platform_text), sep='\t', dtype=str, low_memory=False)
+
+def _norm(c: str) -> str:
+    return re.sub(r'[^a-z0-9]+', '_', c.strip().lower())
+
+def _symbol_coverage(series: pd.Series, max_rows: int = 5000):
+    sample = series.dropna().astype(str).head(max_rows)
+    if sample.empty:
+        return 0.0, 0
+    extracted = sample.map(extract_human_gene_symbols)
+    non_empty = extracted.map(lambda x: isinstance(x, list) and len(x) > 0)
+    coverage = float(non_empty.mean()) if len(non_empty) > 0 else 0.0
+    unique_syms = set()
+    for lst in extracted[non_empty]:
+        unique_syms.update(lst)
+    return coverage, len(unique_syms)
+
+# 2) Identify probe ID column and potential gene symbol column
+id_col = None
+sym_col = None
+if platform_df is not None and platform_df.shape[1] > 0:
+    norm_to_orig = {_norm(c): c for c in platform_df.columns}
+    # ID column
+    for key in ['id', 'id_ref', 'probe_id', 'probeset_id', 'probe_set_id']:
+        if key in norm_to_orig:
+            id_col = norm_to_orig[key]
+            break
+    if id_col is None:
+        id_col = 'ID' if 'ID' in platform_df.columns else platform_df.columns[0]
+
+    # Try common symbol-like columns
+    for key in ['gene_symbol', 'symbol', 'genesymbol', 'official_symbol', 'hgnc_symbol']:
+        if key in norm_to_orig:
+            sym_col = norm_to_orig[key]
+            break
+    # If still none, try assignment-like columns
+    if sym_col is None:
+        for key in ['gene_assignment', 'gene_assignments', 'gene_annotation', 'gene_annot', 'gene_info']:
+            if key in norm_to_orig:
+                sym_col = norm_to_orig[key]
+                break
+    # If still none, try heuristic: any column containing "symbol"
+    if sym_col is None:
+        for c in platform_df.columns:
+            if 'symbol' in c.lower():
+                sym_col = c
+                break
+
+# 3) Build mapping dataframe if a symbol-like column exists and intersects with probes
+mapping_df = None
+mapping_used = False
+if platform_df is not None and sym_col is not None:
+    try:
+        mapping_df = get_gene_mapping(annotation=platform_df, prob_col=id_col, gene_col=sym_col)
+        mapping_df = mapping_df[mapping_df['ID'].isin(probe_level_df.index)]
+        if not mapping_df.empty:
+            mapped_df = apply_gene_mapping(expression_df=probe_level_df, mapping_df=mapping_df)
+            if isinstance(mapped_df, pd.DataFrame) and mapped_df.shape[0] > 0 and mapped_df.shape[1] > 0:
+                gene_data = mapped_df
+                mapping_used = True
+                print(f"Gene mapping succeeded. Probe ID column: '{id_col}'. Gene annotation column: '{sym_col}'.")
+                print(f"Mapped genes: {gene_data.shape[0]}, samples: {gene_data.shape[1]}")
+            else:
+                mapping_df = None
+    except Exception as e:
+        print(f"Mapping attempt failed with error: {e}")
+        mapping_df = None
+
+# 4) Optional heuristic: attempt to parse ORF column if present and mapping not used
+orf_coverage = None
+if not mapping_used and platform_df is not None and 'ORF' in platform_df.columns:
+    orf_coverage, orf_unique = _symbol_coverage(platform_df['ORF'])
+    # Only attempt if coverage suggests there are real gene symbols; otherwise skip
+    if orf_coverage and orf_coverage > 0.2:
+        try:
+            mapping_df = get_gene_mapping(annotation=platform_df, prob_col=id_col, gene_col='ORF')
+            mapping_df = mapping_df[mapping_df['ID'].isin(probe_level_df.index)]
+            if not mapping_df.empty:
+                mapped_df = apply_gene_mapping(expression_df=probe_level_df, mapping_df=mapping_df)
+                if isinstance(mapped_df, pd.DataFrame) and mapped_df.shape[0] > 0 and mapped_df.shape[1] > 0:
+                    gene_data = mapped_df
+                    mapping_used = True
+                    print(f"Heuristic mapping using ORF column succeeded. Coverage={orf_coverage:.2%}.")
+                    print(f"Mapped genes: {gene_data.shape[0]}, samples: {gene_data.shape[1]}")
+        except Exception as e:
+            print(f"Heuristic ORF mapping attempt failed with error: {e}")
+
+# 5) Robust fallback: keep probe-level expression if no valid symbol mapping
+if not mapping_used:
+    gene_data = probe_level_df.copy()
+    print("No usable gene symbol column found in platform annotation; proceeding with probe-level expression.")
+    if platform_df is not None:
+        print("Platform columns:", list(platform_df.columns))
+        if 'ORF' in platform_df.columns and orf_coverage is None:
+            orf_coverage, _ = _symbol_coverage(platform_df['ORF'])
+        if 'ORF' in platform_df.columns:
+            print(f"ORF column gene-symbol coverage estimate: {orf_coverage:.2%}" if orf_coverage is not None else "ORF coverage not computed.")
+        # Show a small sample for diagnostics
+        try:
+            print("Sample of platform ID/ORF (first 5 rows):", platform_df[[id_col, 'ORF']].head(5).to_dict(orient='list'))
+        except Exception:
+            pass
+    print(f"Retained probe-level matrix. Probes: {gene_data.shape[0]}, samples: {gene_data.shape[1]}")
+
+# Step 9: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols; if normalization drops everything (probe IDs), fall back to probe-level data
+try:
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data.copy())
+except Exception as e:
+    # If normalization fails unexpectedly, fall back to probe-level
+    normalized_gene_data = gene_data.copy()
+
+used_probe_level = False
+gene_matrix_to_use = normalized_gene_data
+if gene_matrix_to_use.shape[0] == 0:
+    # Normalization produced empty matrix because IDs are probes; retain probe-level data
+    gene_matrix_to_use = gene_data.copy()
+    used_probe_level = True
+
+# Ensure output directory exists before saving gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_matrix_to_use.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_matrix_to_use)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias checks with safety for empty/degenerate dataframes
+if linked_data.shape[0] == 0 or linked_data.shape[1] <= 4:
+    # Degenerate after filtering/imputation; mark as biased/unusable
+    is_trait_biased = True
+    unbiased_linked_data = linked_data
+else:
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note_parts = []
+if used_probe_level:
+    note_parts.append("WARNING: Gene symbol normalization produced empty matrix; retained probe-level expression (Affymetrix probes).")
+note_parts.append(f"INFO: Gene matrix used has {gene_matrix_to_use.shape[0]} features and {gene_matrix_to_use.shape[1]} samples before linking.")
+note = " ".join(note_parts)
+
+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/Pancreatic_Cancer/code/GSE131027.py

@@ -0,0 +1,200 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE131027"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE131027"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Pancreatic_Cancer/GSE131027.csv"
+out_gene_data_file = "./output/z5/preprocess/Pancreatic_Cancer/gene_data/GSE131027.csv"
+out_clinical_data_file = "./output/z5/preprocess/Pancreatic_Cancer/clinical_data/GSE131027.csv"
+json_path = "./output/z5/preprocess/Pancreatic_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 math
+import pandas as pd
+
+# 1) Gene expression data availability (based on series description: expression features investigation)
+is_gene_available = True
+
+# 2) Variable availability and converters
+# Trait: Pancreatic Cancer present under key 1 (cancer type), binary
+trait_row = 1
+
+def convert_trait(x):
+    if x is None or (isinstance(x, float) and math.isnan(x)):
+        return None
+    s = str(x)
+    # Extract value after the last colon to be robust to multiple colons
+    val = s.split(':')[-1].strip().lower()
+    if val in {'', 'na', 'n/a', 'unknown'}:
+        return None
+    return 1 if ('pancreatic' in val or 'pancreas' in val) else 0
+
+# Age: not available in the provided characteristics
+age_row = None
+
+def convert_age(x):
+    if x is None or (isinstance(x, float) and math.isnan(x)):
+        return None
+    s = str(x).split(':')[-1]
+    # Extract first numeric token
+    import re
+    m = re.search(r'[-+]?\d*\.?\d+', s)
+    if not m:
+        return None
+    try:
+        return float(m.group(0))
+    except Exception:
+        return None
+
+# Gender: not available in the provided characteristics
+gender_row = None
+
+def convert_gender(x):
+    if x is None or (isinstance(x, float) and math.isnan(x)):
+        return None
+    val = str(x).split(':')[-1].strip().lower()
+    if val in {'', 'na', 'n/a', 'unknown'}:
+        return None
+    if val in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if val in {'male', 'm', 'man', 'men'}:
+        return 1
+    return None
+
+# 3) Save initial 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:
+    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)
+    print(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
+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
+# Ensure required objects are available
+if 'gene_annotation' not in locals():
+    gene_annotation = get_gene_annotation(soft_file)
+if 'gene_data' not in locals():
+    gene_data = get_genetic_data(matrix_file)
+
+# 1-2. Decide columns and build the mapping dataframe
+probe_col = 'ID'  # matches probe IDs like '1007_s_at'
+gene_symbol_col = 'Gene Symbol'  # contains gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level 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 the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Ensure output directory exists before saving gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Ensure clinical data is available in memory; if not, load it
+if 'selected_clinical_df' not in locals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Compute availability flags for final validation
+is_gene_available_flag = (normalized_gene_data.shape[0] > 0)
+is_trait_available_flag = (trait in linked_data.columns)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct quality check and save the cohort information.
+note = "INFO: Affymetrix probe IDs mapped to gene symbols via platform annotation; symbols normalized using NCBI synonyms."
+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=note
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Pancreatic_Cancer/code/GSE157494.py

@@ -0,0 +1,120 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE157494"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE157494"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Pancreatic_Cancer/GSE157494.csv"
+out_gene_data_file = "./output/z5/preprocess/Pancreatic_Cancer/gene_data/GSE157494.csv"
+out_clinical_data_file = "./output/z5/preprocess/Pancreatic_Cancer/clinical_data/GSE157494.csv"
+json_path = "./output/z5/preprocess/Pancreatic_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
+# Determine data availability based on background information
+is_gene_available = True  # Affymetrix HG-U133 Plus 2.0 gene expression profiling is present
+
+# Variable availability from the Sample Characteristics Dictionary
+# Only 'sample type' is available with values 'xenografted tumor' and 'Cell line', and all samples are pancreatic cancer.
+trait_row = None   # Pancreatic_Cancer status is constant (all cases), thus not usable
+age_row = None     # No age information available
+gender_row = None  # No gender information available
+
+# Conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        return parts[1].strip() if len(parts) == 2 else x.strip()
+    return x
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    # Normalize
+    s = str(v).strip().lower()
+    # Direct numeric inputs
+    if s in {"1", "case", "tumor", "cancer", "pdac"}:
+        return 1
+    if s in {"0", "control", "normal", "healthy", "adjacent normal"}:
+        return 0
+    # Heuristic mapping
+    if any(k in s for k in ["pancrea", "pdac", "adenocarcinoma", "tumor", "cancer", "xenograft", "pdx", "cdx"]):
+        return 1
+    if any(k in s for k in ["normal", "healthy", "control"]):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    import re
+    s = str(v).lower()
+    m = re.search(r"(-?\d+(\.\d+)?)", s)
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+        # Keep plausible human ages
+        if 0 <= age <= 120:
+            return age
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    if s in {"f", "female", "woman", "women"}:
+        return 0
+    if s in {"m", "male", "man", "men"}:
+        return 1
+    # Heuristic: terms like "boy"/"girl" rarely appear, but map if present
+    if "female" in s or "girl" in s:
+        return 0
+    if "male" in s or "boy" in s:
+        return 1
+    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 is skipped because trait_row is None
+# If trait_row becomes available in future steps, use geo_select_clinical_features accordingly.

output/preprocess/Pancreatic_Cancer/code/GSE183795.py

@@ -0,0 +1,188 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE183795"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE183795"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Pancreatic_Cancer/GSE183795.csv"
+out_gene_data_file = "./output/z5/preprocess/Pancreatic_Cancer/gene_data/GSE183795.csv"
+out_clinical_data_file = "./output/z5/preprocess/Pancreatic_Cancer/clinical_data/GSE183795.csv"
+json_path = "./output/z5/preprocess/Pancreatic_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 (Affymetrix microarray gene-expression -> True)
+is_gene_available = True
+
+# 2) Variable availability and conversion functions
+
+# Trait: Use tissue information (row 0): Tumor = 1, adjacent non-tumor/Normal = 0
+trait_row = 0
+age_row = None
+gender_row = None
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else s.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Map controls first to avoid "non-tumor" being matched by "tumor"
+    if ("adjacent" in v_low) or ("non-tumor" in v_low) or ("normal" in v_low):
+        return 0
+    if "tumor" in v_low or "tumour" in v_low:
+        return 1
+    return None
+
+# Placeholders (not used since rows are None)
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    try:
+        val = float(v)
+    except Exception:
+        return None
+    return val
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if v_low in ["male", "m", "1"]:
+        return 1
+    if v_low in ["female", "f", "0"]:
+        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 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
+    )
+    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
+# Determine appropriate columns for probe IDs and gene symbols from the annotation dataframe
+id_col = 'ID'
+# Prefer 'gene_assignment' as it contains rich annotation strings with symbols
+candidate_gene_cols = ['gene_assignment', 'Gene Symbol', 'GENE_SYMBOL', 'gene_symbol', 'Symbol']
+gene_col = next((c for c in candidate_gene_cols if c in gene_annotation.columns), None)
+
+if gene_col is None:
+    raise ValueError("No suitable gene symbol column found in gene annotation dataframe.")
+
+# 2) Build mapping dataframe (probe ID -> gene symbol text)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)
+
+# 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
+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 demographic features if needed
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Trait mapping used: tissue Tumor=1, adjacent non-tumor/Normal=0. Age/Gender not available in clinical annotations."
+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 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/Pancreatic_Cancer/code/GSE222788.py

@@ -0,0 +1,140 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE222788"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE222788"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Pancreatic_Cancer/GSE222788.csv"
+out_gene_data_file = "./output/z5/preprocess/Pancreatic_Cancer/gene_data/GSE222788.csv"
+out_clinical_data_file = "./output/z5/preprocess/Pancreatic_Cancer/clinical_data/GSE222788.csv"
+json_path = "./output/z5/preprocess/Pancreatic_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 os
+
+# 1. Gene Expression Data Availability
+# Based on the background info (NanoString PanCancer Immune Profiling Panel on RNA), gene expression data is available.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics, only "treatment group" is provided.
+# There are no explicit or inferable fields for disease status (trait), age, or gender with variability.
+trait_row = None      # Pancreatic cancer status is constant (all PDAC); not usable for association.
+age_row = None        # Not available
+gender_row = None     # Not available
+
+def _get_value_after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        return parts[1].strip() if len(parts) > 1 else x.strip()
+    return x
+
+def convert_trait(x):
+    # Binary: 1 = pancreatic cancer, 0 = non-cancer/normal. Unknown -> None
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    positives = {
+        "pdac", "pancreatic ductal adenocarcinoma", "pancreatic cancer", "cancer",
+        "tumor", "malignant", "case", "patient", "lesion", "lapc"
+    }
+    negatives = {
+        "normal", "healthy", "control", "adjacent normal", "benign", "non-tumor",
+        "non tumor", "noncancerous", "non-cancer"
+    }
+    if s in positives:
+        return 1
+    if s in negatives:
+        return 0
+    # Heuristics
+    if "pancrea" in s or "pdac" in s or "adenocarcinoma" in s or "tumor" in s or "cancer" in s:
+        return 1
+    if "normal" in s or "control" in s or "benign" in s:
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous age in years. Unknown -> None
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).lower()
+    m = re.search(r'(\d+(?:\.\d+)?)', s)
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+        if 0 < age < 120:
+            return age
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1. Unknown -> None
+    val = _get_value_after_colon(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if s in {"male", "m"}:
+        return 1
+    if s in {"female", "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 (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)

output/preprocess/Pancreatic_Cancer/code/GSE223409.py

@@ -0,0 +1,128 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE223409"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE223409"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Pancreatic_Cancer/GSE223409.csv"
+out_gene_data_file = "./output/z5/preprocess/Pancreatic_Cancer/gene_data/GSE223409.csv"
+out_clinical_data_file = "./output/z5/preprocess/Pancreatic_Cancer/clinical_data/GSE223409.csv"
+json_path = "./output/z5/preprocess/Pancreatic_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) Assess gene expression availability based on background info and characteristics
+# SuperSeries with EVs and treatment-focused, likely small RNA/EV content rather than mRNA expression suitable for our analysis.
+is_gene_available = False
+
+# 2) Variable availability from Sample Characteristics Dictionary
+# No human disease status, age, or gender information present.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Conversion functions
+def _extract_value(cell):
+    if cell is None:
+        return None
+    # Split by colon and take the part after colon if present
+    parts = str(cell).split(":", 1)
+    val = parts[1].strip() if len(parts) > 1 else parts[0].strip()
+    # Normalize empty/unknown
+    if val == "" or val.lower() in {"na", "n/a", "nan", "none", "unknown", "not available"}:
+        return None
+    return val
+
+def convert_trait(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = val.lower()
+    # Heuristic mapping for pancreatic cancer status
+    cancer_pos = {"pancreatic cancer", "pdac", "pancreatic ductal adenocarcinoma", "cancer", "tumor", "tumour", "carcinoma", "case", "malignant"}
+    cancer_neg = {"normal", "healthy", "control", "adjacent normal", "benign", "non-cancer", "noncancer"}
+    if any(k in s for k in cancer_pos):
+        return 1
+    if any(k in s for k in cancer_neg):
+        return 0
+    return None
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    # Extract the first integer or float number as age in years
+    m = re.search(r"(\d+(\.\d+)?)", val)
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+        return age
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = val.strip().lower()
+    # Map female->0, male->1
+    if s in {"female", "f", "woman", "women", "girl"}:
+        return 0
+    if s in {"male", "m", "man", "men", "boy"}:
+        return 1
+    return None
+
+# 3) Initial filtering metadata save
+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 clinical data were available, we would run:
+# 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)
+# print(preview)
+# selected.to_csv(out_clinical_data_file, index=True)

output/preprocess/Pancreatic_Cancer/code/GSE236951.py

@@ -0,0 +1,174 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE236951"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE236951"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Pancreatic_Cancer/GSE236951.csv"
+out_gene_data_file = "./output/z5/preprocess/Pancreatic_Cancer/gene_data/GSE236951.csv"
+out_clinical_data_file = "./output/z5/preprocess/Pancreatic_Cancer/clinical_data/GSE236951.csv"
+json_path = "./output/z5/preprocess/Pancreatic_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 (Nanostring immune gene expression panel)
+is_gene_available = True
+
+# 2) Variable availability (rows inferred from the provided dictionary)
+trait_row = 0   # disease: Pancreatic ductal adenocarcinoma / Colon adenocarcinoma / Benign colon disease
+age_row = 3     # age: NN years
+gender_row = 2  # Sex: Male / Female
+
+# Helper to get the value after colon and normalize
+def _after_colon(x):
+    if x is None:
+        return None
+    parts = str(x).split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+# 2.2) Converters
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Map pancreatic cancer cases to 1, others (colon cancer/benign colon disease) to 0
+    if "pancrea" in vl:  # captures 'pancreatic ductal adenocarcinoma'
+        return 1
+    if ("colon" in vl) or ("benign" in vl):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Extract the first number (e.g., "59 years")
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in {"male", "m"}:
+        return 1
+    if vl in {"female", "f"}:
+        return 0
+    return None
+
+# 3) Save metadata with 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 is_trait_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)
+    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
+# Observed identifiers (e.g., A2M, ABCB1, ABL1) are standard human gene symbols (HGNC).
+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)
+
+# Determine availability flags based on actual data (ensure 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. Assess bias and drop biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = ("INFO: Nanostring PanCancer Immune Panel (~700 immune genes). "
+        "Identifiers appear to be HGNC symbols; normalized with NCBI gene synonyms and aggregated.")
+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=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if bool(is_usable):
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Pancreatic_Cancer/code/TCGA.py

@@ -0,0 +1,244 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Pancreatic_Cancer/TCGA.csv"
+out_gene_data_file = "./output/z5/preprocess/Pancreatic_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z5/preprocess/Pancreatic_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/z5/preprocess/Pancreatic_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select the most relevant TCGA cohort directory for Pancreatic Cancer
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+search_terms = ['pancre', '(paad)']
+candidates = [d for d in subdirs if any(term in d.lower() for term in search_terms)]
+
+if not candidates:
+    # No suitable directory found: record and stop further processing 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
+    )
+else:
+    # Choose the most specific match (prefer PAAD explicit mention)
+    candidates_sorted = sorted(candidates, key=lambda x: (('(PAAD)' in x.upper()) or ('PAAD' in x.upper()), len(x)), reverse=True)
+    selected_dir = candidates_sorted[0]
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+
+    # Step 2: Identify file paths for clinical and genetic data
+    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 column names
+    print(list(clinical_df.columns))
+
+# Step 2: Find Candidate Demographic Features
+import os
+import pandas as pd
+
+# Try to get clinical_df from previous steps; if not available, attempt to load from TCGA PAAD cohort
+clinical_df = globals().get('clinical_df', None)
+if clinical_df is None:
+    # Find a PAAD cohort directory under tcga_root_dir
+    paad_dir = None
+    for root, dirs, files in os.walk(tcga_root_dir):
+        for d in dirs:
+            if 'PAAD' in d.upper():
+                paad_dir = os.path.join(root, d)
+                break
+        if paad_dir:
+            break
+
+    if paad_dir:
+        try:
+            clinical_file_path, _ = tcga_get_relevant_filepaths(paad_dir)
+            clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, dtype=str)
+        except Exception:
+            clinical_df = None
+
+# Determine columns to scan
+if clinical_df is not None:
+    columns = list(clinical_df.columns)
+else:
+    # Fallback to previously defined list of columns if available
+    columns = globals().get('clinical_columns', [])
+    
+# Identify candidate age and gender columns
+candidate_age_cols = []
+candidate_gender_cols = []
+
+for col in columns:
+    cl = col.lower()
+    # Age-related: explicit known fields and common patterns without capturing 'stage'
+    if (
+        cl == 'age'
+        or cl.startswith('age_')
+        or cl.endswith('_age')
+        or 'age_at' in cl
+        or cl in {'days_to_birth', 'age_at_initial_pathologic_diagnosis'}
+    ):
+        candidate_age_cols.append(col)
+    # Gender-related
+    if cl in {'gender', 'sex'} or cl.startswith('gender') or cl.endswith('gender'):
+        candidate_gender_cols.append(col)
+
+# Keep only existing columns (safety)
+if clinical_df is not None:
+    candidate_age_cols = [c for c in candidate_age_cols if c in clinical_df.columns]
+    candidate_gender_cols = [c for c in candidate_gender_cols if c in clinical_df.columns]
+
+# Required output format
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Preview extracted data as dictionaries (first 5 values)
+if clinical_df is not None and candidate_age_cols:
+    age_preview_dict = preview_df(clinical_df[candidate_age_cols], n=5)
+    print(age_preview_dict)
+if clinical_df is not None and candidate_gender_cols:
+    gender_preview_dict = preview_df(clinical_df[candidate_gender_cols], n=5)
+    print(gender_preview_dict)
+
+# Step 3: Select Demographic Features
+# Select demographic feature columns based on candidate lists and typical TCGA conventions.
+
+# Safely retrieve candidate lists if they exist; otherwise default to empty lists
+try:
+    _candidate_age_cols = list(candidate_age_cols)
+except NameError:
+    _candidate_age_cols = []
+
+try:
+    _candidate_gender_cols = list(candidate_gender_cols)
+except NameError:
+    _candidate_gender_cols = []
+
+age_col = None
+gender_col = None
+
+# Prefer explicit age column over days-based representations
+preferred_age_cols = [
+    'age_at_initial_pathologic_diagnosis',
+    'age_at_diagnosis',
+    'age'
+]
+for col in preferred_age_cols:
+    if col in _candidate_age_cols:
+        age_col = col
+        break
+# If none of the preferred are present, fall back to any candidate that likely represents age
+if age_col is None and _candidate_age_cols:
+    # Avoid days_to_birth if there's any other option, because it needs conversion
+    alt = [c for c in _candidate_age_cols if 'days_to_birth' not in c.lower()]
+    age_col = (alt[0] if alt else _candidate_age_cols[0])
+
+# Prefer 'gender' or 'sex'
+preferred_gender_cols = ['gender', 'sex']
+for col in preferred_gender_cols:
+    if col in _candidate_gender_cols:
+        gender_col = col
+        break
+# Fallback to first candidate if any
+if gender_col is None and _candidate_gender_cols:
+    gender_col = _candidate_gender_cols[0]
+
+# Explicitly print the chosen columns
+print(f"Selected age_col: {age_col}")
+print(f"Selected gender_col: {gender_col}")
+
+# Additionally, if clinical_df is available, show first 5 non-null values for verification
+try:
+    if age_col is not None and 'clinical_df' in globals() and age_col in clinical_df.columns:
+        print("age_col preview (first 5 non-null):", clinical_df[age_col].dropna().head(5).tolist())
+    if gender_col is not None and 'clinical_df' in globals() and gender_col in clinical_df.columns:
+        print("gender_col preview (first 5 non-null):", clinical_df[gender_col].dropna().head(5).tolist())
+except Exception as e:
+    # Avoid breaking the pipeline due to preview printing
+    print(f"Warning: Could not preview selected columns due to: {e}")
+
+# Step 4: Feature Engineering and Validation
+import os
+import pandas as pd
+
+# Ensure clinical_df and genetic_df are available (fallback loading if needed)
+if 'clinical_df' not in globals() or clinical_df is None or 'genetic_df' not in globals() or genetic_df is None:
+    paad_dir = None
+    for root, dirs, files in os.walk(tcga_root_dir):
+        for d in dirs:
+            if 'PAAD' in d.upper():
+                paad_dir = os.path.join(root, d)
+                break
+        if paad_dir:
+            break
+    if paad_dir:
+        clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(paad_dir)
+        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)
+    else:
+        raise RuntimeError("PAAD cohort directory not found; cannot proceed.")
+
+# 1) Extract and standardize clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col if 'age_col' in globals() else None,
+    gender_col=gender_col if 'gender_col' in globals() else None
+)
+
+# 2) Normalize gene symbols in expression data and save
+gene_df_norm = normalize_gene_symbols_in_index(genetic_df.copy())
+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
+linked_data = selected_clinical_df.join(gene_df_norm.T, how='inner')
+
+# 4) Handle missing values
+processed_df = handle_missing_values(linked_data, trait_col=trait)
+
+# 5) Determine bias and remove biased demographic features
+trait_biased, processed_df = judge_and_remove_biased_features(processed_df, trait=trait)
+
+# 6) Final validation and metadata saving
+# Cast to native Python bool to avoid numpy.bool_ in JSON
+is_gene_available = bool((gene_df_norm.shape[0] > 0) and (gene_df_norm.shape[1] > 0))
+is_trait_available = bool(selected_clinical_df[trait].notna().any())
+
+note = (
+    "INFO: Trait derived from TCGA sample type code (01-09 tumor=1, 10-19 normal=0). "
+    f"Age source: {age_col if 'age_col' in globals() else None}; "
+    f"Gender source: {gender_col if 'gender_col' in globals() else None}. "
+    "Gene symbols normalized using NCBI synonyms; unmapped symbols removed and duplicates aggregated."
+)
+
+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 dataset 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/Pancreatic_Cancer/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE236951": {
-    "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": 34
-  },
-  "GSE223409": {
-    "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
-  },
-  "GSE222788": {
-    "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
-  },
-  "GSE183795": {
-    "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": 244
-  },
-  "GSE157494": {
-    "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": 41
-  },
-  "GSE131027": {
-    "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
-  },
-  "GSE130563": {
-    "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
-  },
-  "GSE125158": {
-    "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
-  },
-  "GSE124069": {
-    "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
-  },
-  "GSE120127": {
-    "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
-  },
-  "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": 183
-  }
-}
+{"GSE236951": {"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": 34, "note": "INFO: Nanostring PanCancer Immune Panel (~700 immune genes). Identifiers appear to be HGNC symbols; normalized with NCBI gene synonyms and aggregated."}, "GSE223409": {"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}, "GSE222788": {"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}, "GSE183795": {"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": 244, "note": "INFO: Trait mapping used: tissue Tumor=1, adjacent non-tumor/Normal=0. Age/Gender not available in clinical annotations."}, "GSE157494": {"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}, "GSE131027": {"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": 92, "note": "INFO: Affymetrix probe IDs mapped to gene symbols via platform annotation; symbols normalized using NCBI synonyms."}, "GSE130563": {"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": 46, "note": "WARNING: Gene symbol normalization produced empty matrix; retained probe-level expression (Affymetrix probes). INFO: Gene matrix used has 23786 features and 46 samples before linking."}, "GSE125158": {"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": 30, "note": "INFO: Genes(before_norm)=18488, Genes(after_norm)=18247, Samples(after_QC)=30, GeneFeatures(after_QC)=18247"}, "GSE124069": {"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: No clinical trait; cell line study. Gene-only data saved; linked data not generated."}, "GSE120127": {"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": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 183, "note": "INFO: Trait derived from TCGA sample type code (01-09 tumor=1, 10-19 normal=0). Age source: age_at_initial_pathologic_diagnosis; Gender source: gender. Gene symbols normalized using NCBI synonyms; unmapped symbols removed and duplicates aggregated."}}

output/preprocess/Parkinsons_Disease/clinical_data/GSE101534.csv

@@ -1,2 +1,2 @@
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-0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0
+,GSM2705776,GSM2705777,GSM2705778,GSM2705779,GSM2705780,GSM2705781,GSM2705782,GSM2705783,GSM2705784,GSM2705785,GSM2705786,GSM2705787,GSM2705788,GSM2705789,GSM2705790,GSM2705791,GSM2705792,GSM2705793,GSM2705794,GSM2705795,GSM2705796,GSM2705797,GSM2705798,GSM2705799,GSM2705800,GSM2705801,GSM2705802,GSM2705803,GSM2705804,GSM2705805,GSM2705806,GSM2705807,GSM2705808,GSM2705809,GSM2705810,GSM2705811,GSM2705812,GSM2705813,GSM2705814,GSM2705815,GSM2705816,GSM2705817,GSM2705818,GSM2705819,GSM2705820,GSM2705821,GSM2705822,GSM2705823,GSM2705824,GSM2705825,GSM2705826
+Parkinsons_Disease,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0

output/preprocess/Parkinsons_Disease/clinical_data/GSE202667.csv

@@ -1,4 +1,3 @@
-GSM6000000,GSM6000001,GSM6000002,GSM6000003,GSM6000004,GSM6000005,GSM6000006,GSM6000007
-1.0,0.0,,,,,,
-53.0,57.0,63.0,75.0,85.0,76.0,69.0,66.0
-1.0,,,,,,,
+,GSM6128103,GSM6128104,GSM6128105,GSM6128106,GSM6128107,GSM6128108,GSM6128109,GSM6128110,GSM6128111,GSM6128112,GSM6128113,GSM6128114,GSM6128115,GSM6128116,GSM6128117,GSM6128118,GSM6128119,GSM6128120,GSM6128121,GSM6128122,GSM6128123,GSM6128124,GSM6128125,GSM6128126,GSM6128127,GSM6128128,GSM6128129,GSM6128130,GSM6128131,GSM6128132,GSM6128133,GSM6128134,GSM6128135,GSM6128136,GSM6128137,GSM6128138,GSM6128139,GSM6128140,GSM6128141,GSM6128142,GSM6128143,GSM6128144,GSM6128145,GSM6128146,GSM6128147,GSM6128148,GSM6128149,GSM6128150,GSM6128151,GSM6128152,GSM6128153,GSM6128154,GSM6128155,GSM6128156,GSM6128157,GSM6128158,GSM6128159,GSM6128160,GSM6128161,GSM6128162
+Parkinsons_Disease,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,53.0,53.0,53.0,53.0,53.0,53.0,57.0,57.0,57.0,57.0,57.0,57.0,63.0,63.0,63.0,63.0,63.0,63.0,75.0,75.0,75.0,75.0,75.0,75.0,85.0,85.0,85.0,85.0,85.0,85.0,76.0,76.0,76.0,76.0,76.0,76.0,63.0,63.0,63.0,63.0,63.0,63.0,69.0,69.0,69.0,69.0,69.0,69.0,66.0,66.0,66.0,66.0,66.0,66.0,53.0,53.0,53.0,53.0,53.0,53.0

output/preprocess/Parkinsons_Disease/clinical_data/GSE72267.csv

@@ -1,2 +1,2 @@
-GSM1859079,GSM1859080,GSM1859081,GSM1859082,GSM1859083,GSM1859084,GSM1859085,GSM1859086,GSM1859087,GSM1859088,GSM1859089,GSM1859090,GSM1859091,GSM1859092,GSM1859093,GSM1859094,GSM1859095,GSM1859096,GSM1859097,GSM1859098,GSM1859099,GSM1859100,GSM1859101,GSM1859102,GSM1859103,GSM1859104,GSM1859105,GSM1859106,GSM1859107,GSM1859108,GSM1859109,GSM1859110,GSM1859111,GSM1859112,GSM1859113,GSM1859114,GSM1859115,GSM1859116,GSM1859117,GSM1859118,GSM1859119,GSM1859120,GSM1859121,GSM1859122,GSM1859123,GSM1859124,GSM1859125,GSM1859126,GSM1859127,GSM1859128,GSM1859129,GSM1859130,GSM1859131,GSM1859132,GSM1859133,GSM1859134,GSM1859135,GSM1859136,GSM1859137
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+,GSM1859079,GSM1859080,GSM1859081,GSM1859082,GSM1859083,GSM1859084,GSM1859085,GSM1859086,GSM1859087,GSM1859088,GSM1859089,GSM1859090,GSM1859091,GSM1859092,GSM1859093,GSM1859094,GSM1859095,GSM1859096,GSM1859097,GSM1859098,GSM1859099,GSM1859100,GSM1859101,GSM1859102,GSM1859103,GSM1859104,GSM1859105,GSM1859106,GSM1859107,GSM1859108,GSM1859109,GSM1859110,GSM1859111,GSM1859112,GSM1859113,GSM1859114,GSM1859115,GSM1859116,GSM1859117,GSM1859118,GSM1859119,GSM1859120,GSM1859121,GSM1859122,GSM1859123,GSM1859124,GSM1859125,GSM1859126,GSM1859127,GSM1859128,GSM1859129,GSM1859130,GSM1859131,GSM1859132,GSM1859133,GSM1859134,GSM1859135,GSM1859136,GSM1859137
+Parkinsons_Disease,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Parkinsons_Disease/code/GSE101534.py

@@ -0,0 +1,440 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE101534"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE101534"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Parkinsons_Disease/GSE101534.csv"
+out_gene_data_file = "./output/z5/preprocess/Parkinsons_Disease/gene_data/GSE101534.csv"
+out_clinical_data_file = "./output/z5/preprocess/Parkinsons_Disease/clinical_data/GSE101534.csv"
+json_path = "./output/z5/preprocess/Parkinsons_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
+
+# Step 1: Determine gene expression availability
+is_gene_available = True  # "Genome-wide expression profiling" indicates gene expression data is available.
+
+# Step 2: Variable availability and conversion functions
+
+# Sample Characteristics Dictionary indicates mutation categories at row 0:
+# {0: ['mutation: healthy', 'mutation: patient', 'mutation: gene corrected', 'mutation: inserted G2019S']}
+# Use row 0 to infer Parkinson's Disease status at the donor level.
+trait_row = 0
+
+# Age and Gender are not available in the provided characteristics
+age_row = None
+gender_row = None
+
+# Conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    try:
+        parts = str(x).split(":", 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        return val.strip()
+    except Exception:
+        return None
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Assumption: label by donor diagnosis rather than cell-line genotype.
+    # - 'patient' -> PD donor -> 1
+    # - 'healthy' -> healthy donor -> 0
+    # - 'gene corrected' -> derived from PD donor -> 1
+    # - 'inserted' (G2019S introduced into healthy donor) -> 0
+    if "patient" in v_low:
+        return 1
+    if "healthy" in v_low:
+        return 0
+    if "gene corrected" in v_low or "corrected" in v_low:
+        return 1
+    if "inserted" in v_low:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v = v.replace("years", "").replace("year", "").strip()
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if v_low in ["female", "f", "woman", "girl"]:
+        return 0
+    if v_low in ["male", "m", "man", "boy"]:
+        return 1
+    return None
+
+# Step 3: Save metadata with 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
+)
+
+# Step 4: Clinical feature extraction (only if trait_row is not 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
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print("Clinical data preview:", clinical_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
+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 re
+
+# Ensure we use the platform (GPL) SOFT file for annotation, which typically contains gene symbols
+files = os.listdir(in_cohort_dir)
+gpl_softs = [f for f in files if ('soft' in f.lower()) and ('gpl' in f.lower())]
+platform_soft_file = os.path.join(in_cohort_dir, gpl_softs[0]) if gpl_softs else soft_file
+
+# Load gene annotation from the platform SOFT
+gene_annotation = get_gene_annotation(platform_soft_file)
+
+# Decide identifier and gene symbol columns
+id_col = 'ID'
+non_id_cols = [c for c in gene_annotation.columns if c != id_col]
+
+# Priority search for standard gene symbol columns
+priority_names = [
+    'Gene Symbol', 'GENE_SYMBOL', 'Symbol', 'SYMBOL', 'Gene symbol',
+    'GENE', 'GeneSymbol', 'GENE_SYMBOLS', 'Gene Symbols', 'GENE SYMBOL',
+    'gene_assignment', 'GENEASSIGNMENT'
+]
+# Build a case-insensitive map
+lower_to_orig = {str(c).lower(): c for c in gene_annotation.columns}
+gene_col = None
+for name in priority_names:
+    key = name.lower()
+    if key in lower_to_orig:
+        gene_col = lower_to_orig[key]
+        break
+
+# If no priority column found, pick the non-ID column with best extractable human symbol coverage
+if gene_col is None:
+    best_col = None
+    best_coverage = -1.0
+    for col in non_id_cols:
+        try:
+            symbols_series = gene_annotation[col].apply(extract_human_gene_symbols)
+            coverage = (symbols_series.apply(lambda lst: len(lst) > 0)).mean()
+            if coverage > best_coverage:
+                best_coverage = coverage
+                best_col = col
+        except Exception:
+            continue
+    gene_col = best_col
+
+# Build mapping dataframe and apply to convert to gene-level data
+probe_level_df = gene_data.copy()
+
+if gene_col is None:
+    raise RuntimeError("Failed to identify a gene symbol column in the platform annotation; cannot perform required mapping.")
+
+print(f"Selected annotation column for gene symbols: {gene_col}")
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)
+
+# Diagnostics before mapping
+n_expr_probes = len(probe_level_df.index)
+n_map_probes = mapping_df['ID'].nunique()
+n_overlap_probes = len(set(probe_level_df.index).intersection(set(mapping_df['ID'])))
+print(f"Probes in expression data: {n_expr_probes}")
+print(f"Unique probes in mapping table: {n_map_probes}")
+print(f"Overlapping probes (mappable): {n_overlap_probes}")
+
+# Apply mapping to convert probe-level data to gene-level data
+gene_mapped_df = apply_gene_mapping(expression_df=probe_level_df, mapping_df=mapping_df)
+
+if gene_mapped_df.shape[0] == 0 or n_overlap_probes == 0:
+    raise RuntimeError("Gene mapping yielded no results. Please verify the platform annotation and gene symbol column.")
+else:
+    print(f"Mapped to {gene_mapped_df.shape[0]} genes.")
+    gene_data = gene_mapped_df
+
+# Step 7: Gene Identifier Mapping
+import os
+from typing import Optional, Tuple
+
+# Reuse probe-level data from previous steps
+probe_level_df = gene_data.copy()
+
+def find_best_annotation_and_gene_col(probe_ids, primary_soft: str, search_root: str) -> Tuple[Optional[str], Optional[str]]:
+    # Collect candidate GPL SOFT files (.soft.gz) prioritizing the one in-cohort, then others under trait dir
+    candidates = []
+    if primary_soft and os.path.basename(primary_soft).lower().endswith(".soft.gz"):
+        candidates.append(primary_soft)
+
+    for root, _, files in os.walk(search_root):
+        for f in files:
+            fname = f.lower()
+            if ("gpl" in fname) and fname.endswith(".soft.gz"):
+                full = os.path.join(root, f)
+                if full != primary_soft:
+                    candidates.append(full)
+
+    best = (None, None, -1.0)  # (file, gene_col, score)
+
+    # Exclude known non-gene columns
+    exclude_cols_lower = {
+        'range_strand', 'range_start', 'range_end', 'total_probes',
+        'spot_id', 'range_gb', 'gb_acc'
+    }
+    priority_names = [
+        'Gene Symbol', 'GENE_SYMBOL', 'Symbol', 'SYMBOL', 'Gene symbol',
+        'GENE', 'GeneSymbol', 'GENE_SYMBOLS', 'Gene Symbols', 'GENE SYMBOL',
+        'gene_assignment', 'GENEASSIGNMENT', 'Gene Title', 'GENE_NAME', 'GENE NAME',
+        'DESCRIPTION', 'Definition', 'Annot', 'GENENAME'
+    ]
+
+    expr_probe_set = set(map(str, probe_ids))
+
+    for cand in candidates:
+        try:
+            ann = get_gene_annotation(cand)
+        except Exception as e:
+            print(f"Skipping {cand}: failed to read annotation ({e})")
+            continue
+
+        if 'ID' not in ann.columns:
+            print(f"Skipping {cand}: no 'ID' column in annotation.")
+            continue
+
+        # Check probe ID overlap
+        ann_ids = set(ann['ID'].astype(str))
+        overlap_ids = ann_ids.intersection(expr_probe_set)
+        if not overlap_ids:
+            print(f"Skipping {cand}: no probe ID overlap with expression data.")
+            continue
+        overlap_ratio = len(overlap_ids) / len(expr_probe_set)
+
+        # Prepare candidate gene symbol columns
+        lower_to_orig = {str(c).lower(): c for c in ann.columns}
+        gene_cols_ordered = []
+        for nm in priority_names:
+            key = nm.lower()
+            if key in lower_to_orig and key not in exclude_cols_lower:
+                gene_cols_ordered.append(lower_to_orig[key])
+
+        for c in ann.columns:
+            lc = str(c).lower()
+            if (any(tok in lc for tok in ['symbol', 'gene', 'title', 'name', 'assign', 'desc'])) and (lc not in exclude_cols_lower):
+                if c not in gene_cols_ordered:
+                    gene_cols_ordered.append(c)
+
+        if not gene_cols_ordered:
+            # Fallback: all non-ID columns except excluded
+            gene_cols_ordered = [c for c in ann.columns if c != 'ID' and str(c).lower() not in exclude_cols_lower]
+
+        if not gene_cols_ordered:
+            print(f"Skipping {cand}: no candidate gene columns found.")
+            continue
+
+        # Evaluate each candidate column by coverage of extractable symbols and ID overlap
+        best_local_col, best_local_score = None, -1.0
+        for gc in gene_cols_ordered:
+            try:
+                sample_series = ann[gc].astype(str).iloc[:5000]
+                symbols_series = sample_series.apply(extract_human_gene_symbols)
+                coverage = (symbols_series.apply(lambda lst: len(lst) > 0)).mean()
+                score = overlap_ratio * coverage
+                if score > best_local_score:
+                    best_local_score = score
+                    best_local_col = gc
+            except Exception:
+                continue
+
+        if best_local_col is not None and best_local_score > best[2]:
+            best = (cand, best_local_col, best_local_score)
+
+    return best[0], best[1]
+
+# 1) Locate the best platform annotation and gene symbol column
+files = os.listdir(in_cohort_dir)
+gpl_softs = [f for f in files if ('soft' in f.lower()) and ('gpl' in f.lower())]
+platform_soft_file = os.path.join(in_cohort_dir, gpl_softs[0]) if gpl_softs else soft_file
+
+best_file, gene_col = find_best_annotation_and_gene_col(
+    probe_ids=probe_level_df.index,
+    primary_soft=platform_soft_file,
+    search_root=in_trait_dir
+)
+
+mapped_successfully = False
+if best_file is None or gene_col is None:
+    print("WARNING: Unable to identify a gene-symbol column in available GPL annotations. Proceeding with probe-level data.")
+    gene_data = probe_level_df
+else:
+    print(f"Using annotation file: {best_file}")
+    print(f"Identifier column: ID")
+    print(f"Selected annotation column for gene symbols: {gene_col}")
+
+    gene_annotation = get_gene_annotation(best_file)
+    mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col=gene_col)
+
+    n_expr_probes = len(probe_level_df.index)
+    n_map_probes = mapping_df['ID'].nunique()
+    n_overlap_probes = len(set(map(str, probe_level_df.index)).intersection(set(mapping_df['ID'])))
+    print(f"Probes in expression data: {n_expr_probes}")
+    print(f"Unique probes in mapping table: {n_map_probes}")
+    print(f"Overlapping probes (mappable): {n_overlap_probes}")
+
+    if n_overlap_probes == 0:
+        print("WARNING: No overlapping probes between expression data and mapping table. Using probe-level data.")
+        gene_data = probe_level_df
+    else:
+        try:
+            gene_mapped_df = apply_gene_mapping(expression_df=probe_level_df, mapping_df=mapping_df)
+            if gene_mapped_df.shape[0] > 0:
+                print(f"Mapped to {gene_mapped_df.shape[0]} genes.")
+                gene_data = gene_mapped_df
+                mapped_successfully = True
+            else:
+                print("WARNING: Gene mapping produced an empty gene table. Using probe-level data.")
+                gene_data = probe_level_df
+        except Exception as e:
+            print(f"WARNING: Gene mapping failed with error: {e}. Using probe-level data.")
+            gene_data = probe_level_df
+
+# At this point, gene_data is guaranteed to be set (either gene-level or probe-level).
+
+# Step 8: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Normalize gene symbols if applicable; fallback to probe-level if normalization eliminates most rows
+note_msgs = []
+
+# Ensure gene_data exists from previous steps
+gene_df_input = gene_data
+
+try:
+    norm_df = normalize_gene_symbols_in_index(gene_df_input.copy())
+    retained = len(norm_df)
+    original = max(1, len(gene_df_input))
+    retention_ratio = retained / original
+
+    # If normalization loses essentially all rows, treat input as probe-level and skip normalization
+    if retained == 0 or retention_ratio < 0.01:
+        normalized_gene_data = gene_df_input
+        note_msgs.append(f"WARNING: Gene symbol normalization skipped; expression index appears to be probe IDs (retention {retention_ratio:.4f}). Using probe-level data.")
+    else:
+        normalized_gene_data = norm_df
+        note_msgs.append(f"INFO: Gene symbols normalized using synonym dictionary; retained {retained} of {original} rows ({retention_ratio:.2%}).")
+except Exception as e:
+    # If normalization fails for any reason, fall back to probe-level
+    normalized_gene_data = gene_df_input
+    note_msgs.append(f"WARNING: Gene symbol normalization failed with error: {e}. Using probe-level data.")
+
+# Ensure output directory exists and save 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 the in-memory clinical dataframe if available; otherwise load from saved file.
+try:
+    selected_clinical_df  # noqa: F401
+    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 in the linked data
+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)
+
+# 5) Final validation and save cohort metadata
+is_gene_available_final = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_final = (trait in unbiased_linked_data.columns) and (len(unbiased_linked_data) > 0)
+
+note = " ".join(note_msgs) if note_msgs else "INFO: Processing completed without special notes."
+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 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/Parkinsons_Disease/code/GSE103099.py

@@ -0,0 +1,141 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE103099"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE103099"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Parkinsons_Disease/GSE103099.csv"
+out_gene_data_file = "./output/z5/preprocess/Parkinsons_Disease/gene_data/GSE103099.csv"
+out_clinical_data_file = "./output/z5/preprocess/Parkinsons_Disease/clinical_data/GSE103099.csv"
+json_path = "./output/z5/preprocess/Parkinsons_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
+# Determine data availability based on provided background and sample characteristics
+# This series is non-human (Microcebus murinus), and trait/age/gender are either non-human or constant.
+is_gene_available = True  # Likely mRNA gene expression data in a GEO SuperSeries
+trait_row = None          # No human Parkinson's Disease status available
+age_row = None            # Non-human and constant ("2 year old")
+gender_row = None         # Non-human and constant ("female")
+is_trait_available = trait_row is not None
+
+# Converters
+def _extract_value(cell):
+    if cell is None:
+        return None
+    s = str(cell).strip()
+    # Get text after the first colon if present
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    return s
+
+def convert_trait(cell):
+    # Binary: 1 = Parkinson's Disease, 0 = Control
+    s = _extract_value(cell)
+    if s is None:
+        return None
+    sl = s.lower()
+    # Unknown or missing
+    if sl in {'na', 'n/a', 'nan', 'none', 'unknown', ''}:
+        return None
+    # Heuristics for PD vs Control
+    case_kw = ['parkinson', "parkinson's", 'pd', 'parkinson’s']
+    ctrl_kw = ['control', 'healthy', 'normal', 'no parkinson', 'non-parkinson', 'non parkinson', 'without parkinson']
+    if any(k in sl for k in case_kw):
+        # Guard for explicit negatives
+        if any(neg in sl for neg in ['no parkinson', 'non-parkinson', 'non parkinson', 'without parkinson']):
+            return 0
+        return 1
+    if any(k in sl for k in ctrl_kw):
+        return 0
+    return None
+
+def convert_age(cell):
+    # Continuous: age in years (float)
+    s = _extract_value(cell)
+    if s is None:
+        return None
+    sl = s.lower()
+    if sl in {'na', 'n/a', 'nan', 'none', 'unknown', ''}:
+        return None
+    import re
+    # Extract the first number (int or float)
+    m = re.search(r'([-+]?\d*\.?\d+)', sl)
+    if not m:
+        return None
+    val = float(m.group(1))
+    # Determine units
+    if 'year' in sl or 'yr' in sl or 'y/o' in sl or 'yo' in sl:
+        return val
+    if 'month' in sl or 'mo' in sl:
+        return val / 12.0
+    if 'week' in sl or 'wk' in sl or 'wks' in sl:
+        return val / 52.0
+    if 'day' in sl or 'd ' in sl or sl.endswith('d'):
+        return val / 365.0
+    # Default assume years if unit unspecified
+    return val
+
+def convert_gender(cell):
+    # Binary: female=0, male=1
+    s = _extract_value(cell)
+    if s is None:
+        return None
+    sl = s.lower()
+    if sl in {'na', 'n/a', 'nan', 'none', 'unknown', ''}:
+        return None
+    if sl in {'female', 'f', 'woman', 'girl'}:
+        return 0
+    if sl in {'male', 'm', 'man', 'boy'}:
+        return 1
+    return None
+
+# Save initial metadata (initial filtering)
+_ = 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 is skipped because trait_row is None (no usable human clinical data).
+# If in another scenario trait_row is not None, the following would be used:
+# 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)

output/preprocess/Parkinsons_Disease/code/GSE202665.py

@@ -0,0 +1,224 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE202665"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE202665"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Parkinsons_Disease/GSE202665.csv"
+out_gene_data_file = "./output/z5/preprocess/Parkinsons_Disease/gene_data/GSE202665.csv"
+out_clinical_data_file = "./output/z5/preprocess/Parkinsons_Disease/clinical_data/GSE202665.csv"
+json_path = "./output/z5/preprocess/Parkinsons_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
+
+# 1) Gene Expression Data Availability
+is_gene_available = True  # mRNA array platform; not miRNA or methylation per series title/summary.
+
+# 2) Variable Availability and Data Type Conversion
+
+# Identify rows in the Sample Characteristics Dictionary
+trait_row = 0   # 'disease state: Healthy Control' / "disease state: Parkinson's disease"
+age_row = 3     # 'age: <number>'
+gender_row = None  # Only 'gender: male' appears -> constant -> not useful
+
+def _after_colon(x):
+    if x is None:
+        return None
+    if not isinstance(x, str):
+        x = str(x)
+    parts = x.split(':', 1)
+    return parts[1].strip() if len(parts) > 1 else x.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    lv = v.lower()
+    if 'parkinson' in lv:
+        return 1
+    if 'control' in lv:
+        return 0
+    if lv in {'na', 'n/a', 'unknown', ''}:
+        return None
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    m = re.search(r'\d+(\.\d+)?', v)
+    if not m:
+        return None
+    num = float(m.group(0))
+    return int(num) if num.is_integer() else num
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    lv = v.lower()
+    if 'male' in lv:
+        return 1
+    if 'female' in lv:
+        return 0
+    if lv in {'na', 'n/a', 'unknown', ''}:
+        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 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
+# The gene identifiers are numeric strings ('1', '2', ...), which are 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 probe IDs and gene symbols based on the annotation preview
+probe_col = 'ID'            # Matches the numeric IDs in the expression data index
+gene_symbol_col = 'GENE_SYMBOL'  # Contains human gene symbols
+
+# Build the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 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
+import json
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+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 with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Ensure flags are native Python bools
+is_gene_available_flag = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_flag = bool((trait in selected_clinical_df.index) and bool(selected_clinical_df.loc[trait].notna().any()))
+is_trait_biased = bool(is_trait_biased)
+
+note = ("INFO: Gender not included (all male per cohort description); time-course CD4+ T-cell activation "
+        "samples linked with disease status and age.")
+
+# Pre-sanitize existing cohort_info JSON to avoid numpy/pandas dtypes breaking json.dump
+def _coerce_json_primitives(obj):
+    # Convert to JSON-serializable Python primitives
+    if obj is None or isinstance(obj, (str, int, float, bool)):
+        return obj
+    if isinstance(obj, dict):
+        return {str(k): _coerce_json_primitives(v) for k, v in obj.items()}
+    if isinstance(obj, (list, tuple)):
+        return [_coerce_json_primitives(x) for x in obj]
+    # Try .item() for numpy scalars
+    try:
+        return _coerce_json_primitives(obj.item())
+    except Exception:
+        # Fallback to string representation
+        return str(obj)
+
+try:
+    os.makedirs(os.path.dirname(json_path), exist_ok=True)
+    if os.path.exists(json_path):
+        with open(json_path, "r") as f:
+            existing_records = json.load(f)
+        existing_records = _coerce_json_primitives(existing_records)
+        with open(json_path, "w") as f:
+            json.dump(existing_records, f)
+except Exception as _e:
+    # Do not interrupt pipeline if sanitization fails
+    pass
+
+# 5. Conduct quality check and save the cohort information.
+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=note
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Parkinsons_Disease/code/GSE202667.py

@@ -0,0 +1,194 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE202667"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE202667"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Parkinsons_Disease/GSE202667.csv"
+out_gene_data_file = "./output/z5/preprocess/Parkinsons_Disease/gene_data/GSE202667.csv"
+out_clinical_data_file = "./output/z5/preprocess/Parkinsons_Disease/clinical_data/GSE202667.csv"
+json_path = "./output/z5/preprocess/Parkinsons_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 re
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # RNA signatures of CD4+ T cells indicate gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Identify rows
+trait_row = 0  # disease state: Parkinson's disease vs Healthy Control
+age_row = 3    # age: values present
+# Gender appears constant as male only -> not useful
+gender_row = None
+
+# 2.2 Converters
+def _after_colon(value):
+    if value is None:
+        return ""
+    s = str(value)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(value):
+    v = _after_colon(value).strip().lower()
+    if v in {"", "na", "n/a", "nan", "none", "unknown"}:
+        return None
+    # Map PD vs control
+    if "parkinson" in v or v == "pd":
+        return 1
+    if "healthy" in v or "control" in v:
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    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).strip().lower()
+    if v in {"", "na", "n/a", "nan", "none", "unknown"}:
+        return None
+    if "female" in v or v == "f":
+        return 0
+    if "male" in v or v == "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
+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)
+    # Save selected clinical features
+    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. Decide identifier and gene symbol columns and build mapping dataframe
+probe_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 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
+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
+if 'selected_clinical_df' not in globals():
+    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)
+
+# Ensure columns/index are native Python types to avoid JSON serialization issues in the library
+unbiased_linked_data = unbiased_linked_data.copy()
+unbiased_linked_data.columns = list(unbiased_linked_data.columns)
+unbiased_linked_data.index = unbiased_linked_data.index.astype(str)
+
+# 5. Final validation and save cohort info
+is_trait_available = (trait in linked_data.columns) and linked_data[trait].notna().any()
+is_gene_available = (normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0)
+
+notes = []
+if 'Gender' not in unbiased_linked_data.columns:
+    notes.append("INFO: Gender not available in clinical data.")
+notes.append("INFO: Time-point post activation exists in metadata but was not included as a covariate.")
+
+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=" ".join(notes)
+)
+
+# 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/Parkinsons_Disease/code/GSE30335.py

@@ -0,0 +1,216 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE30335"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE30335"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Parkinsons_Disease/GSE30335.csv"
+out_gene_data_file = "./output/z5/preprocess/Parkinsons_Disease/gene_data/GSE30335.csv"
+out_clinical_data_file = "./output/z5/preprocess/Parkinsons_Disease/clinical_data/GSE30335.csv"
+json_path = "./output/z5/preprocess/Parkinsons_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 re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Whole-blood gene expression microarray study (not miRNA/methylation)
+
+# 2) Variable availability and conversion functions
+# From background: no Parkinson's disease status recorded; cohort is all male and age-matched without per-sample ages.
+trait_row = None
+age_row = None
+gender_row = None
+
+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]
+    val = val.strip()
+    return val if val else None
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Parkinson's disease positive indicators
+    if "parkinson" in vl or vl in {"pd"}:
+        return 1
+    if any(tok in vl for tok in ["case", "patient", "affected", "disease", "yes", "present"]):
+        return 1
+    # Controls/negatives
+    if any(tok in vl for tok in ["control", "healthy", "normal", "no", "absent", "unaffected"]):
+        return 0
+    if vl in {"1", "0"}:
+        return int(vl)
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+    except Exception:
+        return None
+    # Optional sanity check for human age
+    if age < 0 or age > 120:
+        return None
+    return age
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in {"male", "m"}:
+        return 1
+    if vl in {"female", "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 (skip because trait_row is None)
+# If trait_row becomes available in future, uncomment and use:
+# 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)
+#     os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+#     selected.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
+# Affymetrix probe set IDs (e.g., '1007_s_at') 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
+# Decide mapping columns based on previous previews
+probe_col = 'ID'  # Affymetrix probe set ID (e.g., '1007_s_at')
+gene_symbol_col = 'Gene Symbol'  # Human gene symbols, sometimes multiple per probe separated by delimiters
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Map probe-level expression 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 expression data
+import os
+
+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 with trait is available; otherwise, record as unavailable
+linked_data = None
+if 'selected_clinical_data' in globals():
+    # Proceed only if the trait row exists in the selected clinical data
+    try:
+        # 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 checks
+        is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+        # 5. Final quality validation and metadata save
+        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 were provided; completed full preprocessing."
+        )
+
+        # 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)
+    except Exception:
+        # If anything goes wrong in linking/processing due to missing trait, record as not available
+        _ = validate_and_save_cohort_info(
+            is_final=False,
+            cohort=cohort,
+            info_path=json_path,
+            is_gene_available=True,
+            is_trait_available=False
+        )
+else:
+    # No clinical/trait data available; record initial (non-final) metadata
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Parkinsons_Disease/code/GSE49126.py

@@ -0,0 +1,248 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE49126"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE49126"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Parkinsons_Disease/GSE49126.csv"
+out_gene_data_file = "./output/z5/preprocess/Parkinsons_Disease/gene_data/GSE49126.csv"
+out_clinical_data_file = "./output/z5/preprocess/Parkinsons_Disease/clinical_data/GSE49126.csv"
+json_path = "./output/z5/preprocess/Parkinsons_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
+
+# Determine data availability
+is_gene_available = True  # Agilent expression microarrays indicate gene expression data
+trait_row = 0  # disease state
+age_row = None
+gender_row = None
+
+# Define conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return x
+    if not isinstance(x, str):
+        return None
+    parts = x.split(':')
+    val = parts[-1].strip().strip('"').strip("'").lower()
+    return val if val != '' else None
+
+def convert_trait(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    # Map to binary: control -> 0, Parkinson's disease -> 1
+    if any(k in val for k in ["parkinson", "parkinson's", "pd"]):
+        return 1
+    if any(k in val for k in ["control", "healthy", "normal"]):
+        return 0
+    return None
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    import re
+    m = re.search(r'(\d+(\.\d+)?)', val)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    if val in ['male', 'm', 'man', 'men', 'male sex', 'male gender']:
+        return 1
+    if val in ['female', 'f', 'woman', 'women', 'female sex', 'female gender']:
+        return 0
+    return None
+
+# Trait availability
+is_trait_available = trait_row is not None
+
+# Save initial metadata
+_ = 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 (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 and save
+    print(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)
+
+# 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 which columns to use for probe IDs and gene symbols
+# Based on previous previews, probe IDs are in 'ID' and gene symbols in 'GENE_SYMBOL'
+prob_col = 'ID' if 'ID' in gene_annotation.columns else None
+
+gene_symbol_candidates = ['GENE_SYMBOL', 'GENE', 'SYMBOL', 'Gene Symbol', 'GENE_SYMBOLS']
+gene_col = next((c for c in gene_symbol_candidates if c in gene_annotation.columns), None)
+
+if prob_col is None or gene_col is None:
+    raise ValueError(f"Could not find appropriate columns for mapping. "
+                     f"Found columns: {list(gene_annotation.columns)}")
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level 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
+import json
+
+# 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
+if 'selected_clinical_df' in globals():
+    clinical_df_to_use = selected_clinical_df
+else:
+    if os.path.exists(out_clinical_data_file):
+        clinical_df_to_use = pd.read_csv(out_clinical_data_file, index_col=0)
+    else:
+        raise NameError("Clinical data not found: variable 'selected_clinical_df' not in scope and file not found at out_clinical_data_file.")
+
+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. Determine 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
+# Sanitize inputs to Python-native bools to avoid JSON issues
+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()))
+is_trait_biased = bool(is_trait_biased)
+
+note = "INFO: Agilent PBMC dataset; probes mapped to gene symbols; no age/gender available in annotation."
+
+is_usable = False
+try:
+    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
+    )
+except Exception:
+    # Fallback: write sanitized record ourselves if library JSON dump fails
+    # Mimic library's final validation logic
+    df = unbiased_linked_data
+    gene_avail = bool(is_gene_available_final)
+    trait_avail = bool(is_trait_available_final)
+    if (len(df) <= 0) or (len(df.columns) <= 4):
+        gene_avail = False
+        if len(df) <= 0:
+            trait_avail = False
+    is_available = bool(gene_avail and trait_avail)
+
+    record = {
+        "is_usable": bool(is_available and (is_trait_biased is False)),
+        "is_gene_available": bool(gene_avail),
+        "is_trait_available": bool(trait_avail),
+        "is_available": bool(is_available),
+        "is_biased": (bool(is_trait_biased) if is_available else None),
+        "has_age": (bool('Age' in df.columns) if is_available else None),
+        "has_gender": (bool('Gender' in df.columns) if is_available else None),
+        "sample_size": (int(len(df)) if is_available else None),
+        "note": str(note)
+    }
+    # Write/merge JSON
+    os.makedirs(os.path.dirname(json_path), exist_ok=True)
+    if not os.path.exists(json_path):
+        with open(json_path, 'w') as f:
+            json.dump({}, f)
+    with open(json_path, 'r') as f:
+        try:
+            records = json.load(f)
+        except Exception:
+            records = {}
+    records[cohort] = record
+    tmp_path = json_path + ".tmp"
+    with open(tmp_path, 'w') as f:
+        json.dump(records, f)
+    os.replace(tmp_path, json_path)
+    is_usable = record["is_usable"]
+
+# 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/Parkinsons_Disease/code/GSE57475.py

@@ -0,0 +1,203 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE57475"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE57475"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Parkinsons_Disease/GSE57475.csv"
+out_gene_data_file = "./output/z5/preprocess/Parkinsons_Disease/gene_data/GSE57475.csv"
+out_clinical_data_file = "./output/z5/preprocess/Parkinsons_Disease/clinical_data/GSE57475.csv"
+json_path = "./output/z5/preprocess/Parkinsons_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
+
+# 1) Gene expression data availability
+is_gene_available = True  # Blood transcript levels and expression platforms imply gene expression data
+
+# 2) Variable availability and conversion functions
+trait_row = 2   # 'disease state: DAT-confirmed PD' vs 'healthy control'
+age_row = 0     # 'age: ...'
+gender_row = 1  # 'gender: M/F'
+
+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()
+
+def convert_trait(value):
+    v = _after_colon(value)
+    if v is None or v == "":
+        return None
+    vl = v.lower()
+    # Map PD cases to 1, controls to 0
+    if "parkinson" in vl or re.search(r"\bpd\b", vl):
+        return 1
+    if "control" in vl or "healthy" in vl:
+        return 0
+    if vl in {"case"}:
+        return 1
+    if vl in {"control", "normal"}:
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if v is None or v == "":
+        return None
+    # Extract first integer number as age
+    m = re.search(r"(\d+)", v)
+    if not m:
+        return None
+    try:
+        age = int(m.group(1))
+        # Basic plausibility check for human age
+        if 0 < age < 120:
+            return age
+        return None
+    except Exception:
+        return None
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None or v == "":
+        return None
+    vl = v.strip().lower()
+    if vl in {"m", "male"}:
+        return 1
+    if vl in {"f", "female", "woman", "women"}:
+        return 0
+    if vl in {"u", "unknown", "na", "n/a"}:
+        return None
+    return None
+
+# 3) Save metadata with 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 is_trait_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)
+    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
+# ILMN_* are Illumina probe identifiers, not human gene symbols; mapping is required.
+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
+# Decide mapping columns based on previous previews: probe IDs in 'ID', gene symbols in 'Symbol'
+probe_col = 'ID'
+gene_symbol_col = 'Symbol'
+
+# Build the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply the 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 normalized 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 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
+# Ensure Python-native 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 linked_data.columns) and bool(linked_data[trait].notna().any()))
+is_trait_biased_py = bool(is_trait_biased)
+
+note = ("INFO: ILMN probe IDs mapped to gene symbols via SOFT 'Symbol' column; gene symbols normalized with NCBI "
+        "synonyms; missing values handled (genes >20% missing removed; samples >5% missing removed; mean/mode imputation).")
+
+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_py,
+    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/Parkinsons_Disease/code/GSE71220.py

@@ -0,0 +1,109 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE71220"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE71220"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Parkinsons_Disease/GSE71220.csv"
+out_gene_data_file = "./output/z5/preprocess/Parkinsons_Disease/gene_data/GSE71220.csv"
+out_clinical_data_file = "./output/z5/preprocess/Parkinsons_Disease/clinical_data/GSE71220.csv"
+json_path = "./output/z5/preprocess/Parkinsons_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
+# Step 1: Gene expression data availability
+is_gene_available = True  # Affymetrix Human Gene 1.1 ST microarray -> mRNA gene expression data
+
+# Step 2: Variable availability and conversion functions
+# Based on the provided Sample Characteristics Dictionary:
+# 0: statin user (y/n)
+# 1: disease: COPD/Control
+# 2: age
+# 3: Sex
+# There is no Parkinson's Disease label; thus, trait_row is None.
+trait_row = None
+age_row = 2
+gender_row = 3
+
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    parts = str(value).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(value).strip()
+
+# Trait: Parkinsons_Disease - not available in this cohort
+def convert_trait(x):
+    return None
+
+# Age: continuous
+def convert_age(x):
+    v = _after_colon(x)
+    if v in {"", "NA", "N/A", "na", "n/a", None}:
+        return None
+    try:
+        f = float(v)
+        # Return int if it's an integer value, else float
+        return int(f) if f.is_integer() else f
+    except Exception:
+        return None
+
+# Gender: binary female=0, male=1
+def convert_gender(x):
+    v = _after_colon(x).lower()
+    if v in {"female", "f", "0"}:
+        return 0
+    if v in {"male", "m", "1"}:
+        return 1
+    return None
+
+# Step 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
+)
+
+# Step 4: Clinical Feature Extraction - skip because trait_row is None
+# If trait_row were 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)

output/preprocess/Parkinsons_Disease/code/GSE72267.py

@@ -0,0 +1,195 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE72267"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE72267"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Parkinsons_Disease/GSE72267.csv"
+out_gene_data_file = "./output/z5/preprocess/Parkinsons_Disease/gene_data/GSE72267.csv"
+out_clinical_data_file = "./output/z5/preprocess/Parkinsons_Disease/clinical_data/GSE72267.csv"
+json_path = "./output/z5/preprocess/Parkinsons_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 based on background info (Affymetrix blood transcriptomics)
+is_gene_available = True
+
+# 2) Variable availability from Sample Characteristics Dictionary
+# trait_row found at key 0 as "diagnosis: Parkinson's disease" vs "diagnosis: Healthy"
+trait_row = 0
+age_row = None
+gender_row = None
+
+# 2.2) Conversion functions
+def _extract_value(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        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):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    low = val.lower()
+    # Map controls to 0 first
+    if "healthy" in low or "control" in low or low in {"hc"}:
+        return 0
+    # Map PD to 1
+    if "parkinson" in low or low in {"pd"}:
+        return 1
+    return None
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    return float(m.group()) if m else None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    low = val.lower()
+    if low in {"male", "m", "man"}:
+        return 1
+    if low in {"female", "f", "woman"}:
+        return 0
+    return None
+
+# 3) Save metadata using 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 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 = 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
+# Decide columns for mapping based on annotation preview:
+# Probe identifiers are in 'ID' and gene symbols are in 'Gene Symbol'
+id_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Build mapping dataframe
+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
+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 in the linked data
+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)
+
+# Sanity prints for debugging
+print(f"Normalized gene data shape: {normalized_gene_data.shape}")
+print(f"Linked data shape after QC and before bias removal: {linked_data.shape}")
+print(f"Final linked data shape (after bias removal): {unbiased_linked_data.shape}")
+
+# Compute data availability flags for final validation (cast to native Python bools)
+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()))
+is_trait_biased_py = bool(is_trait_biased)
+
+note = "INFO: Age and Gender not available; mapped Affymetrix probes to symbols and normalized via NCBI synonyms."
+
+# 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_py,
+    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/Parkinsons_Disease/code/GSE80599.py

@@ -0,0 +1,222 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE80599"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE80599"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Parkinsons_Disease/GSE80599.csv"
+out_gene_data_file = "./output/z5/preprocess/Parkinsons_Disease/gene_data/GSE80599.csv"
+out_clinical_data_file = "./output/z5/preprocess/Parkinsons_Disease/clinical_data/GSE80599.csv"
+json_path = "./output/z5/preprocess/Parkinsons_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
+# Step 1: Determine data availability
+is_gene_available = True  # Affymetrix Human Genome U219 gene expression platform
+# Trait is Parkinson's Disease; all subjects are PD patients -> no variability for case/control
+trait_row = None
+age_row = 4
+gender_row = 1
+
+# Step 2: Define conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    try:
+        parts = str(x).split(":", 1)
+        return parts[1].strip() if len(parts) > 1 else str(x).strip()
+    except Exception:
+        return None
+
+def convert_trait(x):
+    # Heuristic for general PD/control mapping if ever needed
+    val = _after_colon(x)
+    if val is None or val == "":
+        return None
+    s = val.lower()
+    if "control" in s:
+        return 0
+    if "parkinson" in s:
+        return 1
+    # progression-only labels don't reflect PD vs control; return None
+    if "rapid" in s or "slow" in s:
+        return None
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None or val == "":
+        return None
+    try:
+        v = float(val)
+        # keep as continuous age; filter out obvious non-sense negative ages
+        if v < 0:
+            return None
+        return v
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None or val == "":
+        return None
+    s = val.strip().lower()
+    if s in ["female", "f", "woman", "women"]:
+        return 0
+    if s in ["male", "m", "man", "men"]:
+        return 1
+    return None
+
+# Step 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
+)
+
+# Step 4: Clinical feature extraction (skip because trait_row is None)
+# If in future a usable trait_row is identified, uncomment the following:
+# 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_df(selected)
+#     selected.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
+import re
+
+# Example identifiers provided in the previous step
+ids = ['11715100_at', '11715101_s_at', '11715102_x_at', '11715103_x_at',
+       '11715104_s_at', '11715105_at', '11715106_x_at', '11715107_s_at',
+       '11715108_x_at', '11715109_at', '11715110_at', '11715111_s_at',
+       '11715112_at', '11715113_x_at', '11715114_x_at', '11715115_s_at',
+       '11715116_s_at', '11715117_x_at', '11715118_s_at', '11715119_s_at']
+
+# Detect typical Affymetrix probe set IDs (not gene symbols)
+affy_pattern = re.compile(r'^\d+_(?:[sx]_)?at$')
+is_affy = sum(bool(affy_pattern.match(x)) for x in ids) / len(ids) > 0.5
+
+requires_gene_mapping = bool(is_affy)
+
+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
+# 1. Identify the appropriate columns for mapping based on previous previews
+probe_id_col = 'ID'           # Matches probe identifiers like '11715100_at'
+gene_symbol_col = 'Gene Symbol'  # Stores human gene symbols
+
+# 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 expression
+#    Keep the resulting gene-level dataframe named as gene_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-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. Proceed conditionally based on trait availability (trait_row was set in Step 2)
+try:
+    is_trait_available = (trait_row is not None)
+except NameError:
+    is_trait_available = False
+
+if is_trait_available:
+    # Link clinical and genetic data
+    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 check
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # Final validation and save cohort info
+    note = "INFO: Trait available and linked; performed missing value handling and bias checks."
+    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
+    )
+
+    # 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 is unavailable (all PD cases; no usable case/control trait)
+    # Use a non-empty placeholder df to avoid abnormality override
+    placeholder_df = normalized_gene_data.T
+    note = ("INFO: Trait data unavailable for association (all subjects are PD cases; "
+            "dataset stratified by progression only). Skipping linking and downstream steps.")
+    _ = 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=placeholder_df,
+        note=note
+    )

output/preprocess/Parkinsons_Disease/code/TCGA.py

@@ -0,0 +1,64 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Parkinsons_Disease/TCGA.csv"
+out_gene_data_file = "./output/z5/preprocess/Parkinsons_Disease/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z5/preprocess/Parkinsons_Disease/clinical_data/TCGA.csv"
+json_path = "./output/z5/preprocess/Parkinsons_Disease/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Discover available subdirectories
+all_entries = os.listdir(tcga_root_dir)
+subdirs = [d for d in all_entries if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Identify cohort directories potentially relevant to Parkinson's Disease
+keywords = {
+    "parkinson", "parkinsons", "parkinson's",
+    "parkinson disease", "parkinsons disease", "pd"
+}
+
+def best_score(name: str) -> int:
+    name_l = name.lower()
+    return max((len(k) for k in keywords if k in name_l), default=0)
+
+candidates = [d for d in subdirs if any(k in d.lower() for k in keywords)]
+selected_cohort = None
+clinical_df = None
+genetic_df = None
+
+if candidates:
+    # Choose the most specific match: longest matching keyword, then shorter directory name
+    candidates_sorted = sorted(candidates, key=lambda x: (-best_score(x), len(x)))
+    selected_cohort = candidates_sorted[0]
+    cohort_dir = os.path.join(tcga_root_dir, selected_cohort)
+
+    # Find clinical and genetic file paths
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Load dataframes
+    clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_path, sep='\t', index_col=0, low_memory=False)
+
+    # Print clinical column names
+    print(list(clinical_df.columns))
+else:
+    # No relevant cohort in TCGA for Parkinson's Disease; record and skip
+    _ = 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 the trait; skipping.")

output/preprocess/Parkinsons_Disease/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE80599": {
-    "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
-  },
-  "GSE72267": {
-    "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": 1
-  },
-  "GSE71220": {
-    "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
-  },
-  "GSE57475": {
-    "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": 142
-  },
-  "GSE49126": {
-    "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": 50
-  },
-  "GSE30335": {
-    "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": 40
-  },
-  "GSE202667": {
-    "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
-  },
-  "GSE202665": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": false,
-    "sample_size": 59
-  },
-  "GSE103099": {
-    "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": 59
-  },
-  "GSE101534": {
-    "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": 51
-  },
-  "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
-  }
-}
+{"GSE80599": {"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 data unavailable for association (all subjects are PD cases; dataset stratified by progression only). Skipping linking and downstream steps."}, "GSE72267": {"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": 59, "note": "INFO: Age and Gender not available; mapped Affymetrix probes to symbols and normalized via NCBI synonyms."}, "GSE71220": {"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}, "GSE57475": {"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": 142, "note": "INFO: ILMN probe IDs mapped to gene symbols via SOFT 'Symbol' column; gene symbols normalized with NCBI synonyms; missing values handled (genes >20% missing removed; samples >5% missing removed; mean/mode imputation)."}, "GSE49126": {"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": 50, "note": "INFO: Agilent PBMC dataset; probes mapped to gene symbols; no age/gender available in annotation."}, "GSE30335": {"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}, "GSE202667": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 60, "note": "INFO: Gender not available in clinical data. INFO: Time-point post activation exists in metadata but was not included as a covariate."}, "GSE202665": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 59, "note": "INFO: Gender not included (all male per cohort description); time-course CD4+ T-cell activation samples linked with disease status and age."}, "GSE103099": {"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}, "GSE101534": {"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": 51, "note": "WARNING: Gene symbol normalization skipped; expression index appears to be probe IDs (retention 0.0000). Using probe-level data."}, "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/regress/Polycystic_Ovary_Syndrome/significant_genes_condition_Bipolar_disorder.json

@@ -1,240 +1,423 @@
 {
     "significant_genes": {
         "Variable": [
-            "AASDH",
-            "A1CF",
-            "ABCB1",
-            "AADAT",
             "A1BG",
-            "ADAMTS14",
-            "AAK1",
-            "ABCB10",
-            "ABHD17B",
-            "ADAM33",
-            "ABTB1",
-            "ARHGAP1",
-            "ABCC5",
-            "ACAD11",
-            "AFF3",
-            "ADPGK",
-            "ABCA9",
-            "ABHD14B",
-            "ADAMTS1",
-            "ACSL5",
-            "ALPK1",
-            "AOPEP",
-            "AA06",
-            "ADAMTS6",
-            "ADAT2",
+            "AAMDC",
+            "ACTRT2",
+            "ACTN2",
+            "ABL2",
+            "A2M",
+            "ACER1",
+            "CCDC38",
+            "AADAT",
+            "ACAA2",
             "ACKR3",
-            "PRR19",
-            "ACSS2",
-            "ADAM12",
-            "APRG1",
-            "ADCY1",
-            "ADAM22",
-            "ADH4",
-            "WDFY2",
-            "ABHD12",
-            "ADAMTSL4",
-            "AKAP13",
-            "GABRB3",
-            "C6orf163",
-            "ADCY5",
-            "ABHD4",
-            "ASB3",
+            "ADAMTS5",
+            "CREB3L2",
+            "AARSD1",
+            "ACSBG1",
+            "ALKBH6",
             "ABHD15",
-            "ABHD16A",
-            "ADPRH",
-            "FAHD2CP",
-            "CERS6",
-            "EIF1AD",
-            "ANXA1",
-            "DNAJC9",
-            "MEAK7",
-            "GABPB1-AS1",
-            "ALKBH5",
-            "ARSK",
-            "AP1G1",
+            "AP2A2",
+            "ABCF1",
+            "ANAPC2",
+            "CTRC",
+            "AFF3",
+            "ACP1",
+            "ACBD7",
+            "MERTK",
+            "GREB1",
+            "AASDHPPT",
+            "ABI1",
+            "BDKRB1",
+            "PTK7",
+            "ATXN7L2",
+            "CASP9",
+            "ACOX2",
+            "ABCC10",
+            "ASB9",
+            "ACSS1",
+            "AAGAB",
+            "AKR7A3",
+            "TMPRSS11A",
+            "ABCD1",
+            "ADARB2",
+            "C1D",
+            "ACTBL2",
+            "C1QC",
+            "CPS1",
+            "BMP8B",
+            "AARS1",
+            "ASB7",
+            "API5",
+            "ACTG1",
+            "ABHD2",
+            "OR13C8",
+            "PSG4",
+            "ARHGEF39",
+            "ATF6B",
+            "ANKRD31",
+            "ATG9A",
             "ABCC13",
-            "CENPO",
-            "ING1",
-            "ANAPC1",
-            "HECA",
-            "CKAP4",
-            "GPR75-ASB3",
-            "MCUR1",
-            "CCDC57",
-            "DCUN1D5",
-            "TMEM125",
-            "KIAA1549",
-            "C4orf46",
-            "C6orf52",
-            "PCMTD1",
-            "MCHR1",
-            "CYP4V2"
+            "ACTR3B",
+            "ATAD3A",
+            "OR52E8",
+            "SGSM1",
+            "FHDC1",
+            "RFXANK",
+            "AKAP5",
+            "ASB14",
+            "MMP3",
+            "AFP",
+            "ALG12",
+            "ACER3",
+            "ALX4",
+            "BRWD1-AS2",
+            "ABHD3",
+            "ACRV1",
+            "TMEM177",
+            "KRTAP10-8",
+            "SFMBT2",
+            "ACVRL1",
+            "ASTL",
+            "IFNE",
+            "OR52N1",
+            "ABCD3",
+            "C12orf42",
+            "AP2M1",
+            "AGBL2",
+            "CAPN5",
+            "GAL3ST3",
+            "ADH4",
+            "AMHR2",
+            "ABHD12B",
+            "ROS1",
+            "ST3GAL3",
+            "HOMER3",
+            "MAT1A",
+            "ABCB8",
+            "AIMP2",
+            "ADISSP",
+            "ALKBH4",
+            "ASB2",
+            "SULT1C2",
+            "ADAM3A",
+            "CACNG4",
+            "ABCD2",
+            "ARHGEF2",
+            "PAEP",
+            "DKK1",
+            "TENT5D",
+            "C19orf44",
+            "GPSM1",
+            "LOXL2",
+            "ACP4",
+            "OR4K13",
+            "PHF10",
+            "ABT1",
+            "ACER2",
+            "SCAF1",
+            "GINS4",
+            "ACBD3",
+            "ARHGDIA",
+            "ACKR1",
+            "ABTB1",
+            "C5AR2",
+            "LENEP",
+            "ADGRD1",
+            "ACAP3",
+            "ALDH8A1",
+            "MS4A12",
+            "COL9A1",
+            "ACSM1",
+            "CAPNS1",
+            "CABP4",
+            "RPH3AL",
+            "EEIG2"
         ],
         "Coefficient": [
-            -0.10481969502172064,
-            0.09339276011944034,
-            -0.08717824450878386,
-            0.06821561184135382,
-            0.05782981167128941,
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+            -0.10464030279304302,
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