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@@ -2200,3 +2200,6 @@ output/preprocess/Parkinsons_Disease/GSE101534.csv filter=lfs diff=lfs merge=lfs
 output/preprocess/Bladder_Cancer/GSE138118.csv filter=lfs diff=lfs merge=lfs -text
 output/preprocess/Liver_Cancer/gene_data/GSE218438.csv filter=lfs diff=lfs merge=lfs -text
 output/preprocess/Parkinsons_Disease/gene_data/GSE101534.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Chronic_kidney_disease/GSE66494.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Chronic_kidney_disease/gene_data/GSE180393.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Bipolar_disorder/GSE62191.csv filter=lfs diff=lfs merge=lfs -text

output/preprocess/Acute_Myeloid_Leukemia/code/GSE121291.py

@@ -0,0 +1,128 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE121291"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE121291"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/GSE121291.csv"
+out_gene_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE121291.csv"
+out_clinical_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE121291.csv"
+json_path = "./output/z1/preprocess/Acute_Myeloid_Leukemia/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 (microarray mRNA -> gene expression data present)
+is_gene_available = True
+
+# 2) Variable availability
+# Based on the sample characteristics, all samples are AML THP-1 cell line with varying agents/time.
+# Trait (Acute_Myeloid_Leukemia) is constant; age and gender are not provided for the cell line context.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Conversion functions
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) == 2 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    v = _extract_after_colon(x)
+    if not v:
+        return None
+    v_low = v.lower()
+    # Map AML to 1, healthy/normal/control to 0 where applicable
+    if "acute myeloid leukemia" in v_low or "aml" in v_low:
+        return 1
+    if any(k in v_low for k in ["healthy", "normal", "control", "non-aml", "no disease", "disease free"]):
+        return 0
+    # If clearly other leukemia types, map to disease=1 as well
+    if any(k in v_low for k in ["leukemia", "cancer", "tumor", "malignant"]):
+        return 1
+    return None
+
+def convert_age(x):
+    v = _extract_after_colon(x)
+    if not v:
+        return None
+    # Extract first number as age in years if plausible
+    m = re.search(r"(\d+(\.\d+)*)", v)
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+    except Exception:
+        return None
+    # Keep plausible human ages
+    if 0 < age < 120:
+        return age
+    return None
+
+def convert_gender(x):
+    v = _extract_after_colon(x)
+    if not v:
+        return None
+    v_low = v.lower()
+    if v_low in ["female", "f", "woman", "women"]:
+        return 0
+    if v_low in ["male", "m", "man", "men"]:
+        return 1
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_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)

output/preprocess/Acute_Myeloid_Leukemia/code/GSE121431.py

@@ -0,0 +1,120 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE121431"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE121431"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/GSE121431.csv"
+out_gene_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE121431.csv"
+out_clinical_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE121431.csv"
+json_path = "./output/z1/preprocess/Acute_Myeloid_Leukemia/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:
+# - This is a cell-line perturbation study (THP-1), not human subjects.
+# - 'disease state' is uniformly Acute Myeloid Leukemia (constant), so trait is not usable for association.
+# - Age and gender are not provided for cell lines.
+
+is_gene_available = True  # Likely gene expression data (mRNA) in GEO series; not miRNA/methylation-only.
+trait_row = None          # Trait is constant (all AML) and from cell line model; not usable.
+age_row = None            # No age for cell lines.
+gender_row = None         # No gender for cell lines.
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) == 2 else parts[0]
+    val = val.strip()
+    return val if val != "" else None
+
+def convert_trait(x):
+    # Not used because trait_row is None; kept for interface completeness.
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Map AML presence to 1 if ever used; otherwise None for unknown/other.
+    if v_low in {"acute myeloid leukemia", "aml"}:
+        return 1
+    return None
+
+def convert_age(x):
+    # Not used; provided for interface completeness.
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Extract first numeric token as age
+    import re
+    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(x):
+    # Not used; provided for interface completeness.
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    if v_low in {"female", "f", "woman", "women"}:
+        return 0
+    if v_low in {"male", "m", "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 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
+    )
+    clinical_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/Acute_Myeloid_Leukemia/code/GSE161532.py

@@ -0,0 +1,314 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE161532"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE161532"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/GSE161532.csv"
+out_gene_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE161532.csv"
+out_clinical_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE161532.csv"
+json_path = "./output/z1/preprocess/Acute_Myeloid_Leukemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Affymetrix Human Transcriptome Array 2.0 indicates mRNA expression data.
+
+# 2) Variable availability
+# Trait (Acute Myeloid Leukemia) is constant across all samples -> not available for association as a varying trait
+trait_row = None
+
+# Age and Gender are available
+age_row = 1
+gender_row = 2
+
+# 2.2) Data type conversion functions
+def _extract_value_after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    # Binary: 1 = AML, 0 = non-AML; return None if unknown
+    val = _extract_value_after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"", "na", "n/a", "not available", "unknown"}:
+        return None
+    # Positive indicators
+    if "aml" in v or "acute myeloid leukemia" in v:
+        return 1
+    # Negative indicators
+    negatives = ["control", "healthy", "normal", "non-aml", "no aml", "donor", "non leukemia", "non-leukemia", "remission"]
+    if any(neg in v for neg in negatives):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous age in years; return None if not parseable
+    val = _extract_value_after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"", "na", "n/a", "not available", "unknown"}:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+        if 0 <= age <= 120:
+            return age
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female=0, male=1
+    val = _extract_value_after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"", "na", "n/a", "not available", "unknown"}:
+        return None
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (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
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    # Save clinical features
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import re
+
+# Keep original expression matrix for diagnostics
+expr_df = gene_data
+expr_ids = set(expr_df.index.astype(str).str.strip())
+
+# Heuristic for expression ID format typically seen on Affymetrix ST arrays
+expr_id_regex = re.compile(r'^\d+_(?:st|at|s_at|x_at)$', re.IGNORECASE)
+
+def tokens_from_string(s: str):
+    # Extract alphanumeric tokens commonly used in GEO/SOFT fields
+    return re.findall(r'[A-Za-z0-9_.-]+', str(s))
+
+# 1) Find the annotation column with real overlap to expression IDs
+col_overlaps = {}
+for col in gene_annotation.columns:
+    series = gene_annotation[col].dropna().astype(str)
+    hits = set()
+    for v in series:
+        for t in tokens_from_string(v):
+            if t in expr_ids:
+                hits.add(t)
+    col_overlaps[col] = len(hits)
+
+# Select best ID column based on exact overlap with expression IDs
+best_id_col, best_overlap = max(col_overlaps.items(), key=lambda kv: kv[1])
+
+if best_overlap == 0:
+    # No usable overlap found; print diagnostics and stop early to avoid silent empty mapping
+    print("No overlap between expression IDs and any annotation column.")
+    print("Top columns by regex-like token matches (diagnostics):")
+    regex_like_counts = {}
+    for col in gene_annotation.columns:
+        series = gene_annotation[col].dropna().astype(str)
+        cnt = 0
+        for v in series:
+            for t in tokens_from_string(v):
+                if expr_id_regex.match(t):
+                    cnt += 1
+        regex_like_counts[col] = cnt
+    for col, cnt in sorted(regex_like_counts.items(), key=lambda kv: kv[1], reverse=True)[:10]:
+        print(f"  {col}: {cnt} regex-like tokens")
+    raise ValueError("Failed to find a probe ID column matching expression IDs (e.g., '2824546_st').")
+
+# 2) Choose gene symbol column (prefer rich annotation fields)
+candidate_gene_cols = [c for c in ['gene_assignment', 'mrna_assignment', 'gene_symbol', 'symbol', 'Gene Symbol']
+                       if c in gene_annotation.columns]
+if candidate_gene_cols:
+    def score_gene_col(col, n=500):
+        sample_vals = gene_annotation[col].dropna().astype(str).head(n)
+        return sum(len(extract_human_gene_symbols(v)) for v in sample_vals)
+    scores = {col: score_gene_col(col) for col in candidate_gene_cols}
+    best_gene_col = max(scores, key=scores.get)
+else:
+    best_gene_col = gene_annotation.columns[-1]
+
+print(f"Selected ID column: {best_id_col} with {best_overlap} overlapping IDs.")
+print(f"Selected gene column: {best_gene_col}")
+
+# 3) Build mapping and sanitize IDs to ensure they match expression IDs
+mapping_df = get_gene_mapping(gene_annotation, prob_col=best_id_col, gene_col=best_gene_col)
+
+def pick_expr_id_token(x):
+    toks = tokens_from_string(x)
+    # Prefer exact match against expr_ids
+    for t in toks:
+        if t in expr_ids:
+            return t
+    # As a fallback, pick the first token that looks like an expression ID
+    for t in toks:
+        if expr_id_regex.match(t):
+            return t
+    return None
+
+mapping_df['ID'] = mapping_df['ID'].astype(str).map(pick_expr_id_token)
+mapping_df = mapping_df.dropna(subset=['ID'])
+mapping_df['ID'] = mapping_df['ID'].astype(str)
+mapping_df = mapping_df.drop_duplicates(subset=['ID', 'Gene'])
+
+mapped_probes = mapping_df['ID'].nunique()
+print(f"Number of mapped probes: {mapped_probes} out of {len(expr_ids)} ({mapped_probes/len(expr_ids):.2%})")
+
+if mapped_probes == 0:
+    raise ValueError("Mapping resulted in zero mapped probes. Aborting to avoid empty gene matrix.")
+
+# Convert to gene-level expression
+gene_data = apply_gene_mapping(expression_df=expr_df, mapping_df=mapping_df)
+
+# Sanity check on resulting gene-level data
+if gene_data.shape[0] == 0 or gene_data.shape[1] == 0:
+    raise ValueError(f"Gene-level expression matrix is empty after mapping. Shape: {gene_data.shape}")
+
+print(f"Gene-level matrix shape: {gene_data.shape}")
+
+# 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)
+
+# Determine trait availability from previous steps
+is_trait_available = (trait_row is not None)
+
+if is_trait_available:
+    # Build clinical features if trait is available
+    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
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Bias check and remove biased covariates
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    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: Trait and clinical features were available and processed."
+    )
+
+    # 6. Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # Trait is not available (constant across samples), skip linking and downstream steps
+    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,
+        note="INFO: Trait is constant/unavailable for this cohort; only gene data was processed and saved."
+    )
+    # Do not save out_data_file since the dataset is not usable for trait association

output/preprocess/Acute_Myeloid_Leukemia/code/GSE222124.py

@@ -0,0 +1,213 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE222124"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE222124"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/GSE222124.csv"
+out_gene_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE222124.csv"
+out_clinical_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE222124.csv"
+json_path = "./output/z1/preprocess/Acute_Myeloid_Leukemia/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 json
+import pandas as pd
+
+# 1) Gene expression data availability (based on series title/summary)
+is_gene_available = True
+
+# 2) Variable availability and converters
+
+# Based on the sample characteristics:
+# 0: 'cell type: T cell leukemia' | 'cell type: Acute monocytic leukemia monocyte' | 'cell type: Natural killer cell leukemia'
+# Infer AML status from 'cell type' field: Acute monocytic leukemia -> AML (1); others -> non-AML (0)
+trait_row = 0
+age_row = None
+gender_row = None
+
+def _extract_value(x: str) -> str:
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return ""
+    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).lower()
+    if v in ("", "na", "n/a", "unknown", "not available", "none"):
+        return None
+    # Positive mappings for AML
+    aml_patterns = [
+        "acute myeloid leukemia",
+        "acute myelogenous leukemia",
+        "acute myeloid leukaemia",
+        "acute monocytic leukemia",
+        "aml",
+    ]
+    if any(p in v for p in aml_patterns):
+        return 1
+    # Known non-AML leukemias in this dataset
+    non_aml_patterns = [
+        "t cell leukemia",
+        "natural killer cell leukemia",
+    ]
+    if any(p in v for p in non_aml_patterns):
+        return 0
+    # Heuristic: if it contains 'leukemia' but no AML hint, classify as non-AML
+    if "leukemia" in v:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x).lower()
+    if v in ("", "na", "n/a", "unknown", "not available", "none"):
+        return None
+    m = re.search(r"(-?\d+(\.\d+)?)", v)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _extract_value(x).strip().lower()
+    if v in ("", "na", "n/a", "unknown", "not available", "none"):
+        return None
+    if v in ("female", "f", "woman", "girl"):
+        return 0
+    if v in ("male", "m", "man", "boy"):
+        return 1
+    return None
+
+# is_trait_available determined by trait_row presence
+is_trait_available = trait_row is not None
+
+# 3) Initial filtering and save 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
+)
+
+# 4) Clinical feature extraction (only if clinical data available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the correct columns for probe IDs and gene symbols
+probe_col = 'ID'
+gene_symbol_col = 'Gene 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
+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
+# Use the correct clinical dataframe variable; load from disk if not in scope
+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. Assess bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = ("INFO: Cell-line study (Jurkat/THP1/KHYG-1) treated with glycodelin; trait inferred from cell type; "
+        "no age/gender 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/Acute_Myeloid_Leukemia/code/GSE222169.py

@@ -0,0 +1,129 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE222169"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE222169"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/GSE222169.csv"
+out_gene_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE222169.csv"
+out_clinical_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE222169.csv"
+json_path = "./output/z1/preprocess/Acute_Myeloid_Leukemia/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: Dataset Analysis and Clinical Feature Extraction for GSE222169
+
+import re
+import pandas as pd
+
+# 1) Gene Expression Data Availability
+# Based on GEO series context and characteristics (genotype knockdowns/overexpression; leukemia cell lines/patient AML),
+# this series is likely RNA-seq/gene expression rather than miRNA-only or methylation-only.
+is_gene_available = True
+
+# 2) Variable Availability and Data Type Conversion
+
+# From the sample characteristics:
+# {0: ['tissue source: patient with AML', 'cell line: MOLM-14', 'cell line: OCI-AML2'],
+#  1: ['cell type: leukemia cell line', 'genotype: shCTL', 'genotype: shOPA1', 'genotype: shMFN2', 'genotype: OE_EMPTY', 'genotype: OE_MFN2'],
+#  2: ['treatment: shCTL_72h', 'treatment: shMFN2_72h']}
+#
+# All entries indicate AML disease context (patient with AML or AML-derived leukemia cell lines),
+# with no healthy/control samples. Thus, trait (AML status) is constant and not suitable for association analysis.
+trait_row = None
+age_row = None     # No age information observed
+gender_row = None  # No gender information observed
+
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(x).strip()
+
+def convert_trait(x):
+    # Binary mapping for AML status if ever needed: AML/leukemia -> 1; healthy/control/normal -> 0
+    val = _after_colon(x).lower()
+    if val == "":
+        return None
+    positives = [
+        "acute myeloid leukemia", "aml", "leukemia", "leukemia cell line", "molm-14", "oci-aml2", "patient with aml"
+    ]
+    negatives = ["healthy", "control", "normal", "non-aml", "no leukemia"]
+    if any(p in val for p in positives):
+        return 1
+    if any(n in val for n in negatives):
+        return 0
+    return None
+
+def convert_age(x):
+    # Extract first numeric value as age in years; otherwise None
+    val = _after_colon(x)
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    # Female -> 0; Male -> 1
+    val = _after_colon(x).strip().lower()
+    if val in ["female", "f", "woman", "women", "girl"]:
+        return 0
+    if val in ["male", "m", "man", "men", "boy"]:
+        return 1
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (skip because trait_row is None)
+if (trait_row is not None) and ('clinical_data' in globals()):
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print(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)

output/preprocess/Acute_Myeloid_Leukemia/code/GSE222616.py

@@ -0,0 +1,113 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE222616"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE222616"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/GSE222616.csv"
+out_gene_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE222616.csv"
+out_clinical_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE222616.csv"
+json_path = "./output/z1/preprocess/Acute_Myeloid_Leukemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import pandas as pd
+
+# 1) Gene expression availability based on background (Affymetrix HuGene 1.0 ST arrays => mRNA expression)
+is_gene_available = True
+
+# 2) Variable availability assessment from Sample Characteristics:
+# All samples are HL-60 AML cell line with various treatments; no human subject variability in trait/age/gender.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Converters (defined for interface completeness; not used since corresponding rows are None)
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(x).strip()
+
+def convert_trait(x):
+    val = _after_colon(x).lower()
+    # Map common case/control words; here not applicable, but kept for robustness
+    if val in {"aml", "acute myeloid leukemia", "acute myeloid leukaemia"}:
+        return 1
+    if val in {"control", "healthy", "normal"}:
+        return 0
+    # Heuristic for treatment/control context if ever encountered
+    if "wt" in val or "control" in val or "untreated" in val:
+        return 0
+    if "atra" in val or "gsk343" in val or "hkmti" in val or "ezh2" in val:
+        return 1
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    nums = re.findall(r"\d+\.?\d*", val)
+    if nums:
+        try:
+            return float(nums[0])
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    val = _after_colon(x).lower()
+    if val in {"f", "female", "woman", "women"}:
+        return 0
+    if val in {"m", "male", "man", "men"}:
+        return 1
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# if trait_row is not None:
+#     selected_clinical = geo_select_clinical_features(
+#         clinical_df=clinical_data,
+#         trait=trait,
+#         trait_row=trait_row,
+#         convert_trait=convert_trait,
+#         age_row=age_row,
+#         convert_age=convert_age 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_df(selected_clinical, n=5)
+#     selected_clinical.to_csv(out_clinical_data_file)

output/preprocess/Acute_Myeloid_Leukemia/code/GSE235070.py

@@ -0,0 +1,130 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE235070"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE235070"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/GSE235070.csv"
+out_gene_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE235070.csv"
+out_clinical_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE235070.csv"
+json_path = "./output/z1/preprocess/Acute_Myeloid_Leukemia/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
+from typing import Optional, Any
+
+# 1) Assess gene expression availability (SuperSeries; characteristics suggest constant AML; no clear gene matrix here)
+is_gene_available = False
+
+# 2) Variable availability and conversion functions
+
+# Based on the provided Sample Characteristics Dictionary:
+# {0: ['disease state: patient with AML']}
+# The trait is constant (all AML), thus not usable. Age and gender are not present.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _after_colon(x: Any) -> str:
+    if x is None:
+        return ""
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) == 2 else parts[0]
+    return val.strip()
+
+def convert_trait(x: Any) -> Optional[int]:
+    """
+    Map AML/Leukemia cases to 1; healthy/normal/control to 0; unknown to None.
+    Not used here because trait_row is None, but provided for completeness.
+    """
+    val = _after_colon(x).lower()
+    if not val:
+        return None
+
+    # Positive (case) cues
+    positive_cues = [
+        "aml", "acute myeloid leukemia", "acute myelogenous leukemia",
+        "leukemia", "leukemic", "patient with aml", "case", "tumor", "cancer"
+    ]
+    # Negative (control) cues
+    negative_cues = ["control", "healthy", "normal", "non-cancer", "non cancer", "no cancer", "benign"]
+
+    if any(k in val for k in positive_cues):
+        return 1
+    if any(k in val for k in negative_cues):
+        return 0
+    return None
+
+def convert_age(x: Any) -> Optional[float]:
+    """
+    Extract age in years from free text; return None if not parseable or out of range.
+    """
+    val = _after_colon(x).lower()
+    if not val:
+        return None
+    # Find a number (integer or decimal)
+    m = re.search(r"(\d+(?:\.\d+)?)", val)
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+    except Exception:
+        return None
+    # Simple plausibility check
+    if 0 < age < 120:
+        return age
+    return None
+
+def convert_gender(x: Any) -> Optional[int]:
+    """
+    Map female->0, male->1; unknown -> None.
+    """
+    val = _after_colon(x).strip().lower()
+    if not val:
+        return None
+    if val in ["female", "f", "woman", "girl"]:
+        return 0
+    if val in ["male", "m", "man", "boy"]:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+# Skipped because trait_row is None (no usable clinical variability available).

output/preprocess/Acute_Myeloid_Leukemia/code/GSE249638.py

@@ -0,0 +1,284 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE249638"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE249638"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/GSE249638.csv"
+out_gene_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE249638.csv"
+out_clinical_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE249638.csv"
+json_path = "./output/z1/preprocess/Acute_Myeloid_Leukemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene expression data availability
+is_gene_available = True  # Transcriptomic profiling of CD4+ T cells suggests mRNA expression data
+
+# 2. Variable availability and conversion functions
+
+# Determine rows from the Sample Characteristics Dictionary
+trait_row = 1
+age_row = None
+gender_row = None
+
+# Conversion helpers
+def _extract_value(x):
+    if x is None:
+        return None
+    try:
+        s = str(x)
+    except Exception:
+        return None
+    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
+    vl = v.lower()
+    # Map AML/case to 1, controls to 0
+    if "control" in vl or vl in {"healthy", "normal"}:
+        return 0
+    if "leukemia" in vl or vl in {"aml", "acute myeloid leukemia"}:
+        return 1
+    return None
+
+def convert_age(x):
+    # Not available in this dataset
+    return None
+
+def convert_gender(x):
+    # Not available in this dataset
+    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
+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
+# Keep original probe-level data
+probe_data = gene_data.copy()
+
+expr_ids = set(probe_data.index.astype(str))
+
+# Build auxiliary set stripping common Affy suffixes
+def strip_affy_suffix(s: str) -> str:
+    return re.sub(r'_(st|at|s_at|x_at|a_at)$', '', s)
+
+expr_ids_core = {strip_affy_suffix(x) for x in expr_ids}
+
+# 1) Find the best matching identifier column by overlap with expression IDs
+best_id_col = None
+best_overlap = 0
+best_overlap_core = 0
+
+for col in gene_annotation.columns:
+    try:
+        ann_vals = gene_annotation[col].astype(str).str.strip()
+    except Exception:
+        continue
+    ann_set = set(ann_vals)
+    overlap = len(expr_ids.intersection(ann_set))
+
+    # Also try overlap with stripped core IDs (to tolerate suffix differences)
+    ann_set_core = {strip_affy_suffix(x) for x in ann_set}
+    overlap_core = len(expr_ids_core.intersection(ann_set_core))
+
+    # Prioritize exact overlap, then core overlap
+    if overlap > best_overlap or (overlap == best_overlap and overlap_core > best_overlap_core):
+        best_overlap = overlap
+        best_overlap_core = overlap_core
+        best_id_col = col
+
+# Enforce minimum overlap
+if (best_overlap == 0) and (best_overlap_core == 0):
+    print("WARNING: No overlapping identifier column found between gene annotation and expression IDs. "
+          "Skipping probe-to-gene mapping and keeping probe-level data.")
+else:
+    # 2) Choose a gene symbol column
+    gene_col = None
+    # Prefer columns explicitly indicating symbol(s)
+    symbol_like = [c for c in gene_annotation.columns if 'symbol' in c.lower()]
+    if symbol_like:
+        gene_col = symbol_like[0]
+    elif 'gene_assignment' in gene_annotation.columns:
+        gene_col = 'gene_assignment'
+    else:
+        # Fallbacks: any column mentioning 'gene' or 'assignment' that likely contains symbols
+        candidates = [c for c in gene_annotation.columns if ('gene' in c.lower() and 'seq' not in c.lower())
+                      or ('assign' in c.lower())]
+        if candidates:
+            gene_col = candidates[0]
+        elif 'mrna_assignment' in gene_annotation.columns:
+            gene_col = 'mrna_assignment'
+
+    if gene_col is None:
+        print("WARNING: Could not identify a gene symbol column in the annotation. "
+              "Skipping mapping and keeping probe-level data.")
+    else:
+        # If the best match came from core overlap only, try to harmonize IDs by stripping suffix in the annotation
+        ann_col_series = gene_annotation[best_id_col].astype(str).str.strip()
+        if best_overlap == 0 and best_overlap_core > 0:
+            # Create a temporary harmonized ID column matching expression IDs' core part
+            tmp_id_col = f"__HARMONIZED_ID__"
+            gene_annotation[tmp_id_col] = ann_col_series.apply(strip_affy_suffix)
+            use_id_col = tmp_id_col
+        else:
+            use_id_col = best_id_col
+
+        # 3) Build mapping and apply
+        mapping_df = get_gene_mapping(gene_annotation, prob_col=use_id_col, gene_col=gene_col)
+
+        # Restrict mapping to probes present in expression data (using exact IDs; if harmonized, rebuild a bridge)
+        if use_id_col == best_id_col:
+            # Direct mapping
+            mapped_ids = set(mapping_df['ID'])
+            matched = len(mapped_ids.intersection(expr_ids))
+            print(f"Mapping using column '{best_id_col}' to '{gene_col}': matched probes = {matched}")
+            if matched == 0:
+                print("WARNING: Mapping resulted in zero matched probes. Keeping probe-level data.")
+            else:
+                gene_data_mapped = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+                if gene_data_mapped.shape[0] > 0:
+                    gene_data = gene_data_mapped
+                else:
+                    print("WARNING: Gene-level DataFrame is empty after mapping. Keeping probe-level data.")
+        else:
+            # Harmonized core mapping: need to map core IDs back to full probe IDs present in expression
+            # Build a bridge from core -> full IDs (there can be multiple suffix variants; choose those present)
+            core_to_full = {}
+            for pid in expr_ids:
+                core = strip_affy_suffix(pid)
+                core_to_full.setdefault(core, set()).add(pid)
+
+            # Expand mapping_df so that each core ID is replaced by all corresponding full IDs present in probe_data
+            expanded_rows = []
+            for _, row in mapping_df.iterrows():
+                core_id = row['ID']
+                if core_id in core_to_full:
+                    for full_id in core_to_full[core_id]:
+                        new_row = row.copy()
+                        new_row['ID'] = full_id
+                        expanded_rows.append(new_row)
+
+            if not expanded_rows:
+                print("WARNING: Harmonized mapping produced no matches to full probe IDs. Keeping probe-level data.")
+            else:
+                mapping_df_expanded = pd.DataFrame(expanded_rows)
+                matched = mapping_df_expanded['ID'].isin(probe_data.index).sum()
+                print(f"Mapping using harmonized IDs from '{best_id_col}' to '{gene_col}': matched probes = {matched}")
+                if matched == 0:
+                    print("WARNING: Mapping resulted in zero matched probes after expansion. Keeping probe-level data.")
+                else:
+                    gene_data_mapped = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df_expanded)
+                    if gene_data_mapped.shape[0] > 0:
+                        gene_data = gene_data_mapped
+                    else:
+                        print("WARNING: Gene-level DataFrame is empty after mapping. Keeping probe-level data.")
+
+# Step 7: Data Normalization and Linking
+# 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. Assess bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in unbiased_linked_data.columns) and bool(unbiased_linked_data[trait].notna().any()))
+is_trait_biased_bool = bool(is_trait_biased)
+note = "INFO: Only trait available; Age and Gender not provided in sample characteristics."
+
+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_bool,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Acute_Myeloid_Leukemia/code/GSE98578.py

@@ -0,0 +1,107 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE98578"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE98578"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/GSE98578.csv"
+out_gene_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE98578.csv"
+out_clinical_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE98578.csv"
+json_path = "./output/z1/preprocess/Acute_Myeloid_Leukemia/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
+# Decision: This series contains microarray gene expression data from AML cell lines (not miRNA/methylation), so genes are available.
+# However, all samples are AML; no healthy controls. Thus, the trait (Acute_Myeloid_Leukemia) is constant and not usable.
+# No age or gender information is present for cell lines.
+
+# 1) Gene expression data availability
+is_gene_available = True
+
+# 2) Variable availability
+trait_row = None       # All are AML; no case/control variation
+age_row = None         # Not available for cell lines
+gender_row = None      # Not available for cell lines
+
+# 2) Converters (defined for completeness; they won't be used since rows are None)
+def _after_colon(val):
+    if val is None:
+        return None
+    s = str(val)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    # Not used for this cohort; returning None keeps it safe if accidentally called.
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Try to extract a number if any
+    import re
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    return float(m.group()) if m else None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    if v_low in {"female", "f"}:
+        return 0
+    if v_low in {"male", "m"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If trait_row were available:
+# selected_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_df)
+# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+# selected_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Acute_Myeloid_Leukemia/code/GSE99612.py

@@ -0,0 +1,208 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE99612"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE99612"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/GSE99612.csv"
+out_gene_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE99612.csv"
+out_clinical_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE99612.csv"
+json_path = "./output/z1/preprocess/Acute_Myeloid_Leukemia/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  # Expression profiling in cell lines indicates gene data likely available.
+
+# Step 2: Identify variable availability
+# Given this is a cell line experiment (Caco-2 and THP-1) with mixed annotations split across rows
+# and no consistent human-level trait/age/gender per sample, treat these as not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Step 2.2: Define conversion functions (robust to potential inputs, though not used when rows are None)
+import re
+
+def _after_colon(value):
+    if value is None:
+        return None
+    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)
+    if v is None:
+        return None
+    vlow = v.lower()
+    # Map AML-related origins/cell line to 1; non-AML colon adenocarcinoma/Caco-2 to 0
+    if any(k in vlow for k in ["acute monocytic leukemia", "acute myeloid leukemia", "thp-1"]):
+        return 1
+    if any(k in vlow for k in ["caco-2", "colon adenocarcinoma"]):
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vlow = v.lower()
+    # Extract numeric years if present
+    m = re.search(r'(\d+(\.\d+)?)\s*year', vlow)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    # If explicit number without 'year' present, fallback
+    m2 = re.search(r'\d+(\.\d+)?', vlow)
+    if m2:
+        try:
+            return float(m2.group(0))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vlow = v.lower()
+    if "male" in vlow:
+        return 1
+    if "female" in vlow:
+        return 0
+    return None
+
+# Step 3: Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 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 = 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)
+    preview  # for inspection in the execution result
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for mapping
+probe_col = 'ID'  # Matches the probe IDs in the expression data
+gene_symbol_col = 'gene_assignment'  # Contains gene symbols within the annotation text
+
+# Build the mapping DataFrame
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. Handle two cases depending on clinical trait availability
+trait_data_available = ("selected_clinical_data" in globals())
+
+if trait_data_available and selected_clinical_data is not None:
+    # 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 checking
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final 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 trait extracted; proceeded with full linking and QC."
+    )
+
+    # 6. Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # Trait not available in this cohort; skip linking and record metadata
+    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,  # Dummy value since trait not available
+        df=normalized_gene_data,
+        note="INFO: Trait not available for this cohort (cell line study; no human-level labels). Gene data saved; linking skipped."
+    )

output/preprocess/Acute_Myeloid_Leukemia/code/TCGA.py

@@ -0,0 +1,267 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Acute_Myeloid_Leukemia/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Acute_Myeloid_Leukemia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select the most relevant TCGA cohort directory for Acute Myeloid Leukemia
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+trait_terms = ["acute_myeloid_leukemia", "acute myeloid leukemia", "laml", "aml"]
+
+def score_dir(name: str) -> int:
+    lname = name.lower()
+    score = 0
+    # Strong preference for exact trait name or TCGA code
+    if "acute_myeloid_leukemia" in lname:
+        score += 10
+    if "(laml)" in lname or lname.endswith("_laml)") or lname.startswith("tcga_") and "laml" in lname:
+        score += 8
+    # General matches
+    for t in trait_terms:
+        if t in lname:
+            score += 1
+    return score
+
+scored = [(d, score_dir(d)) for d in subdirs]
+scored.sort(key=lambda x: x[1], reverse=True)
+selected_dir = scored[0][0] if scored and scored[0][1] > 0 else None
+
+if selected_dir is None:
+    # No suitable directory found; record and stop further processing
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+
+    # Step 2: Identify clinical and genetic data 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, 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 for further analysis
+    print(list(clinical_df.columns))
+
+# Step 2: Find Candidate Demographic Features
+# Determine the list of all columns from previous step or fallback options
+try:
+    all_columns = list(clinical_df.columns)
+except NameError:
+    all_columns = ['FISH_test_component', 'FISH_test_component_percentage_value', '_INTEGRATION', '_PANCAN_CNA_PANCAN_K8', '_PANCAN_Cluster_Cluster_PANCAN', '_PANCAN_DNAMethyl_LAML', '_PANCAN_DNAMethyl_PANCAN', '_PANCAN_UNC_RNAseq_PANCAN_K16', '_PANCAN_miRNA_PANCAN', '_PANCAN_mirna_LAML', '_PANCAN_mutation_PANCAN', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'age_at_initial_pathologic_diagnosis', 'atra_exposure', 'cumulative_agent_total_dose', 'cytogenetic_abnormality', 'cytogenetic_abnormality_other', 'cytogenetic_analysis_performed_ind', 'days_to_birth', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'disease_detection_molecular_analysis_method_type', 'fish_evaluation_performed_ind', 'fluorescence_in_situ_hybrid_cytogenetics_metaphase_ncls_rslt_cnt', 'fluorescence_in_situ_hybridization_abnormal_result_indicator', 'form_completion_date', 'gender', 'history_of_neoadjuvant_treatment', 'hydroxyurea_administration_prior_registration_clinicl_stdy_ndctr', 'hydroxyurea_agent_administered_day_count', 'immunophenotype_cytochemistry_testing_result', 'informed_consent_verified', 'is_ffpe', 'lab_procedure_abnormal_lymphocyte_result_percent_value', 'lab_procedure_blast_cell_outcome_percentage_value', 'lab_procedure_bone_marrow_band_cell_result_percent_value', 'lab_procedure_bone_marrow_basophil_result_percent_value', 'lab_procedure_bone_marrow_blast_cell_outcome_percent_value', 'lab_procedure_bone_marrow_cellularity_outcome_percent_value', 'lab_procedure_bone_marrow_lymphocyte_outcome_percent_value', 'lab_procedure_bone_marrow_metamyelocyte_result_value', 'lab_procedure_bone_marrow_myelocyte_result_percent_value', 'lab_procedure_bone_marrow_neutrophil_result_percent_value', 'lab_procedure_bone_marrow_prolymphocyte_result_percent_value', 'lab_procedure_bone_marrow_promonocyte_count_result_percent_value', 'lab_procedure_bone_marrow_promyelocyte_result_percent_value', 'lab_procedure_hematocrit_outcome_percent_value', 'lab_procedure_hemoglobin_result_specified_value', 'lab_procedure_leukocyte_result_unspecified_value', 'lab_procedure_monocyte_result_percent_value', 'lab_procedure_platelet_result_specified_value', 'leukemia_french_american_british_morphology_code', 'leukemia_specimen_cell_source_type', 'molecular_analysis_abnormal_result_indicator', 'molecular_analysis_abnormality_testing_result', 'molecular_analysis_performed_indicator', 'patient_id', 'person_history_nonmedical_leukemia_causing_agent_type', 'prior_dx', 'prior_hematologic_disorder_diagnosis_indicator', 'sample_type', 'sample_type_id', 'steroid_therapy_administered', 'tissue_source_site', 'total_dose_units', 'tumor_tissue_site', 'vial_number', 'vital_status', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_LAML_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_LAML_hMethyl27', '_GENOMIC_ID_TCGA_LAML_exp_HiSeqV2', '_GENOMIC_ID_TCGA_LAML_miRNA_GA', '_GENOMIC_ID_data/public/TCGA/LAML/miRNA_GA_gene', '_GENOMIC_ID_TCGA_LAML_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_LAML_mutation_wustl_hiseq_gene', '_GENOMIC_ID_TCGA_LAML_exp_GA_exon', '_GENOMIC_ID_TCGA_LAML_gistic2', '_GENOMIC_ID_TCGA_LAML_exp_GA', '_GENOMIC_ID_TCGA_LAML_hMethyl450', '_GENOMIC_ID_TCGA_LAML_mutation', '_GENOMIC_ID_TCGA_LAML_PDMRNAseq', '_GENOMIC_ID_TCGA_LAML_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_LAML_gistic2thd', '_GENOMIC_ID_TCGA_LAML_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_LAML_mutation_wustl_gene']
+
+# Identify candidate columns
+candidate_age_cols = []
+candidate_gender_cols = []
+
+for col in all_columns:
+    name = col.lower()
+    tokens = name.split('_')
+    # Age candidates: token 'age' or starts with 'age', and known alias 'days_to_birth'
+    if ('age' in tokens) or name.startswith('age') or (name == 'days_to_birth'):
+        candidate_age_cols.append(col)
+    # Gender candidates: token 'gender' or 'sex'
+    if ('gender' in tokens) or (name == 'sex'):
+        candidate_gender_cols.append(col)
+
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Extract and preview if clinical_df is available
+if 'clinical_df' in globals():
+    age_present = [c for c in candidate_age_cols if c in clinical_df.columns]
+    gender_present = [c for c in candidate_gender_cols if c in clinical_df.columns]
+
+    if age_present:
+        print(preview_df(clinical_df[age_present], n=5))
+    if gender_present:
+        print(preview_df(clinical_df[gender_present], n=5))
+
+# Step 3: Select Demographic Features
+# Select columns based on candidate lists and typical TCGA conventions
+age_col = None
+gender_col = None
+
+# Prefer age in years if available; otherwise fall back to days_to_birth
+if 'candidate_age_cols' in globals() and isinstance(candidate_age_cols, (list, tuple)):
+    if 'age_at_initial_pathologic_diagnosis' in candidate_age_cols:
+        age_col = 'age_at_initial_pathologic_diagnosis'
+    elif 'age' in candidate_age_cols:
+        age_col = 'age'
+    elif 'days_to_birth' in candidate_age_cols:
+        age_col = 'days_to_birth'
+    else:
+        # As a fallback, choose the column with the least missingness (if clinical_df is available)
+        if 'clinical_df' in globals():
+            best_col, best_non_null = None, -1.0
+            for c in candidate_age_cols:
+                if c in clinical_df.columns:
+                    non_null_ratio = float(clinical_df[c].notna().mean())
+                    if non_null_ratio > best_non_null:
+                        best_col, best_non_null = c, non_null_ratio
+            # Require at least some reasonable data availability
+            age_col = best_col if best_non_null >= 0.5 else None
+
+# Prefer 'gender'; otherwise fall back to 'sex' if present
+if 'candidate_gender_cols' in globals() and isinstance(candidate_gender_cols, (list, tuple)):
+    if 'gender' in candidate_gender_cols:
+        gender_col = 'gender'
+    elif 'sex' in candidate_gender_cols:
+        gender_col = 'sex'
+    else:
+        # Fallback based on least missingness (if clinical_df is available)
+        if 'clinical_df' in globals():
+            best_col, best_non_null = None, -1.0
+            for c in candidate_gender_cols:
+                if c in clinical_df.columns:
+                    non_null_ratio = float(clinical_df[c].notna().mean())
+                    if non_null_ratio > best_non_null:
+                        best_col, best_non_null = c, non_null_ratio
+            gender_col = best_col if best_non_null >= 0.5 else None
+
+# Explicitly print chosen columns
+print(f"Selected age_col: {age_col}")
+print(f"Selected gender_col: {gender_col}")
+
+# Additionally, print brief info (first 5 values and missingness) if clinical_df is available
+if 'clinical_df' in globals():
+    if age_col is not None and age_col in clinical_df.columns:
+        age_vals = clinical_df[age_col].head(5).tolist()
+        age_missing_ratio = float(clinical_df[age_col].isna().mean())
+        print("age_col preview (first 5):", age_vals)
+        print("age_col missing ratio:", age_missing_ratio)
+    if gender_col is not None and gender_col in clinical_df.columns:
+        gender_vals = clinical_df[gender_col].head(5).tolist()
+        gender_missing_ratio = float(clinical_df[gender_col].isna().mean())
+        print("gender_col preview (first 5):", gender_vals)
+        print("gender_col missing ratio:", gender_missing_ratio)
+
+# Step 4: Feature Engineering and Validation
+import os
+import json
+
+# 1) Extract and standardize clinical features (trait, Age, Gender)
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2) Normalize gene symbols, drop unrecognized, aggregate duplicates, save normalized gene data
+gene_df = genetic_df.copy()
+gene_df_norm = normalize_gene_symbols_in_index(gene_df)
+
+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 = pd.concat([selected_clinical_df, gene_df_norm.T], axis=1, join='inner')
+
+# 4) Handle missing values systematically
+processed_df = handle_missing_values(linked_data, trait_col=trait)
+
+# 5) Determine bias of trait and remove biased demographic features
+is_biased, processed_df = judge_and_remove_biased_features(processed_df, trait=trait)
+
+# 6) Final validation and save cohort info
+# Coerce to plain Python types to avoid JSON serialization issues
+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())
+is_biased_bool = bool(is_biased)
+
+# Optional note: summarize class distribution if available
+note = ""
+if trait in processed_df.columns:
+    counts = processed_df[trait].value_counts(dropna=True).to_dict()
+    note = f"INFO: Trait distribution after preprocessing: {counts}. Cohort: TCGA LAML. "
+
+# Try the library function first
+is_usable = None
+try:
+    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_bool,
+        df=processed_df,
+        note=note
+    )
+except Exception as e:
+    # Fallback: mirror the library's final validation logic and write JSON safely
+    print(f"validate_and_save_cohort_info failed with error: {e}. Falling back to safe writer.")
+
+    _is_gene_available = bool(is_gene_available)
+    _is_trait_available = bool(is_trait_available)
+
+    # Detect abnormality in data and override indicators (mirror library logic)
+    if (processed_df is None) or (is_biased is None):
+        raise ValueError("For final data validation, 'df' and 'is_biased' must be provided.")
+    if len(processed_df) <= 0 or len(processed_df.columns) <= 4:
+        print(f"Abnormality detected in the cohort: TCGA. Preprocessing failed.")
+        _is_gene_available = False
+        if len(processed_df) <= 0:
+            _is_trait_available = False
+
+    _is_available = bool(_is_gene_available and _is_trait_available)
+    is_usable = bool(_is_available and (is_biased_bool is False))
+
+    new_record = {
+        "is_usable": bool(is_usable),
+        "is_gene_available": bool(_is_gene_available),
+        "is_trait_available": bool(_is_trait_available),
+        "is_available": bool(_is_available),
+        "is_biased": (bool(is_biased_bool) if _is_available else None),
+        "has_age": (bool("Age" in processed_df.columns) if _is_available else None),
+        "has_gender": (bool("Gender" in processed_df.columns) if _is_available else None),
+        "sample_size": (int(len(processed_df)) if _is_available else None),
+        "note": str(note) if note is not None else None
+    }
+
+    trait_directory = os.path.dirname(json_path)
+    os.makedirs(trait_directory, exist_ok=True)
+    if not os.path.exists(json_path):
+        with open(json_path, 'w') as file:
+            json.dump({}, file)
+        print(f"A new JSON file was created at: {json_path}")
+
+    with open(json_path, "r") as file:
+        records = json.load(file)
+    records["TCGA"] = new_record
+
+    temp_path = json_path + ".tmp"
+    try:
+        with open(temp_path, 'w') as file:
+            json.dump(records, file, default=str)
+        os.replace(temp_path, json_path)
+    except Exception as e2:
+        print(f"Safe writer also failed: {e2}")
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+        raise
+
+# 7) Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    processed_df.to_csv(out_data_file)

output/preprocess/Acute_Myeloid_Leukemia/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE99612": {
-    "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
-  },
-  "GSE98578": {
-    "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": 48
-  },
-  "GSE249638": {
-    "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": 37
-  },
-  "GSE235070": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 32
-  },
-  "GSE222616": {
-    "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": 42
-  },
-  "GSE222169": {
-    "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
-  },
-  "GSE222124": {
-    "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": 70
-  },
-  "GSE161532": {
-    "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": 61
-  },
-  "GSE121431": {
-    "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": 30
-  },
-  "GSE121291": {
-    "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": 30
-  },
-  "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": 173
-  }
-}
+{"GSE99612": {"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 not available for this cohort (cell line study; no human-level labels). Gene data saved; linking skipped."}, "GSE98578": {"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}, "GSE249638": {"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": 37, "note": "INFO: Only trait available; Age and Gender not provided in sample characteristics."}, "GSE235070": {"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}, "GSE222616": {"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}, "GSE222169": {"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}, "GSE222124": {"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": 70, "note": "INFO: Cell-line study (Jurkat/THP1/KHYG-1) treated with glycodelin; trait inferred from cell type; no age/gender available."}, "GSE161532": {"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 is constant/unavailable for this cohort; only gene data was processed and saved."}, "GSE121431": {"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}, "GSE121291": {"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": 173, "note": "INFO: Trait distribution after preprocessing: {1: 173}. Cohort: TCGA LAML. "}}

output/preprocess/Adrenocortical_Cancer/clinical_data/GSE68950.csv

@@ -1,2 +1,2 @@
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-Adrenocortical_Cancer,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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output/preprocess/Adrenocortical_Cancer/code/GSE108088.py

@@ -0,0 +1,122 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE108088"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE108088"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE108088.csv"
+out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE108088.csv"
+out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE108088.csv"
+json_path = "./output/z1/preprocess/Adrenocortical_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
+from typing import Optional
+
+# Decision summary based on provided background and sample characteristics:
+# - Gene expression data: Likely available (matrix file parsed; comprehensive molecular profiling) -> True
+# - Trait (Adrenocortical_Cancer): Not available in 'condition' list (no adrenocortical carcinoma; would be constant 0) -> None
+# - Age: Not provided in the sample characteristics dictionary -> None
+# - Gender: Not provided in the sample characteristics dictionary -> None
+
+is_gene_available = True
+trait_row = None
+age_row = None
+gender_row = None
+
+def _after_colon(x: str) -> Optional[str]:
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        val = val.strip().strip('"').strip()
+        return val if val != "" else None
+    return None
+
+def convert_trait(x):
+    # Binary: 1 = Adrenocortical cancer, 0 = others
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    keywords_pos = [
+        "adrenocortical", "adrenocortical carcinoma", "adrenal cortex carcinoma",
+        "adrenal cortical carcinoma", "acc"
+    ]
+    if any(k in v for k in keywords_pos):
+        return 1
+    # If clearly a different cancer type, map to 0
+    return 0
+
+def convert_age(x):
+    # Continuous: age in years
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    try:
+        import re
+        nums = re.findall(r"[-+]?\d*\.?\d+", v)
+        if not nums:
+            return None
+        num = float(nums[0])
+        if any(u in v for u in ["month", "mo", "mons"]):
+            return num / 12.0
+        if any(u in v for u in ["day", "d"]):
+            return num / 365.0
+        if any(u in v for u in ["week", "wk", "wks"]):
+            return num / 52.0
+        # default assume years
+        return num
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in ["female", "f", "woman", "girl"]:
+        return 0
+    if v in ["male", "m", "man", "boy"]:
+        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 (no usable clinical trait data).

output/preprocess/Adrenocortical_Cancer/code/GSE143383.py

@@ -0,0 +1,123 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE143383"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE143383"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE143383.csv"
+out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE143383.csv"
+out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE143383.csv"
+json_path = "./output/z1/preprocess/Adrenocortical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Affymetrix PrimeView gene expression profiling (not miRNA/methylation)
+
+# 2) Variable availability based on Sample Characteristics Dictionary
+# Provided dictionary indicates only gender info at key 0 with multiple values.
+trait_row = None   # ACC tumor-only cohort; trait is constant/not provided explicitly => not available
+age_row = None     # No age field present
+gender_row = 0     # gender: F/M/unknown
+
+# 2.2) Conversion functions
+def _after_colon(val):
+    if val is None:
+        return None
+    s = str(val)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(v):
+    # Binary: 1 = Adrenocortical_Cancer (ACC/tumor), 0 = control/normal/benign
+    x = _after_colon(v)
+    if x is None or x == "":
+        return None
+    xl = x.lower()
+    pos_terms = [
+        "adrenocortical carcinoma", "acc", "carcinoma", "tumor", "metastatic",
+        "adrenocortical cancer"
+    ]
+    neg_terms = [
+        "normal", "control", "benign", "adjacent normal", "healthy", "adenoma",
+        "hyperplasia"
+    ]
+    if any(term in xl for term in pos_terms):
+        return 1
+    if any(term in xl for term in neg_terms):
+        return 0
+    return None
+
+def convert_age(v):
+    # Continuous: extract first numeric age in years
+    x = _after_colon(v)
+    if x is None or x == "":
+        return None
+    m = re.search(r"(\d+(\.\d+)?)", x)
+    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(v):
+    # Binary: female=0, male=1, unknown=None
+    x = _after_colon(v)
+    if x is None or x == "":
+        return None
+    xl = x.strip().lower()
+    if xl in {"f", "female", "woman", "women"}:
+        return 0
+    if xl in {"m", "male", "man", "men"}:
+        return 1
+    if xl in {"u", "unk", "unknown", "na", "n/a", "not available"}:
+        return None
+    # Heuristics
+    if "female" in xl:
+        return 0
+    if "male" in xl:
+        return 1
+    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: skipped because trait_row is None (no usable trait variable)

output/preprocess/Adrenocortical_Cancer/code/GSE19776.py

@@ -0,0 +1,125 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE19776"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE19776"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE19776.csv"
+out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE19776.csv"
+out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE19776.csv"
+json_path = "./output/z1/preprocess/Adrenocortical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Series title indicates gene expression profiling
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+# Trait (Adrenocortical_Cancer): All samples are adrenocortical carcinoma tissue => constant => not usable
+trait_row = None
+
+# Age
+age_row = 5  # 'age: ...'
+# Gender
+gender_row = 4  # 'gender: F/M'
+
+# 2.2 Data Type Conversion
+
+def _extract_value(cell):
+    if cell is None:
+        return None
+    # Split on the first colon to get value part
+    parts = str(cell).split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    # Binary: presence of Adrenocortical Cancer (1) vs not (0). Here it's constant 1 for 'adrenocortical carcinoma'.
+    val = _extract_value(x)
+    if val is None or val == "" or val.lower() in {"unknown", "na", "n/a"}:
+        return None
+    v = val.lower()
+    if "adrenocortical" in v and "carcinoma" in v:
+        return 1
+    # If it explicitly states normal/benign or non-cancer tissue, map to 0, else None to avoid mislabeling
+    if any(k in v for k in ["normal", "benign", "control", "adjacent normal"]):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.strip()
+    if v == "" or v.lower() in {"unknown", "na", "n/a"}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female=0, male=1
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"f", "female", "woman", "women"}:
+        return 0
+    if v in {"m", "male", "man", "men"}:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (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, index=True)

output/preprocess/Adrenocortical_Cancer/code/GSE49278.py

@@ -0,0 +1,119 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE49278"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE49278"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE49278.csv"
+out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE49278.csv"
+out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE49278.csv"
+json_path = "./output/z1/preprocess/Adrenocortical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Affymetrix Human Gene 2.0 ST Array indicates gene expression profiling
+
+# 2) Variable availability based on provided Sample Characteristics Dictionary
+trait_row = None  # 'cell type: Adrenocortical carcinoma' is constant across samples -> not useful
+age_row = 0
+gender_row = 1
+
+# 2.2) Data type conversion functions
+def _extract_value(cell):
+    if cell is None:
+        return None
+    s = str(cell)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(cell):
+    # Generalized mapping for adrenal conditions if present; not used here since trait_row is None.
+    val = _extract_value(cell)
+    if val is None or val == '':
+        return None
+    v = val.lower()
+    # Cancer vs non-cancer heuristic
+    if any(k in v for k in ['adrenocortical carcinoma', 'carcinoma', 'acc', 'cancer', 'malignant']):
+        return 1
+    if any(k in v for k in ['normal', 'benign', 'adenoma', 'hyperplasia', 'control', 'healthy']):
+        return 0
+    return None
+
+def convert_age(cell):
+    val = _extract_value(cell)
+    if val is None or val == '':
+        return None
+    m = re.search(r'[-+]?\d*\.?\d+', val)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+        return age
+    except Exception:
+        return None
+
+def convert_gender(cell):
+    val = _extract_value(cell)
+    if val is None or val == '':
+        return None
+    v = val.strip().lower()
+    if v in ['f', 'female', 'woman', 'women']:
+        return 0
+    if v in ['m', 'male', 'man', 'men']:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction: skipped because trait_row is None
+# 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)
+# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+# selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Adrenocortical_Cancer/code/GSE67766.py

@@ -0,0 +1,213 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE67766"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE67766"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE67766.csv"
+out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE67766.csv"
+out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE67766.csv"
+json_path = "./output/z1/preprocess/Adrenocortical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression availability based on background info (cell line study likely includes expression profiling)
+is_gene_available = True
+
+# 2) Variable availability assessment from sample characteristics:
+# Sample Characteristics showed only: {0: ['cell line: SW-13']}
+# No human clinical variation; trait is constant (all SW-13 adrenocortical carcinoma cell line), age/gender not provided.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Converters
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    return s.split(":", 1)[1].strip() if ":" in s else s.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if not v:
+        return None
+    vl = v.lower()
+    # Map plausible labels to case/control
+    positive_markers = [
+        "adrenocortical cancer", "adrenocortical carcinoma",
+        "adrenal cortex carcinoma", "adrenal carcinoma", "acc", "sw-13", "sw13"
+    ]
+    negative_markers = ["normal", "control", "healthy", "adjacent normal", "benign"]
+    if any(p in vl for p in positive_markers):
+        return 1
+    if any(n in vl for n in negative_markers):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if not v:
+        return None
+    vl = v.lower()
+    # Extract number and possible unit
+    m = re.search(r'([-+]?\d*\.?\d+)', vl)
+    if not m:
+        return None
+    num = float(m.group(1))
+    if "month" in vl:
+        return num / 12.0
+    # Assume years otherwise
+    return num
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if not v:
+        return None
+    vl = v.strip().lower()
+    # Common mappings
+    if vl in ["female", "f", "woman", "women"]:
+        return 0
+    if vl in ["male", "m", "man", "men"]:
+        return 1
+    # Sometimes embedded like "sex: female" handled by _after_colon
+    if "female" in vl:
+        return 0
+    if "male" in vl:
+        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 existed and trait_row was available, we would extract and save features as below:
+# 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)
+# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+# 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
+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. Determine appropriate columns and create mapping dataframe
+# Probe IDs align with 'ID' and gene symbols are in 'Symbol'
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 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 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)
+
+# 2-6. Branch based on clinical trait availability
+if 'trait_row' not in locals() or trait_row is None:
+    # No clinical data available; skip linking and final QC
+    linked_data = None
+    # Record metadata as unusable for linkage analysis
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )
+else:
+    # Clinical data available: proceed with linking and downstream processing
+    # Retrieve clinical data if not in memory
+    if 'selected_clinical_data' not in locals() or selected_clinical_data is None:
+        if os.path.exists(out_clinical_data_file):
+            selected_clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+        else:
+            raise RuntimeError("Clinical data not found in memory or on disk, cannot proceed with linking.")
+
+    # 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 and removal of biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final 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: Proceeded with clinical-genetic linking and QC."
+    )
+
+    # 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/Adrenocortical_Cancer/code/GSE68606.py

@@ -0,0 +1,125 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE68606"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE68606"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE68606.csv"
+out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE68606.csv"
+out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE68606.csv"
+json_path = "./output/z1/preprocess/Adrenocortical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene expression data availability
+is_gene_available = True  # Affymetrix HG-U133A; Assay Type: Gene Expression
+
+# 2. Variable availability and conversion functions
+
+# From the sample characteristics dictionary:
+# - trait (Adrenocortical_Cancer): Not available (no evidence of carcinoma; only "Adrenal Cortical Adenoma")
+trait_row = None
+
+# - Age is available at key 6
+age_row = 6
+
+# - Gender is available at key 5
+gender_row = 5
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    # Not used since trait_row is None. Provided for completeness if needed later.
+    val = _after_colon(x)
+    if val is None or val == "" or val == "--":
+        return None
+    v = val.lower()
+    # Positive (1): adrenocortical carcinoma
+    if ("adrenocortical" in v or "adrenal cortical" in v or "adrenal cortex" in v) and ("carcinoma" in v or "cancer" in v):
+        return 1
+    # Explicit negatives (0): adenoma or benign adrenal
+    if ("adrenal cortical adenoma" in v) or ("adenoma" in v and ("adrenal" in v or "adrenocortical" in v)):
+        return 0
+    # If explicitly healthy/control, map to 0
+    if any(tok in v for tok in ["normal", "control", "benign"]):
+        return 0
+    # Otherwise, unknown relative to this trait
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None or val == "" or val in {"--", "na", "n/a", "NA", "unknown"}:
+        return None
+    try:
+        num = float(val)
+        # Return integer if it's whole number
+        return int(num) if num.is_integer() else num
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None or val == "" or val in {"--", "na", "n/a", "NA", "unknown"}:
+        return None
+    v = val.strip().lower()
+    if v in {"male", "m"}:
+        return 1
+    if v 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 (skip because trait_row is None)
+# If trait_row becomes available in future, uncomment the following:
+# 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)
+# selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Adrenocortical_Cancer/code/GSE68950.py

@@ -0,0 +1,160 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE68950"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE68950"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE68950.csv"
+out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE68950.csv"
+out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE68950.csv"
+json_path = "./output/z1/preprocess/Adrenocortical_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) Determine gene expression availability (Affymetrix HT_HG-U133A, gene expression)
+is_gene_available = True
+
+# Candidate rows from sample characteristics dictionary
+# 0: cosmic id
+# 1: disease state  -> candidate for trait (binary: Adrenocortical Cancer vs others)
+# 2: disease location
+# 3: organism part
+# 4: sample
+# 5: cell line code
+# 6: supplier
+# 7: affy_batch
+# 8: crna plate
+
+trait_row = 1
+age_row = None  # Cell line compendium; no human subject age
+gender_row = None  # Cell line compendium; no human subject gender
+
+# Conversion helpers
+def _post_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip()
+    if s == '':
+        return None
+    return s
+
+def convert_trait(v):
+    s = _post_colon(v)
+    if s is None:
+        return None
+    t = s.lower()
+    if t in {'na', 'n/a', '#n/a', 'unknown'}:
+        return None
+    # Positive mapping for Adrenocortical_Cancer
+    # Capture common phrasings
+    if ('adrenocortical' in t and 'carcin' in t) \
+       or (('adrenal' in t) and ('cortical' in t) and ('carcin' in t)) \
+       or (('adrenal' in t) and ('cortex' in t) and ('carcin' in t)) \
+       or ('adrenal cortical carcinoma' in t) \
+       or ('adrenocortical carcinoma' in t) \
+       or ('adrenal cortex carcinoma' in t):
+        return 1
+    return 0
+
+def convert_age(v):
+    s = _post_colon(v)
+    if s is None:
+        return None
+    t = s.lower().replace('years', '').replace('year', '').replace('yrs', '').replace('yr', '').strip()
+    try:
+        val = float(t)
+        if math.isnan(val):
+            return None
+        return val
+    except Exception:
+        return None
+
+def convert_gender(v):
+    s = _post_colon(v)
+    if s is None:
+        return None
+    t = s.strip().lower()
+    if t in {'female', 'f', 'woman', 'women', 'girl'}:
+        return 0
+    if t in {'male', 'm', 'man', 'men', 'boy'}:
+        return 1
+    return None
+
+# 2) Determine if the trait is actually available (non-constant) in this cohort
+is_trait_available = False
+if trait_row is not None:
+    # Map the candidate trait row values to 0/1/None and check variability
+    try:
+        mapped = clinical_data.loc[trait_row].apply(convert_trait)
+        unique_vals = set([x for x in mapped if x is not None])
+        # Trait must have at least two classes to be usable
+        if unique_vals == {0, 1}:
+            is_trait_available = True
+        else:
+            # All 0s (no ACC) or all 1s or only None -> treat as unavailable
+            trait_row = None
+    except Exception:
+        # If anything goes wrong accessing the row, treat as unavailable
+        trait_row = None
+
+# 3) Save metadata (initial filtering)
+# Note: This is a cancer cell line compendium; age and gender are not provided.
+_ = 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 and non-constant)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+else:
+    print("INFO: Trait is not available for association analysis in this cohort (constant or absent). Skipping clinical feature extraction. This is a cell line compendium without age or gender.")

output/preprocess/Adrenocortical_Cancer/code/GSE75415.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE75415"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE75415"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE75415.csv"
+out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE75415.csv"
+out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE75415.csv"
+json_path = "./output/z1/preprocess/Adrenocortical_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
+
+# 1) Gene expression data availability
+is_gene_available = True  # Microarray-based gene expression profiling per background
+
+# 2) Variable availability
+trait_row = 1  # 'histologic type' with carcinoma/adenoma/normal/unknown
+age_row = None  # Age not present in the characteristics dictionary
+gender_row = 0  # 'gender' with female/male/unknown
+
+# 2.2 Conversion functions
+def _extract_value(x):
+    if x is None or (isinstance(x, float) and math.isnan(x)):
+        return None
+    try:
+        s = str(x)
+    except Exception:
+        return None
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip().lower()
+
+# Trait (binary): adrenocortical carcinoma=1; normal/adenoma=0; unknown=None
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None or v == '' or v == 'unknown':
+        return None
+    if 'carcinoma' in v:
+        return 1
+    if 'normal' in v or 'adenoma' in v:
+        return 0
+    return None
+
+# Age (continuous): not available here, but provide a robust parser
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None or v == '' or v == 'unknown' or v == 'not applicable' or v == 'not available':
+        return None
+    # Try to extract a number (years). Handles formats like "5", "5 yrs", "5 years", "60 months"
+    import re
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if not m:
+        return None
+    num = float(m.group(1))
+    # Convert months to years if clearly indicated
+    if 'month' in v:
+        return num / 12.0
+    return num
+
+# Gender (binary): female=0; male=1; unknown=None
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None or v == '' or v == 'unknown':
+        return None
+    if v.startswith('fem'):
+        return 0
+    if v.startswith('mal'):
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait 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,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    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
+# Determine mapping columns based on previous previews:
+# Probe IDs: 'ID'; Gene symbols: 'Gene Symbol'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# Ensure required input paths are available
+if 'soft_file' not in locals() or 'matrix_file' not in locals():
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Ensure clinical data is available (reload if not in scope)
+if 'selected_clinical_df' not in locals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# Reconstruct gene-level expression data deterministically (probe -> gene mapping)
+raw_probe_df = get_genetic_data(matrix_file)
+gene_annotation = get_gene_annotation(soft_file)
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+gene_data = apply_gene_mapping(raw_probe_df, mapping_df)
+
+# 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 checks (remove 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 derived from histologic type; Gender available; Age not provided in series characteristics."
+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 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/Adrenocortical_Cancer/code/GSE76019.py

@@ -0,0 +1,128 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE76019"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE76019"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE76019.csv"
+out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE76019.csv"
+out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE76019.csv"
+json_path = "./output/z1/preprocess/Adrenocortical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Background indicates gene expression microarrays were used.
+
+# 2. Variable Availability
+# Based on the provided Sample Characteristics Dictionary:
+# 0: histology: ACC (constant)
+# 1: Stage: I/II/III/IV
+# 2: efs.time
+# 3: efs.event
+# No explicit age or gender. Trait (Adrenocortical_Cancer) is constant "ACC" -> not usable.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion
+
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    parts = str(value).split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(value):
+    """
+    Binary: 1 = Adrenocortical cancer present, 0 = no cancer/benign/normal.
+    Unknown -> None.
+    """
+    v = _after_colon(value).lower()
+    if not v:
+        return None
+    # Positive indicators
+    pos_markers = ["acc", "adrenocortical carcinoma", "adrenocortical cancer", "carcinoma"]
+    if any(tok == v or tok in v for tok in pos_markers):
+        return 1
+    # Negative indicators
+    neg_markers = ["normal", "control", "benign", "adenoma", "healthy", "non-cancer", "noncancer"]
+    if any(tok in v for tok in neg_markers):
+        return 0
+    return None
+
+def convert_age(value):
+    """
+    Continuous: age in years (float). Tries to parse numeric; converts months/days to years if indicated.
+    Unknown -> None.
+    """
+    v = _after_colon(value).lower()
+    if not v or v in {"na", "nan", "none", "unknown", ""}:
+        return None
+    # Find first float/integers in the string
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    num = float(m.group())
+    # Unit heuristics
+    if "month" in v or "mo" in v:
+        return num / 12.0
+    if "day" in v or "d " in v or v.endswith("d"):
+        return num / 365.25
+    # Default assume years
+    return num
+
+def convert_gender(value):
+    """
+    Binary: female -> 0, male -> 1. Unknown -> None.
+    """
+    v = _after_colon(value).lower()
+    if not v:
+        return None
+    v = v.strip()
+    if v in {"female", "f", "woman", "girl"} or "female" in v:
+        return 0
+    if v in {"male", "m", "man", "boy"} or "male" in v:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skipped because trait_row is None (no usable trait variability).

output/preprocess/Adrenocortical_Cancer/code/GSE90713.py

@@ -0,0 +1,177 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE90713"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE90713"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE90713.csv"
+out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE90713.csv"
+out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE90713.csv"
+json_path = "./output/z1/preprocess/Adrenocortical_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: Gene expression data availability
+is_gene_available = True  # Affymetrix microarrays with RNA extraction imply gene expression data
+
+# Step 2: Variable availability and converters
+
+# Identify rows in the clinical sample characteristics
+trait_row = 2   # 'condition: tumor' vs 'condition: normal'
+age_row = None  # No age field in sample characteristics
+gender_row = None  # No gender field in sample characteristics
+
+# Converters
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if not v:
+        return None
+    v_low = v.lower()
+    if v_low in {'tumor', 'tumour', 'cancer', 'carcinoma'}:
+        return 1
+    if v_low in {'normal', 'control'}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if not v:
+        return None
+    # Extract first number found
+    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 not v:
+        return None
+    v_low = v.lower()
+    if v_low in {'male', 'm'}:
+        return 1
+    if v_low in {'female', 'f'}:
+        return 0
+    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 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(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify appropriate columns for probe IDs and gene symbols based on annotation preview
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Build mapping DataFrame
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping 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 the obtained gene data 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 the 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 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_msg = "INFO: Age and Gender not available in clinical annotations for this cohort."
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note=note_msg
+)
+
+# 6. If the linked data is usable, save it
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Adrenocortical_Cancer/code/TCGA.py

@@ -0,0 +1,348 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Adrenocortical_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Identify the most relevant TCGA cohort directory for the current trait
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+target_keywords = ['adrenocortical', '(acc)']  # prioritize exact trait and abbreviation
+matches = []
+for d in subdirs:
+    name = d.lower()
+    score = 0
+    if 'adrenocortical' in name:
+        score += 2
+    if '(acc)' in name or '_acc' in name:
+        score += 1
+    if score > 0:
+        matches.append((score, d))
+
+selected_dir = None
+if matches:
+    # Choose the highest score; if tie, the first one encountered
+    matches.sort(key=lambda x: (-x[0], x[1]))
+    selected_dir = matches[0][1]
+
+# If no suitable directory is found, mark as completed for this trait and stop further processing
+if selected_dir is None:
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    # Identify clinical and genetic file paths
+    try:
+        clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+    except Exception:
+        # If file identification fails, mark as unavailable
+        validate_and_save_cohort_info(
+            is_final=False,
+            cohort="TCGA",
+            info_path=json_path,
+            is_gene_available=False,
+            is_trait_available=False
+        )
+    else:
+        # Load clinical and genetic data
+        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)
+        # Print clinical column names
+        print(clinical_df.columns.tolist())
+
+# Step 2: Find Candidate Demographic Features
+import os
+import pandas as pd
+
+# Try to use existing clinical_df; otherwise, attempt to load from TCGA ACC cohort
+if 'clinical_df' not in globals():
+    # Heuristic to locate ACC cohort directory
+    acc_dir = os.path.join(tcga_root_dir, 'ACC')
+    if not os.path.isdir(acc_dir):
+        subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+        acc_candidates = [d for d in subdirs if 'ACC' in d.upper()]
+        acc_dir = os.path.join(tcga_root_dir, acc_candidates[0]) if acc_candidates else None
+
+    if acc_dir:
+        try:
+            clinical_file_path, _ = tcga_get_relevant_filepaths(acc_dir)
+            clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, dtype=str)
+        except Exception:
+            clinical_df = None
+    else:
+        clinical_df = None
+
+available_cols = list(clinical_df.columns) if isinstance(clinical_df, pd.DataFrame) else []
+
+def is_age_col(col: str) -> bool:
+    c = col.lower()
+    if 'stage' in c:  # avoid false positive from 'stage'
+        return False
+    if c.startswith('age') or 'age_' in c or '_age' in c or 'age at' in c:
+        return True
+    if 'days_to_birth' in c or 'birth' in c:
+        return True
+    return False
+
+def is_gender_col(col: str) -> bool:
+    c = col.lower().strip()
+    if c in {'gender', 'sex'}:
+        return True
+    if c.startswith('gender') or c.endswith('_gender'):
+        return True
+    # Avoid false positive from strings containing 'sex' (e.g., 'excess')
+    if c == 'sex':
+        return True
+    return False
+
+candidate_age_cols = [c for c in available_cols if is_age_col(c)]
+candidate_gender_cols = [c for c in available_cols if is_gender_col(c)]
+
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Preview extracted data if clinical_df is available and there are candidate columns
+selected_cols = [c for c in (candidate_age_cols + candidate_gender_cols) if c in available_cols]
+if isinstance(clinical_df, pd.DataFrame) and len(selected_cols) > 0:
+    preview_dict = preview_df(clinical_df[selected_cols], n=5)
+    print(preview_dict)
+else:
+    print({})
+
+# Step 3: Select Demographic Features
+import math
+
+# Helper to check if a small list of preview values is usable (not mostly missing)
+def _is_valid_preview(values, min_non_missing=3):
+    if not isinstance(values, (list, tuple)) or len(values) == 0:
+        return False
+    def _is_missing(v):
+        if v is None:
+            return True
+        if isinstance(v, float) and math.isnan(v):
+            return True
+        if isinstance(v, str) and v.strip() == "":
+            return True
+        return False
+    non_missing = sum(0 if _is_missing(v) else 1 for v in values)
+    return non_missing >= min_non_missing
+
+# Try to locate the preview dictionaries created in prior steps
+age_values_dict = {}
+gender_values_dict = {}
+
+# Known possible variable names
+_possible_age_dict_names = ["age_values_dict", "age_preview_dict", "age_dict"]
+_possible_gender_dict_names = ["gender_values_dict", "gender_preview_dict", "gender_dict"]
+
+# Pull from known names if available
+for _name in _possible_age_dict_names:
+    try:
+        _val = eval(_name)
+        if isinstance(_val, dict):
+            age_values_dict = _val
+            break
+    except NameError:
+        pass
+
+for _name in _possible_gender_dict_names:
+    try:
+        _val = eval(_name)
+        if isinstance(_val, dict):
+            gender_values_dict = _val
+            break
+    except NameError:
+        pass
+
+# If separate dicts not found, try to derive them from any combined dict present in the environment
+if (not age_values_dict or not gender_values_dict):
+    # Search for a dict with keys covering candidate columns
+    try:
+        # Collect candidate keys for age and gender
+        age_keys = set(candidate_age_cols) if 'candidate_age_cols' in globals() else set()
+        gender_keys = set(candidate_gender_cols) if 'candidate_gender_cols' in globals() else set()
+        # Scan global namespace for any dict that might contain these keys
+        for _var, _obj in list(globals().items()):
+            if isinstance(_obj, dict):
+                if not age_values_dict and age_keys and any(k in _obj for k in age_keys):
+                    age_values_dict = {k: _obj[k] for k in age_keys if k in _obj}
+                if not gender_values_dict and gender_keys and any(k in _obj for k in gender_keys):
+                    gender_values_dict = {k: _obj[k] for k in gender_keys if k in _obj}
+                if age_values_dict and gender_values_dict:
+                    break
+    except Exception:
+        pass
+
+# Initialize selections
+age_col = None
+gender_col = None
+
+# Select age column with preference and validity checks
+if isinstance(candidate_age_cols, (list, tuple)) and len(candidate_age_cols) > 0 and isinstance(age_values_dict, dict):
+    # Filter to candidates that exist in the preview dict and look valid
+    valid_age_candidates = [c for c in candidate_age_cols if c in age_values_dict and _is_valid_preview(age_values_dict.get(c, []))]
+    # Apply preference: age_at_initial_pathologic_diagnosis > days_to_birth > first valid
+    if 'age_at_initial_pathologic_diagnosis' in valid_age_candidates:
+        age_col = 'age_at_initial_pathologic_diagnosis'
+    elif 'days_to_birth' in valid_age_candidates:
+        age_col = 'days_to_birth'
+    elif valid_age_candidates:
+        age_col = valid_age_candidates[0]
+
+# Select gender column with validity checks
+if isinstance(candidate_gender_cols, (list, tuple)) and len(candidate_gender_cols) > 0 and isinstance(gender_values_dict, dict):
+    valid_gender_candidates = [c for c in candidate_gender_cols if c in gender_values_dict and _is_valid_preview(gender_values_dict.get(c, []))]
+    if 'gender' in valid_gender_candidates:
+        gender_col = 'gender'
+    elif valid_gender_candidates:
+        gender_col = valid_gender_candidates[0]
+
+# If preview dicts are empty, set to None explicitly per instruction
+if not isinstance(age_values_dict, dict) or len(age_values_dict) == 0:
+    age_col = None
+if not isinstance(gender_values_dict, dict) or len(gender_values_dict) == 0:
+    gender_col = None
+
+# Explicitly print out selected columns and their first 5 values (if available)
+print(f"Selected age_col: {age_col}")
+if age_col is not None and isinstance(age_values_dict, dict) and age_col in age_values_dict:
+    print(f"age_col first5 values: {age_values_dict[age_col]}")
+else:
+    print("age_col first5 values: None")
+
+print(f"Selected gender_col: {gender_col}")
+if gender_col is not None and isinstance(gender_values_dict, dict) and gender_col in gender_values_dict:
+    print(f"gender_col first5 values: {gender_values_dict[gender_col]}")
+else:
+    print("gender_col first5 values: None")
+
+# Step 4: Feature Engineering and Validation
+import os
+import pandas as pd
+
+# Ensure clinical_df and genetic_df are available (fallback to reload if needed)
+if 'clinical_df' not in globals() or 'genetic_df' not in globals():
+    # Locate ACC cohort directory
+    subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+    acc_dir = None
+    for d in subdirs:
+        if 'adrenocortical_cancer_(acc)' in d.lower() or d.upper().endswith('(ACC)') or d.upper() == 'ACC':
+            acc_dir = os.path.join(tcga_root_dir, d)
+            break
+    if acc_dir is None:
+        # Worst-case, pick any directory containing ACC
+        acc_candidates = [d for d in subdirs if 'ACC' in d.upper()]
+        acc_dir = os.path.join(tcga_root_dir, acc_candidates[0]) if acc_candidates else None
+
+    if acc_dir:
+        clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(acc_dir)
+        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)
+    else:
+        raise RuntimeError("ACC cohort directory not found; cannot proceed.")
+
+# Use selected demographic columns from previous step; default to None if missing
+age_col = age_col if 'age_col' in globals() else None
+gender_col = gender_col if 'gender_col' in globals() else None
+
+# 1) Extract and standardize clinical features (Trait, optional Age and Gender)
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2) Normalize gene symbols and save normalized gene expression
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df.copy())
+normalized_gene_df = normalized_gene_df.apply(pd.to_numeric, errors='coerce')
+
+# Save normalized gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3) Link clinical and genetic data on sample IDs
+expr_t = normalized_gene_df.T  # samples x genes
+linked_data = selected_clinical_df.join(expr_t, how='inner')
+
+# 4) Handle missing values systematically
+processed_df = handle_missing_values(linked_data, trait_col=trait)
+
+# 5) Determine bias in trait and demographic features; remove biased demographics
+trait_biased, processed_df = judge_and_remove_biased_features(processed_df, trait)
+
+# 6) Final validation and save cohort info
+# Sanitize DataFrame to avoid potential non-JSON-serializable types from pandas index/columns
+processed_df_safe = processed_df.copy()
+processed_df_safe.index = processed_df_safe.index.astype(str)
+processed_df_safe.columns = [str(c) for c in list(processed_df_safe.columns)]
+
+covariate_cols = [trait, 'Age', 'Gender']
+gene_cols_in_processed = [c for c in processed_df_safe.columns if c not in covariate_cols]
+is_gene_available = bool(len(gene_cols_in_processed) > 0)
+is_trait_available = bool((trait in processed_df_safe.columns) and processed_df_safe[trait].notna().any())
+
+note_parts = [
+    "INFO: TCGA ACC cohort processed; gene symbols normalized via NCBI synonyms.",
+]
+if age_col or gender_col:
+    note_parts.append(f"INFO: Age from '{age_col if age_col else 'None'}', Gender from '{gender_col if gender_col else 'None'}'.")
+if trait_biased:
+    note_parts.append("WARNING: Trait is severely biased (likely no normal controls in ACC).")
+note = " ".join(note_parts)
+
+# Attempt validation; if serialization fails, retry after deeper sanitization
+is_usable = False
+try:
+    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=bool(trait_biased),
+        df=processed_df_safe,
+        note=note
+    )
+except TypeError as e:
+    # Deep sanitize: ensure Python-native types in a minimal copy of df metadata
+    processed_df_safe2 = processed_df_safe.copy()
+    processed_df_safe2.index = [str(x) for x in processed_df_safe2.index.tolist()]
+    processed_df_safe2.columns = [str(x) for x in processed_df_safe2.columns.tolist()]
+    try:
+        is_usable = validate_and_save_cohort_info(
+            is_final=True,
+            cohort="TCGA",
+            info_path=json_path,
+            is_gene_available=bool(is_gene_available),
+            is_trait_available=bool(is_trait_available),
+            is_biased=bool(trait_biased),
+            df=processed_df_safe2,
+            note=note
+        )
+    except Exception as e2:
+        # If still failing, do not raise to keep pipeline running; mark unusable in a minimal way
+        is_usable = False
+
+# 7) Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    processed_df_safe.to_csv(out_data_file)

output/preprocess/Adrenocortical_Cancer/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE90713": {
-    "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": 63
-  },
-  "GSE76019": {
-    "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
-  },
-  "GSE75415": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": true,
-    "sample_size": 30
-  },
-  "GSE68950": {
-    "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
-  },
-  "GSE68606": {
-    "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": 88
-  },
-  "GSE67766": {
-    "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
-  },
-  "GSE49278": {
-    "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": 43
-  },
-  "GSE19776": {
-    "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": 12
-  },
-  "GSE143383": {
-    "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
-  },
-  "GSE108088": {
-    "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
-  },
-  "TCGA": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 79
-  }
-}
+{"GSE90713": {"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": 63, "note": "INFO: Age and Gender not available in clinical annotations for this cohort."}, "GSE76019": {"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}, "GSE75415": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 30, "note": "INFO: Trait derived from histologic type; Gender available; Age not provided in series characteristics."}, "GSE68950": {"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}, "GSE68606": {"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}, "GSE67766": {"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}, "GSE49278": {"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}, "GSE19776": {"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}, "GSE143383": {"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}, "GSE108088": {"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": 79, "note": "INFO: TCGA ACC cohort processed; gene symbols normalized via NCBI synonyms. INFO: Age from 'age_at_initial_pathologic_diagnosis', Gender from 'gender'. WARNING: Trait is severely biased (likely no normal controls in ACC)."}}

output/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE29801.csv

@@ -0,0 +1,4 @@
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output/preprocess/Age-Related_Macular_Degeneration/code/GSE29801.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE29801"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE29801"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/GSE29801.csv"
+out_gene_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/gene_data/GSE29801.csv"
+out_clinical_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE29801.csv"
+json_path = "./output/z1/preprocess/Age-Related_Macular_Degeneration/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  # Transcriptome microarray with two-color universal reference -> gene expression
+
+# 2. Variable availability and conversion functions
+trait_row = 3       # ocular disease: normal vs AMD
+age_row = 2         # age (years)
+gender_row = 1      # gender: male/female
+
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return x
+    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):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    # Using ocular disease field only
+    if v_low in {"amd"}:
+        return 1
+    if v_low in {"normal", "none"}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v = v.strip()
+    # extract first number
+    m = re.search(r"-?\d+(\.\d+)?", v)
+    if not m:
+        return None
+    try:
+        val = float(m.group(0))
+        return val
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    if v_low in {"female", "f"}:
+        return 0
+    if v_low in {"male", "m"}:
+        return 1
+    return None
+
+# 3. Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+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 provided identifiers are numeric strings (e.g., '12', '13'), 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
+# Decide the appropriate columns for mapping: probe identifier and gene symbol
+probe_col = 'ID'            # Matches numeric string probe IDs in gene_data index
+gene_symbol_col = 'GENE_SYMBOL'  # Column containing gene symbols
+
+# 2. Build the mapping DataFrame
+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(gene_data, 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 the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and remove biased demographic features if any
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available_flag = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_flag = True  # trait_row was not None and we successfully extracted clinical features
+is_usable = validate_and_save_cohort_info(
+    True,
+    cohort,
+    json_path,
+    is_gene_available_flag,
+    is_trait_available_flag,
+    is_trait_biased,
+    unbiased_linked_data,
+    note="INFO: Gene symbols normalized using NCBI synonyms; probe-level data aggregated to genes."
+)
+
+# 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/Age-Related_Macular_Degeneration/code/GSE38662.py

@@ -0,0 +1,143 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE38662"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE38662"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/GSE38662.csv"
+out_gene_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/gene_data/GSE38662.csv"
+out_clinical_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE38662.csv"
+json_path = "./output/z1/preprocess/Age-Related_Macular_Degeneration/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Affymetrix RNA expression arrays => gene expression data is available.
+
+# 2) Variable availability (by inspecting the provided sample characteristics dictionary)
+# Trait (Age-Related Macular Degeneration) is not available in this hESC dataset.
+trait_row = None
+
+# No age information is provided.
+age_row = None
+
+# Gender is available at row 3 with values like 'gender: 46,XX' and 'gender: 46,XY'.
+gender_row = 3
+
+# 2.2) Conversion functions
+def convert_trait(v):
+    # Binary: 1 = AMD case, 0 = control; unknown => None
+    # Not used here since trait_row is None, but kept for interface completeness.
+    if pd.isna(v):
+        return None
+    s = str(v)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip().lower()
+    # Heuristics for AMD labeling
+    if any(k in s for k in ['amd', 'age-related macular degeneration', 'macular degeneration']):
+        # Exclude explicit negatives
+        if any(k in s for k in ['non-amd', 'control', 'normal', 'healthy', 'no amd']):
+            return 0
+        return 1
+    if any(k in s for k in ['control', 'normal', 'healthy', 'non-amd']):
+        return 0
+    return None
+
+def convert_age(v):
+    # Continuous numeric age in years; unknown => None
+    if pd.isna(v):
+        return None
+    s = str(v)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip().lower()
+    # Extract first numeric token
+    m = re.search(r'(\d+(\.\d+)?)', s)
+    if not m:
+        return None
+    try:
+        age_val = float(m.group(1))
+    except Exception:
+        return None
+    # Plausibility check for human age
+    if 0 <= age_val <= 120:
+        return age_val
+    return None
+
+def convert_gender(v):
+    # Binary: female=0, male=1; unknown => None
+    if pd.isna(v):
+        return None
+    s = str(v)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip().lower().replace(' ', '')
+    # Textual labels
+    if s in {'female', 'f', 'woman', 'girl'}:
+        return 0
+    if s in {'male', 'm', 'man', 'boy'}:
+        return 1
+    # Karyotype patterns
+    if 'xx' in s:
+        return 0
+    if 'xy' in s:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (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 = preview_df(selected_clinical_df)
+    print(preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    # Save as features x samples; adjust transpose here if your downstream expects samples as rows.
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Age-Related_Macular_Degeneration/code/GSE43176.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE43176"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE43176"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/GSE43176.csv"
+out_gene_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/gene_data/GSE43176.csv"
+out_clinical_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE43176.csv"
+json_path = "./output/z1/preprocess/Age-Related_Macular_Degeneration/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix U133A gene expression profiling; not miRNA/methylation
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the provided Sample Characteristics Dictionary, no AMD-related trait, age, or gender fields are available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Conversion functions
+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()
+
+def convert_trait(x):
+    """
+    Binary: 1 = AMD case, 0 = control.
+    This dataset is AML-focused and lacks AMD labels; return None unless AMD/control keywords are detected.
+    """
+    v = _after_colon(x).lower()
+    if not v:
+        return None
+    # Positive AMD indicators
+    amd_pos = ['amd', 'age-related macular degeneration', 'armd', 'macular degeneration']
+    if any(k in v for k in amd_pos):
+        # Avoid false positives like "no amd"
+        if any(neg in v for neg in ['no amd', 'non-amd', 'without amd']):
+            return 0
+        return 1
+    # Control/normal indicators (common in AMD datasets)
+    if any(k in v for k in ['control', 'normal', 'healthy', 'non-amd', 'no amd']):
+        return 0
+    return None
+
+def convert_age(x):
+    """
+    Continuous age in years. Extract first numeric token.
+    """
+    v = _after_colon(x).lower()
+    if not v or v in ['na', 'n/a', 'not available', 'unknown']:
+        return None
+    # Extract number (int/float)
+    import re
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if m:
+        try:
+            return float(m.group(1))
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    """
+    Binary: female=0, male=1
+    """
+    v = _after_colon(x).lower()
+    if not v or v in ['na', 'n/a', 'not available', 'unknown']:
+        return None
+    if v in ['male', 'm']:
+        return 1
+    if v in ['female', 'f']:
+        return 0
+    # Handle phrases like "sex: Male" already covered; unknown otherwise
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skipped because trait_row is None (no AMD clinical trait available in this cohort).
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+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 columns for probe IDs and gene symbols based on annotation preview
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# 2. Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert 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
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. Branch based on availability of clinical trait data
+if 'selected_clinical_data' in locals():
+    # Link, handle missingness, bias check, validate, and optionally save linked data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+    linked_data = handle_missing_values(linked_data, trait)
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Clinical trait available; processed, missingness handled, and bias evaluated."
+    )
+
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Trait not available; skip linking and downstream steps. Validate and record metadata.
+    _ = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,  # placeholder; not used when trait is unavailable
+        df=normalized_gene_data.T,
+        note="WARNING: Trait (AMD) not available in this cohort; only normalized gene data saved."
+    )

output/preprocess/Age-Related_Macular_Degeneration/code/GSE45485.py

@@ -0,0 +1,375 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE45485"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE45485"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/GSE45485.csv"
+out_gene_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/gene_data/GSE45485.csv"
+out_clinical_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE45485.csv"
+json_path = "./output/z1/preprocess/Age-Related_Macular_Degeneration/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 expression data availability
+# Background information indicates skin gene expression profiling (not miRNA/methylation).
+is_gene_available = True
+
+# Step 2: Variable availability and conversion functions
+# The current cohort is about Systemic Sclerosis vs Normal controls; no AMD trait, age, or gender fields detected.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Chosen data types
+trait_type = 'binary'
+age_type = 'continuous'
+gender_type = 'binary'
+
+def _extract_value(cell):
+    if cell is None:
+        return None
+    try:
+        parts = str(cell).split(':', 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        val = val.strip()
+        return val if val != '' else None
+    except Exception:
+        return None
+
+def convert_trait(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if 'age-related macular degeneration' in v or 'amd' in v:
+        return 1
+    if v in {'control', 'normal', 'healthy'} or 'control' in v or 'normal' in v or 'healthy' in v:
+        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 m:
+        try:
+            age = float(m.group())
+            if 0 <= age <= 120:
+                return age
+        except Exception:
+            pass
+    return None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.lower()
+    if v in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if v in {'male', 'm', 'man', 'men'}:
+        return 1
+    if 'female' in v or ' sex f' in v or ' sex: f' in v:
+        return 0
+    if 'male' in v or ' sex m' in v or ' sex: m' in v:
+        return 1
+    return None
+
+# Step 3: Save initial metadata (trait availability determined by trait_row is not None)
+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_data, trait, trait_row,
+#                                                     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)
+
+# Step 3: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import json
+import pandas as pd
+import numpy as np
+
+# 1) Determine if gene expression data is likely available (exclude pure miRNA/methylation)
+def detect_gene_expression_availability(base_dir: str) -> bool:
+    miRNA_patterns = re.compile(r'mirna|micro.?rna', re.I)
+    methyl_patterns = re.compile(r'methyl|humanmethylation|450k|850k|epic|bisulfite|rrbs', re.I)
+    expr_signals = re.compile(r'series_matrix|matrix|expression|counts|rsem|tpm|fpkm|cel', re.I)
+
+    has_expr_signal = False
+    has_exclusion_signal = False
+    for root, _, files in os.walk(base_dir):
+        for fn in files:
+            fn_l = fn.lower()
+            if expr_signals.search(fn_l):
+                has_expr_signal = True
+            if miRNA_patterns.search(fn_l) or methyl_patterns.search(fn_l):
+                has_exclusion_signal = True
+            # Quick check inside platform annotation if available
+            if fn_l.endswith(('.txt', '.soft', '.tsv', '.csv')):
+                # Only peek small files to avoid overhead
+                try:
+                    fpath = os.path.join(root, fn)
+                    if os.path.getsize(fpath) < 2_000_000:  # 2MB
+                        with open(fpath, 'r', errors='ignore') as fh:
+                            head = fh.read(5000).lower()
+                            if expr_signals.search(head):
+                                has_expr_signal = True
+                            if miRNA_patterns.search(head) or methyl_patterns.search(head):
+                                has_exclusion_signal = True
+                except Exception:
+                    pass
+    # Prefer excluding when clearly miRNA/methylation; otherwise require some expression signal
+    if has_exclusion_signal:
+        return False
+    return has_expr_signal
+
+is_gene_available = detect_gene_expression_availability(in_cohort_dir)
+
+# 2) Variable availability and conversion functions
+
+def _after_colon(x):
+    if x is None or (isinstance(x, float) and np.isnan(x)):
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    return s if s != '' else None
+
+def _lower(s):
+    return s.lower() if isinstance(s, str) else s
+
+# Conversion functions
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # positive AMD indicators
+    pos_kw = [
+        r'\bamd\b', 'age[- ]related macular degeneration', 'macular degeneration',
+        r'\bdry\b', r'\bwet\b', r'\bnamd\b', 'neovascular', 'geographic atrophy', r'\bga\b'
+    ]
+    # negative/control indicators
+    neg_kw = ['control', 'normal', 'healthy', 'no amd', 'non-amd', 'without amd']
+    if any(re.search(k, vl) for k in pos_kw):
+        return 1
+    if any(k in vl for k in neg_kw):
+        return 0
+    # Sometimes values like "case" vs "control"
+    if re.search(r'\bcase\b', vl):
+        return 1
+    if re.search(r'\bctrl\b', vl) or re.search(r'\bcontrol\b', vl):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Extract first plausible age number
+    nums = re.findall(r'(\d+(?:\.\d+)?)', vl)
+    if not nums:
+        return None
+    try:
+        age = float(nums[0])
+        if 0 < age < 120:
+            return age
+    except Exception:
+        return None
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    # normalize single-letter
+    if vl in {'m', 'male'}:
+        return 1
+    if vl in {'f', 'female'}:
+        return 0
+    # variations
+    if 'male' in vl:
+        return 1
+    if 'female' in vl or 'femen' in vl:
+        return 0
+    return None
+
+# Heuristic scanners to identify rows in clinical_data
+def _norm_cell(cell):
+    # Always return a tuple (header, val) to avoid unpacking errors
+    if cell is None or (isinstance(cell, float) and np.isnan(cell)):
+        return '', ''
+    s = str(cell).strip()
+    if not s:
+        return '', ''
+    parts = s.split(':', 1)
+    header = parts[0].strip().lower()
+    val = parts[1].strip().lower() if len(parts) > 1 else header
+    return header, val
+
+def find_rows_for_vars(clin_df: pd.DataFrame):
+    trait_row = None
+    age_row = None
+    gender_row = None
+
+    def score_trait_row(values):
+        pos_hits = 0
+        neg_hits = 0
+        header_hits = 0
+        for v in values:
+            h, val = _norm_cell(v)
+            if any(k in h for k in ['disease', 'diagnosis', 'phenotype', 'condition', 'status']):
+                header_hits += 1
+            ct = convert_trait(v)
+            if ct == 1:
+                pos_hits += 1
+            elif ct == 0:
+                neg_hits += 1
+            # direct keyword fallback
+            if re.search(r'\bamd\b', val) or 'macular deg' in val:
+                pos_hits += 1
+            if 'control' in val or 'normal' in val or 'healthy' in val:
+                neg_hits += 1
+        uniq = set([convert_trait(v) for v in values if convert_trait(v) is not None])
+        if len(uniq) < 2:
+            return -1  # constant or insufficient variation
+        return pos_hits + neg_hits + header_hits
+
+    def score_age_row(values):
+        age_like = 0
+        header_like = 0
+        numeric_count = 0
+        uniq = set()
+        for v in values:
+            h, val = _norm_cell(v)
+            if 'age' in h:
+                header_like += 1
+            a = convert_age(v)
+            if a is not None:
+                numeric_count += 1
+                uniq.add(a)
+            if 'age' in val:
+                age_like += 1
+        if numeric_count == 0:
+            return -1
+        if len(uniq) <= 1:
+            return -1
+        return numeric_count + header_like + age_like
+
+    def score_gender_row(values):
+        header_like = 0
+        mapped = []
+        for v in values:
+            h, _val = _norm_cell(v)
+            if 'gender' in h or 'sex' in h:
+                header_like += 1
+            mapped.append(convert_gender(v))
+        uniq = set([m for m in mapped if m is not None])
+        if len(uniq) < 2:
+            return -1
+        # score by number of mapped values plus header match
+        return len(uniq) + header_like + sum([1 for m in mapped if m is not None])
+
+    # Iterate rows by position to ensure int indexing is valid for geo_select_clinical_features
+    best_trait = (-1, None)
+    best_age = (-1, None)
+    best_gender = (-1, None)
+    for r in range(clin_df.shape[0]):
+        vals = clin_df.iloc[r, :].tolist()
+        tscore = score_trait_row(vals)
+        if tscore > best_trait[0]:
+            best_trait = (tscore, r)
+        ascore = score_age_row(vals)
+        if ascore > best_age[0]:
+            best_age = (ascore, r)
+        gscore = score_gender_row(vals)
+        if gscore > best_gender[0]:
+            best_gender = (gscore, r)
+
+    trait_row = best_trait[1] if best_trait[0] > 0 else None
+    age_row = best_age[1] if best_age[0] > 0 else None
+    gender_row = best_gender[1] if best_gender[0] > 0 else None
+    return trait_row, age_row, gender_row
+
+# Attempt to access clinical_data from previous step
+clinical_df = None
+if 'clinical_data' in globals():
+    clinical_df = clinical_data
+elif 'clinical_df' in globals():
+    clinical_df = globals()['clinical_df']
+
+trait_row = None
+age_row = None
+gender_row = None
+
+if isinstance(clinical_df, pd.DataFrame) and clinical_df.shape[0] > 0 and clinical_df.shape[1] > 0:
+    trait_row, age_row, gender_row = find_rows_for_vars(clinical_df)
+
+# 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 is available
+if is_trait_available and isinstance(clinical_df, pd.DataFrame):
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_df,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+    # Preview and save
+    _preview = preview_df(selected_clinical_df, n=5)
+    print(_preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Age-Related_Macular_Degeneration/code/GSE62224.py

@@ -0,0 +1,462 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE62224"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE62224"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/GSE62224.csv"
+out_gene_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/gene_data/GSE62224.csv"
+out_clinical_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE62224.csv"
+json_path = "./output/z1/preprocess/Age-Related_Macular_Degeneration/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 based on background info (Agilent whole-genome microarrays)
+is_gene_available = True
+
+# 2) Variable availability from Sample Characteristics Dictionary:
+# Keys present: 0 donor id, 1 plating density, 2 passage number, 3 culture time, 4 cultureware, 5 treatment
+# No AMD status, age, or gender information is available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Converters (robust to varied input formats; unused here due to lack of fields)
+def _after_colon(x):
+    if x is None:
+        return None
+    try:
+        part = str(x)
+        if ':' in part:
+            part = part.split(':', 1)[1]
+        return part.strip()
+    except Exception:
+        return None
+
+def convert_trait(x):
+    # Map AMD-related statuses to binary: AMD=1, control/normal/healthy=0
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    s = v.lower()
+    # Negative patterns first
+    neg_terms = ['control', 'normal', 'healthy', 'no amd', 'non-amd', 'non amd', 'none']
+    if any(t in s for t in neg_terms):
+        return 0
+    pos_terms = ['amd', 'age-related macular degeneration', 'age related macular degeneration', 'neovascular', 'geographic atrophy']
+    if any(t in s for t in pos_terms):
+        # Guard: if explicitly "no amd" handled above
+        return 1
+    return None
+
+def convert_age(x):
+    # Extract numeric age; support units days/weeks/months/years.
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    s = v.lower()
+    m = re.search(r'[-+]?\d*\.?\d+', s)
+    if not m:
+        return None
+    num = float(m.group())
+    # Infer units
+    if 'day' in s:
+        return num / 365.0
+    if 'week' in s:
+        return num / 52.0
+    if 'month' in s:
+        return num / 12.0
+    if 'hour' in s:
+        return num / (24.0 * 365.0)
+    # Default assume years
+    return num
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    s = v.strip().lower()
+    if s in ['f', 'female', 'woman', 'girl']:
+        return 0
+    if s in ['m', 'male', 'man', 'boy']:
+        return 1
+    # Handle phrases like "sex: Male", "gender: female"
+    if 'female' in s:
+        return 0
+    if 'male' in s:
+        return 1
+    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: skip because trait_row is None (no clinical trait available)
+# If trait_row were available:
+# selected = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected, n=5)
+# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+# selected.to_csv(out_clinical_data_file, index=False)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+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 identifier and gene symbol columns by matching against existing data and non-null counts
+id_candidates = ['ID', 'ID_REF', 'PROBE_ID', 'ProbeID', 'SPOT_ID', 'NAME', 'SPOT_ID.1']
+sym_candidates = ['GENE_SYMBOL', 'GENE', 'SYMBOL', 'GENE_NAME', 'DESCRIPTION']
+
+# Select probe ID column that best matches the expression index
+best_id_col = None
+best_overlap = -1
+gene_index_set = set(map(str, gene_data.index))
+for col in id_candidates:
+    if col in gene_annotation.columns:
+        vals = gene_annotation[col].astype(str)
+        overlap = vals.isin(gene_index_set).sum()
+        if overlap > best_overlap:
+            best_overlap = overlap
+            best_id_col = col
+
+# Select gene symbol column with most non-null entries
+best_sym_col = None
+best_nonnull = -1
+for col in sym_candidates:
+    if col in gene_annotation.columns:
+        nonnull = gene_annotation[col].notna().sum()
+        if nonnull > best_nonnull:
+            best_nonnull = nonnull
+            best_sym_col = col
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=best_id_col, gene_col=best_sym_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: Gene Identifier Mapping
+import re
+import pandas as pd
+
+# Robust mapping with normalization and multiple fallbacks
+
+def _normalize_token(x):
+    if pd.isna(x):
+        return None
+    s = str(x).strip()
+    # Normalize integers that may appear as floats like "12.0" -> "12"
+    if re.fullmatch(r'\d+(\.0+)?', s):
+        try:
+            return str(int(float(s)))
+        except Exception:
+            pass
+    try:
+        fx = float(s)
+        if fx.is_integer():
+            return str(int(fx))
+    except Exception:
+        pass
+    return s
+
+# 1) Start from annotation and remove obvious Agilent control probes if CONTROL_TYPE provided
+ann = gene_annotation.copy()
+if 'CONTROL_TYPE' in ann.columns:
+    ct = ann['CONTROL_TYPE'].astype(str).str.lower()
+    ann = ann.loc[~ct.isin({'pos', 'neg'}) | ann['CONTROL_TYPE'].isna()]
+
+# 2) Decide probe ID column by maximizing normalized overlap with expression row IDs
+id_candidates = ['ID', 'ORDER', 'ID_REF', 'PROBE_ID', 'ProbeID', 'SPOT_ID', 'NAME', 'SPOT_ID.1']
+expr_ids_norm = pd.Index(gene_data.index.astype(str).map(_normalize_token))
+expr_id_set = set(expr_ids_norm)
+
+overlaps = {}
+for col in id_candidates:
+    if col in ann.columns:
+        vals_norm = ann[col].map(_normalize_token)
+        overlaps[col] = vals_norm.isin(expr_id_set).sum()
+
+# If no candidates present, fail early
+if not overlaps:
+    raise ValueError("No candidate probe ID columns found in annotation.")
+
+# Pick the column with the highest overlap
+best_id_col = max(overlaps, key=overlaps.get)
+best_id_overlap = overlaps[best_id_col]
+
+# If overlap is zero across the board, try to at least pick 'ID' or 'ORDER' if present as a last resort
+if best_id_overlap == 0:
+    for pref in ['ID', 'ORDER']:
+        if pref in overlaps:
+            best_id_col = pref
+            break
+
+print(f"ID candidate overlaps: {overlaps}")
+print(f"Selected probe ID column: {best_id_col} (overlap={overlaps.get(best_id_col, 0)})")
+
+# 3) Choose a gene-symbol-related column; prefer those yielding extractable gene symbols
+sym_candidates = ['GENE_SYMBOL', 'GENE', 'SYMBOL', 'GENE_NAME', 'DESCRIPTION']
+sym_stats = {}
+for col in sym_candidates:
+    if col in ann.columns:
+        # Count rows that are not null (basic availability)
+        nonnull = ann[col].notna().sum()
+        # Rough estimate of extractable symbols (use the provided extractor)
+        extracted_counts = ann[col].astype(str).map(lambda s: len(extract_human_gene_symbols(s)))
+        nonempty_extracted = int((extracted_counts > 0).sum())
+        sym_stats[col] = (nonnull, nonempty_extracted)
+
+if not sym_stats:
+    raise ValueError("No candidate gene symbol columns found in annotation.")
+
+# Rank symbol columns primarily by nonempty_extracted, secondarily by nonnull
+sym_ranked = sorted(sym_stats.items(), key=lambda kv: (kv[1][1], kv[1][0]), reverse=True)
+print(f"Symbol column candidates ranked (nonnull, extractable): {sym_ranked}")
+
+# 4) Build mapping with fallbacks: try ranked symbol columns until overlap with expression IDs is >0
+mapping_df = None
+chosen_sym_col = None
+overlap_after_drop = 0
+
+for sym_col, (_nn, _ext) in sym_ranked:
+    tmp_map = get_gene_mapping(ann, prob_col=best_id_col, gene_col=sym_col)
+    if tmp_map.empty:
+        continue
+    # Normalize IDs to match expression index style
+    tmp_map['ID'] = tmp_map['ID'].map(_normalize_token)
+    tmp_map = tmp_map.dropna(subset=['ID'])
+    # Check overlap with expression after cleaning
+    cur_overlap = tmp_map['ID'].isin(expr_ids_norm).sum()
+    print(f"Trying symbol column '{sym_col}': mapping rows={len(tmp_map)}, overlap={cur_overlap}")
+    if cur_overlap > 0:
+        mapping_df = tmp_map
+        chosen_sym_col = sym_col
+        overlap_after_drop = cur_overlap
+        break
+
+# If still none found, pick the best available even if overlap is zero (last resort)
+if mapping_df is None:
+    # Choose the top-ranked symbol column to salvage what we can
+    sym_col = sym_ranked[0][0]
+    tmp_map = get_gene_mapping(ann, prob_col=best_id_col, gene_col=sym_col)
+    if tmp_map.empty:
+        raise ValueError("Failed to build a non-empty mapping dataframe from annotation.")
+    tmp_map['ID'] = tmp_map['ID'].map(_normalize_token)
+    tmp_map = tmp_map.dropna(subset=['ID'])
+    mapping_df = tmp_map
+    chosen_sym_col = sym_col
+    overlap_after_drop = mapping_df['ID'].isin(expr_ids_norm).sum()
+
+print(f"Selected symbol column: {chosen_sym_col}, final mapping rows={len(mapping_df)}, final overlap={overlap_after_drop}")
+
+# 5) Apply mapping to convert probe-level to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 8: Gene Identifier Mapping
+import pandas as pd
+
+# If previous attempt emptied gene_data, reload it
+try:
+    if gene_data is None or getattr(gene_data, 'empty', True):
+        gene_data = get_genetic_data(matrix_file)
+except NameError:
+    gene_data = get_genetic_data(matrix_file)
+
+ann = gene_annotation.copy()
+
+# Helpers
+def _normalize_int_str(x):
+    s = str(x).strip()
+    # normalize floats like "12.0" to "12" when they represent integers
+    try:
+        f = float(s)
+        if f.is_integer():
+            return str(int(f))
+    except Exception:
+        pass
+    return s
+
+# 1) Choose symbol column: prefer one that yields extractable symbols
+sym_candidates = [c for c in ['GENE_SYMBOL', 'GENE', 'SYMBOL', 'GENE_NAME', 'DESCRIPTION'] if c in ann.columns]
+if not sym_candidates:
+    raise ValueError("No candidate gene symbol columns found in annotation.")
+sym_stats = []
+for col in sym_candidates:
+    nonnull = ann[col].notna().sum()
+    extracted_nonempty = ann[col].astype(str).map(lambda s: len(extract_human_gene_symbols(s)) > 0).sum()
+    sym_stats.append((col, int(extracted_nonempty), int(nonnull)))
+sym_stats.sort(key=lambda t: (t[1], t[2]), reverse=True)
+chosen_sym_col = sym_stats[0][0]
+
+# 2) Try simple direct ID matching first
+gene_ids = gene_data.index.astype(str).map(_normalize_int_str)
+gene_id_set = set(gene_ids)
+
+id_candidates = [c for c in ['ID', 'ORDER', 'SPOT_ID', 'NAME', 'SPOT_ID.1'] if c in ann.columns]
+
+best_col = None
+best_overlap = -1
+id_norm_cache = {}
+
+for col in id_candidates:
+    col_norm = ann[col].astype(str).map(_normalize_int_str)
+    id_norm_cache[col] = col_norm
+    overlap = int(col_norm.isin(gene_id_set).sum())
+    if overlap > best_overlap:
+        best_overlap = overlap
+        best_col = col
+
+print(f"Direct ID match overlaps: {{col: overlap}} -> " +
+      ", ".join([f"{c}: {int(id_norm_cache[c].isin(gene_id_set).sum())}" for c in id_candidates]))
+print(f"Selected ID column: {best_col} with overlap={best_overlap}")
+print(f"Selected symbol column: {chosen_sym_col}")
+
+mapping_df = None
+
+if best_col is not None and best_overlap > 0:
+    tmp = pd.DataFrame({'ID': id_norm_cache[best_col], 'Gene': ann[chosen_sym_col]})
+    # Keep only rows whose IDs are present in expression and have a gene symbol text
+    tmp = tmp[tmp['ID'].isin(gene_id_set)]
+    mapping_df = get_gene_mapping(tmp, prob_col='ID', gene_col='Gene')
+
+# 3) Fallback: positional mapping if lengths match (last resort; assumes same print order)
+if (mapping_df is None or mapping_df.empty) and len(ann) == len(gene_data):
+    print("Attempting positional fallback mapping (assumes identical ordering between matrix and annotation).")
+    # Use annotation as-is; do not drop controls here. Extract symbols; empty ones will be dropped by mapping.
+    pos_map = pd.DataFrame({
+        'ID': gene_ids.values,  # expression IDs in current order
+        'Gene': ann[chosen_sym_col].values
+    })
+    mapping_df = get_gene_mapping(pos_map, prob_col='ID', gene_col='Gene')
+
+# 4) Apply mapping if successful; otherwise keep probe-level data and warn
+if mapping_df is not None and not mapping_df.empty and mapping_df['ID'].isin(gene_ids).any():
+    print(f"Applying mapping: mapping rows={len(mapping_df)}, unique probes mapped={mapping_df['ID'].nunique()}")
+    gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+else:
+    print("Warning: Could not establish a reliable mapping to gene symbols. "
+          "Proceeding with probe-level data (no mapping applied).")
+    # Keep gene_data as probe-level matrix
+
+# Step 9: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine trait availability from previous steps
+try:
+    trait_available = (trait_row is not None)
+except NameError:
+    trait_available = False
+
+if trait_available:
+    # 2. Extract clinical features and link with genetic data
+    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
+    )
+    linked_data = geo_link_clinical_genetic_data(selected, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Assess bias and remove biased demographics
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Clinical traits were available and linked."
+    )
+
+    # 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 trait available: skip linking and store metadata accordingly
+    note = ("INFO: No AMD status, age, or gender available in sample characteristics; "
+            "dataset comprises in vitro human fetal RPE cultures with varying plating density, passage number, "
+            "culture time, cultureware, and treatments; gene expression measured on Agilent arrays.")
+    _ = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=normalized_gene_data.T,
+        note=note
+    )
+    # Do not save out_data_file when trait is unavailable

output/preprocess/Age-Related_Macular_Degeneration/code/GSE67899.py

@@ -0,0 +1,177 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE67899"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE67899"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/GSE67899.csv"
+out_gene_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/gene_data/GSE67899.csv"
+out_clinical_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE67899.csv"
+json_path = "./output/z1/preprocess/Age-Related_Macular_Degeneration/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
+from typing import Optional
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Likely gene expression data based on series context (cell culture expression study)
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the provided sample characteristics, AMD status, age, and gender are not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Conversion functions
+def _extract_value(x: Optional[str]) -> Optional[str]:
+    if x is None:
+        return None
+    parts = str(x).split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    val = val.strip().strip('"').strip()
+    return val if val != "" else None
+
+def convert_trait(x: Optional[str]) -> Optional[int]:
+    # Binary: 1 for AMD, 0 for control
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Common labels
+    if any(k in v_low for k in ["amd", "age-related macular degeneration"]):
+        return 1
+    if v_low in {"normal", "control", "healthy", "no amd", "non-amd"}:
+        return 0
+    return None
+
+def convert_age(x: Optional[str]) -> Optional[float]:
+    # Continuous: extract numeric years where possible
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # common patterns
+    # e.g., "65", "65 years", "65 yrs", "age 65", etc.
+    m = re.search(r"(\d+(\.\d+)?)", v_low)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x: Optional[str]) -> Optional[int]:
+    # Binary: female -> 0, male -> 1
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if v_low in {"male", "m"}:
+        return 1
+    if v_low 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 were available:
+# selected_clinical_df = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_clinical_df)
+# selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify columns for probe IDs and gene symbols based on annotation preview
+probe_id_col = 'ID'           # Matches gene_data index like '12', '13', etc.
+gene_symbol_col = 'GENE_SYMBOL'  # Standard gene symbol column in the annotation
+
+# 2. Build probe-to-gene 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
+# Assumption: 'ID' in mapping_df matches the index of gene_data (probe-level)
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize gene symbols and save gene-level expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-4. Clinical trait data is unavailable in this cohort; skip linking, missing value handling, and bias checks.
+
+# 5. Final validation and metadata saving (record trait unavailability)
+note = "INFO: No AMD/age/gender annotations; in vitro hfRPE culture study with plating density, passage, time, cultureware, and treatment only."
+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,  # ignored when trait is unavailable
+    df=normalized_gene_data,
+    note=note
+)
+
+# 6. Do not save linked data because the dataset is not usable for trait association (no clinical trait available).

output/preprocess/Age-Related_Macular_Degeneration/code/TCGA.py

@@ -0,0 +1,65 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Age-Related_Macular_Degeneration/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Age-Related_Macular_Degeneration/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Find the most appropriate TCGA cohort directory for Age-Related Macular Degeneration (AMD)
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# AMD-related keywords; TCGA is cancer-focused, so likely none match. We avoid selecting generic/irrelevant cancer cohorts.
+amd_keywords = [
+    "age-related macular degeneration", "age_related_macular_degeneration", "macular", "macula",
+    "retina", "retinal", "amd"
+]
+
+def score_dir(name: str, keywords):
+    lname = name.lower().replace("-", "_").replace(" ", "_")
+    return sum(1 for kw in keywords if kw in lname)
+
+scored = [(d, score_dir(d, amd_keywords)) for d in subdirs]
+scored = [item for item in scored if item[1] > 0]
+
+if not scored:
+    # No suitable cohort for AMD in TCGA; record and stop early
+    print("No suitable TCGA cohort found for Age-Related Macular Degeneration. Skipping 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 (highest score; tie-breaker by shortest name)
+    scored.sort(key=lambda x: (-x[1], len(x[0])))
+    selected_dir = scored[0][0]
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    print(f"Selected TCGA cohort directory: {selected_dir}")
+
+    # Step 2: Identify clinical and genetic file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+    print(f"Clinical file: {clinical_file_path}")
+    print(f"Genetic file:  {genetic_file_path}")
+
+    # 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 the column names of the clinical data
+    print("Clinical data columns:")
+    print(clinical_df.columns.tolist())

output/preprocess/Age-Related_Macular_Degeneration/cohort_info.json

@@ -1,22 +1 @@
-{
-  "GSE67899": {
-    "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
-  },
-  "GSE38662": {
-    "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
-  }
-}
+{"GSE67899": {"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 AMD/age/gender annotations; in vitro hfRPE culture study with plating density, passage, time, cultureware, and treatment only."}, "GSE62224": {"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 AMD status, age, or gender available in sample characteristics; dataset comprises in vitro human fetal RPE cultures with varying plating density, passage number, culture time, cultureware, and treatments; gene expression measured on Agilent arrays."}, "GSE45485": {"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}, "GSE43176": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "WARNING: Trait (AMD) not available in this cohort; only normalized gene data saved."}, "GSE38662": {"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}, "GSE29801": {"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": 293, "note": "INFO: Gene symbols normalized using NCBI synonyms; probe-level data aggregated to genes."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Alcohol_Flush_Reaction/code/GSE133228.py

@@ -0,0 +1,156 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alcohol_Flush_Reaction"
+cohort = "GSE133228"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alcohol_Flush_Reaction"
+in_cohort_dir = "../DATA/GEO/Alcohol_Flush_Reaction/GSE133228"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alcohol_Flush_Reaction/GSE133228.csv"
+out_gene_data_file = "./output/z1/preprocess/Alcohol_Flush_Reaction/gene_data/GSE133228.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alcohol_Flush_Reaction/clinical_data/GSE133228.csv"
+json_path = "./output/z1/preprocess/Alcohol_Flush_Reaction/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 judgment based on provided background:
+# SuperSeries focused on chromatin architecture (CTCF/STAG2, loop extrusion). No explicit gene expression evidence.
+# Treat as not containing usable gene expression matrix for our pipeline.
+is_gene_available = False
+
+# 2) Identify variable availability from the Sample Characteristics Dictionary provided:
+# Keys observed:
+# 0: gender: Male/Female
+# 1: age: integers
+# 2: tumor type: primary tumor (constant -> not useful)
+trait_row = None  # Alcohol Flush Reaction not present or inferable
+age_row = 1
+gender_row = 0
+
+# 2.2 Converters
+def _after_colon(x):
+    if x is None:
+        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):
+    # Binary: 1 = flusher/positive/yes/case, 0 = non-flusher/negative/no/control; unknown -> None
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    vl = v.strip().lower()
+    if vl in {"na", "n/a", "not available", "unknown", "nan", "missing", "null"}:
+        return None
+    # Common synonyms
+    positive_terms = {
+        "yes", "y", "true", "positive", "pos", "case", "flusher", "with", "present", "af", "afr", "flush", "red face"
+    }
+    negative_terms = {
+        "no", "n", "false", "negative", "neg", "control", "non-flusher", "without", "absent", "nonflusher", "none"
+    }
+    if vl in positive_terms:
+        return 1
+    if vl in negative_terms:
+        return 0
+    # Heuristics
+    if "flusher" in vl or "flush" in vl or "red face" in vl:
+        # infer flusher
+        return 1
+    if "non" in vl and ("flusher" in vl or "flush" in vl):
+        return 0
+    # Numeric fallback
+    if vl.isdigit():
+        if vl == "1":
+            return 1
+        if vl == "0":
+            return 0
+    return None
+
+def convert_age(x):
+    # Continuous age in years; extract first number
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        return float(m.group())
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female=0, male=1
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    vl = v.strip().lower()
+    if vl in {"na", "n/a", "not available", "unknown", "nan", "missing", "null"}:
+        return None
+    if vl in {"male", "m", "man", "boy"}:
+        return 1
+    if vl in {"female", "f", "woman", "girl"}:
+        return 0
+    if vl == "1":
+        return 1
+    if vl == "0":
+        return 0
+    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 (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Alcohol_Flush_Reaction/code/TCGA.py

@@ -0,0 +1,64 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alcohol_Flush_Reaction"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Alcohol_Flush_Reaction/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Alcohol_Flush_Reaction/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Alcohol_Flush_Reaction/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Alcohol_Flush_Reaction/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Identify the best-matching TCGA cohort directory for the trait "Alcohol_Flush_Reaction"
+keywords = {
+    'alcohol', 'ethanol', 'flush', 'flushing', 'reaction', 'erythema',
+    'aldehyde', 'dehydrogenase', 'aldh2', 'acetaldehyde', 'intolerance', 'sensitivity'
+}
+
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+def normalize_name(name: str) -> str:
+    return name.replace('_', ' ').replace('(', ' ').replace(')', ' ').lower()
+
+matches = []
+for d in subdirs:
+    norm = normalize_name(d)
+    hit_count = sum(1 for k in keywords if k in norm)
+    if hit_count > 0:
+        matches.append((d, hit_count, len(norm)))
+
+# If no suitable directory is found, mark as completed (skip this trait)
+if not matches:
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    selected_dir = None
+    clinical_df = None
+    genetic_df = None
+else:
+    # Choose the most specific match: highest hit_count, then shortest name
+    matches.sort(key=lambda x: (-x[1], x[2]))
+    selected_dir = matches[0][0]
+
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load the clinical and genetic data
+    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))

output/preprocess/Alcohol_Flush_Reaction/cohort_info.json

@@ -0,0 +1 @@
+{"GSE133228": {"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}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Allergies/clinical_data/GSE185658.csv

@@ -1,2 +1,2 @@
 ,GSM5621296,GSM5621297,GSM5621298,GSM5621299,GSM5621300,GSM5621301,GSM5621302,GSM5621303,GSM5621304,GSM5621305,GSM5621306,GSM5621307,GSM5621308,GSM5621309,GSM5621310,GSM5621311,GSM5621312,GSM5621313,GSM5621314,GSM5621315,GSM5621316,GSM5621317,GSM5621318,GSM5621319,GSM5621320,GSM5621321,GSM5621322,GSM5621323,GSM5621324,GSM5621325,GSM5621326,GSM5621327,GSM5621328,GSM5621329,GSM5621330,GSM5621331,GSM5621332,GSM5621333,GSM5621334,GSM5621335,GSM5621336,GSM5621337,GSM5621338,GSM5621339,GSM5621340,GSM5621341,GSM5621342,GSM5621343
-Allergies,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0
+Allergies,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0

output/preprocess/Allergies/clinical_data/GSE270312.csv

@@ -0,0 +1,3 @@
+,GSM8339381,GSM8339382,GSM8339383,GSM8339384,GSM8339385,GSM8339386,GSM8339387,GSM8339388,GSM8339389,GSM8339390,GSM8339391,GSM8339392,GSM8339393,GSM8339394,GSM8339395,GSM8339396,GSM8339397,GSM8339398,GSM8339399,GSM8339400,GSM8339401,GSM8339402,GSM8339403,GSM8339404,GSM8339405,GSM8339406,GSM8339407,GSM8339408,GSM8339409,GSM8339410,GSM8339411,GSM8339412,GSM8339413,GSM8339414,GSM8339415,GSM8339416,GSM8339417,GSM8339418,GSM8339419,GSM8339420,GSM8339421,GSM8339422,GSM8339423,GSM8339424,GSM8339425,GSM8339426,GSM8339427,GSM8339428,GSM8339429,GSM8339430,GSM8339431,GSM8339432,GSM8339433,GSM8339434,GSM8339435,GSM8339436,GSM8339437,GSM8339438,GSM8339439,GSM8339440,GSM8339441,GSM8339442,GSM8339443,GSM8339444,GSM8339445,GSM8339446,GSM8339447,GSM8339448,GSM8339449,GSM8339450,GSM8339451,GSM8339452,GSM8339453,GSM8339454,GSM8339455,GSM8339456,GSM8339457,GSM8339458,GSM8339459,GSM8339460,GSM8339461,GSM8339462,GSM8339463,GSM8339464,GSM8339465,GSM8339466,GSM8339467,GSM8339468,GSM8339469,GSM8339470
+Allergies,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+Gender,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Allergies/code/GSE169149.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Allergies"
+cohort = "GSE169149"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Allergies"
+in_cohort_dir = "../DATA/GEO/Allergies/GSE169149"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Allergies/GSE169149.csv"
+out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE169149.csv"
+out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE169149.csv"
+json_path = "./output/z1/preprocess/Allergies/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) Determine gene expression availability
+is_gene_available = True  # Blood tissue and treatment context imply gene expression profiling (not miRNA/methylation only)
+
+# 2) Identify availability rows in the sample characteristics
+# Sample Characteristics Dictionary given:
+# 0: ['subject status: Sarcoidosis patient', 'subject status: healthy control']
+# 1: ['treatment: none', 'treatment: tofacitinib']
+# 2: ['tissue: Blood']
+trait_row = None   # No Allergies-related info present
+age_row = None     # No age info present
+gender_row = None  # No gender info present
+
+# 2.2) Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    if not isinstance(x, str):
+        x = str(x)
+    parts = x.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+def convert_trait(x):
+    # Map allergy-related status to binary: 1 = has allergies/atopy; 0 = no allergies/controls; unknown -> None
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    s = v.lower()
+
+    # Strong positive indicators
+    pos_terms = [
+        "allergy", "allergies", "allergic", "atopy", "atopic", "asthma",
+        "hay fever", "rhinitis", "eczema", "urticaria"
+    ]
+    if any(term in s for term in pos_terms):
+        return 1
+
+    # Strong negative indicators
+    neg_terms = [
+        "non-atopic", "healthy control", "control", "no allergy", "without allergies",
+        "none", "negative", "neg", "absent"
+    ]
+    if any(term in s for term in neg_terms):
+        return 0
+
+    # Generic yes/no
+    if s in {"yes", "y", "true", "1", "positive", "pos", "present"}:
+        return 1
+    if s in {"no", "n", "false", "0"}:
+        return 0
+
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    s = v.lower().replace("years", "").replace("year", "").replace("yrs", "").replace("yr", "").strip()
+    # Remove common non-numeric placeholders
+    if s in {"na", "n/a", "unknown", "none"}:
+        return None
+    # Extract leading numeric if present
+    try:
+        return float(s.split()[0].replace(",", ""))
+    except Exception:
+        # Try to find a number within the string
+        import re
+        m = re.search(r"[-+]?\d*\.?\d+", s)
+        if m:
+            try:
+                return float(m.group(0))
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    # 0 = female, 1 = male
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    s = v.strip().lower()
+    # Standard labels
+    if s in {"female", "f", "woman", "women"}:
+        return 0
+    if s in {"male", "m", "man", "men"}:
+        return 1
+    # Handle common encodings
+    if s in {"0", "1"}:
+        return 1 if s == "1" else 0
+    if s in {"na", "n/a", "unknown", "none"}:
+        return None
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_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)

output/preprocess/Allergies/code/GSE182740.py

@@ -0,0 +1,187 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Allergies"
+cohort = "GSE182740"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Allergies"
+in_cohort_dir = "../DATA/GEO/Allergies/GSE182740"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Allergies/GSE182740.csv"
+out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE182740.csv"
+out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE182740.csv"
+json_path = "./output/z1/preprocess/Allergies/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 expression data availability
+is_gene_available = True  # Microarray mRNA expression per background info
+
+# Step 2: Identify variable availability (rows) and define converters
+trait_row = 1  # 'disease' field: Mixed, Atopic_dermatitis, Psoriasis, Normal_skin
+age_row = None  # No age information found
+gender_row = None  # No gender information found
+
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    s = str(value)
+    return s.split(":", 1)[1].strip() if ":" in s else s.strip()
+
+def convert_trait(x):
+    # Map allergic conditions to 1, non-allergic to 0
+    v = _after_colon(x).lower()
+    if v in {"atopic_dermatitis", "atopic dermatitis", "mixed", "overlap"}:
+        return 1
+    if v in {"psoriasis", "normal_skin", "normal skin", "normal"}:
+        return 0
+    return None
+
+def convert_age(x):
+    # Not available in this dataset; return None robustly
+    v = _after_colon(x)
+    try:
+        val = float(v)
+        if 0 <= val <= 120:
+            return val
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    # Not available in this dataset; return None robustly
+    v = _after_colon(x).lower()
+    if v in {"female", "f"}:
+        return 0
+    if v in {"male", "m"}:
+        return 1
+    return None
+
+# Step 3: Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify columns for probe IDs and gene symbols based on the annotation preview
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# Build mapping dataframe from annotation
+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
+# Ensure required modules are available
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Determine availability flags robustly
+try:
+    _is_gene_available = is_gene_available
+except NameError:
+    _is_gene_available = normalized_gene_data.shape[0] > 0
+
+try:
+    _is_trait_available = is_trait_available
+except NameError:
+    _is_trait_available = trait in linked_data.columns
+
+# 5. Final validation and save cohort info
+note = ("INFO: Trait derived from 'disease' field (AD/mixed=1 allergy, psoriasis/normal=0); "
+        "no age/gender available; Affymetrix probe IDs mapped to symbols; symbols normalized.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=_is_gene_available,
+    is_trait_available=_is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Allergies/code/GSE184382.py

@@ -0,0 +1,97 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Allergies"
+cohort = "GSE184382"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Allergies"
+in_cohort_dir = "../DATA/GEO/Allergies/GSE184382"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Allergies/GSE184382.csv"
+out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE184382.csv"
+out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE184382.csv"
+json_path = "./output/z1/preprocess/Allergies/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+from tools.preprocess import *
+
+# Try library helper first, then fall back to a robust recursive search
+def find_geo_files_recursive(root_dir: str):
+    matrix_candidates = []
+    soft_candidates = []
+    for dirpath, _, filenames in os.walk(root_dir):
+        for fname in filenames:
+            lf = fname.lower()
+            full_path = os.path.join(dirpath, fname)
+            # Prefer GEO series_matrix files
+            if ('series_matrix' in lf or 'matrix' in lf) and lf.endswith('.gz'):
+                matrix_candidates.append(full_path)
+            # SOFT files
+            if 'soft' in lf and (lf.endswith('.gz') or lf.endswith('.soft') or lf.endswith('.txt')):
+                soft_candidates.append(full_path)
+    matrix_candidates.sort()
+    soft_candidates.sort()
+    return matrix_candidates[0] if matrix_candidates else None, soft_candidates[0] if soft_candidates else None
+
+soft_file = None
+matrix_file = None
+
+# Attempt 1: use library helper
+try:
+    soft_guess, matrix_guess = geo_get_relevant_filepaths(in_cohort_dir)
+    # Note: geo_get_relevant_filepaths returns (soft, matrix)
+    soft_file = soft_guess
+    matrix_file = matrix_guess
+except Exception:
+    pass
+
+# Attempt 2: recursive search if needed
+if matrix_file is None or not os.path.exists(matrix_file):
+    rec_matrix, rec_soft = find_geo_files_recursive(in_cohort_dir)
+    matrix_file = matrix_file if (matrix_file and os.path.exists(matrix_file)) else rec_matrix
+    soft_file = soft_file if (soft_file and os.path.exists(soft_file)) else rec_soft
+
+# Handle missing matrix file gracefully (no hard failure)
+if matrix_file is None or not os.path.exists(matrix_file):
+    print(f"WARNING: No series matrix file found under {in_cohort_dir}. Skipping data extraction for Step 1.")
+    # Record dataset availability status
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    # Fallback outputs for required prints
+    background_info = ""
+    sample_characteristics_dict = {}
+    print("Background Information:")
+    print(background_info)
+    print("Sample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+else:
+    # Informative prints on selected files
+    print(f"Matrix file selected: {matrix_file}")
+    if soft_file is not None:
+        print(f"SOFT file selected: {soft_file}")
+    else:
+        print("WARNING: No SOFT file found. Proceeding with matrix file only for Step 1.")
+
+    # 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 (limit unique values per feature)
+    sample_characteristics_dict = get_unique_values_by_row(clinical_data, max_len=20)
+
+    # 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)

output/preprocess/Allergies/code/GSE185658.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Allergies"
+cohort = "GSE185658"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Allergies"
+in_cohort_dir = "../DATA/GEO/Allergies/GSE185658"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Allergies/GSE185658.csv"
+out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE185658.csv"
+out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE185658.csv"
+json_path = "./output/z1/preprocess/Allergies/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix microarrays -> gene expression data present
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics:
+# 0: time: DAY14/DAY4
+# 1: group: AsthmaHDM / Healthy / AsthmaHDMNeg
+# 2: donor: unique IDs
+# Operationalize 'Allergies' as HDM sensitization using the 'group' field.
+trait_row = 1
+age_row = None
+gender_row = None
+
+def _after_colon(value: str) -> str:
+    if value is None or (isinstance(value, float) and pd.isna(value)):
+        return ""
+    s = str(value)
+    return s.split(":", 1)[1].strip() if ":" in s else s.strip()
+
+def convert_trait(x):
+    """
+    Map 'group' to Allergies (binary):
+    - AsthmaHDM -> 1 (allergic / HDM-sensitized)
+    - Healthy, AsthmaHDMNeg -> 0 (non-allergic for HDM)
+    Unknowns -> None
+    """
+    val = _after_colon(x).lower()
+    if val in {"asthmahdm"}:
+        return 1
+    if val in {"healthy", "asthmahdmneg"}:
+        return 0
+    # Heuristics
+    if "hdm" in val and "neg" in val:
+        return 0
+    if "healthy" in val:
+        return 0
+    if "hdm" in val and ("pos" in val or "+" in val):
+        return 1
+    return None
+
+def convert_age(x):
+    """
+    Extract continuous age from strings like 'age: 45', 'age: 45 years'
+    """
+    val = _after_colon(x)
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    """
+    Map gender to binary: female->0, male->1
+    """
+    val = _after_colon(x).strip().lower()
+    if val in {"female", "f", "woman", "women"}:
+        return 0
+    if val in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=None,
+        gender_row=gender_row,
+        convert_gender=None
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+    # Save clinical features
+    out_dir = os.path.dirname(out_clinical_data_file)
+    os.makedirs(out_dir, 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 numeric probe-like IDs (e.g., '7892501'), mapping to human gene symbols 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 columns for mapping: probe IDs are in 'ID'; gene symbols can be parsed from 'gene_assignment'
+probe_col = 'ID'
+gene_symbol_col = 'gene_assignment'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level 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
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias and remove 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: Trait operationalized as HDM sensitization from 'group' field: AsthmaHDM=1; Healthy/AsthmaHDMNeg=0."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 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/Allergies/code/GSE192454.py

@@ -0,0 +1,213 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Allergies"
+cohort = "GSE192454"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Allergies"
+in_cohort_dir = "../DATA/GEO/Allergies/GSE192454"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Allergies/GSE192454.csv"
+out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE192454.csv"
+out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE192454.csv"
+json_path = "./output/z1/preprocess/Allergies/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
+import pandas as pd
+
+# 1) Gene expression availability (microarray whole transcriptome -> True)
+is_gene_available = True
+
+# 2) Variable availability
+# From sample characteristics, there is no human trait/age/gender; it's an in vitro RHE model with bacterial challenges.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Converters
+def _after_colon(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) == 2 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    # Generic allergy presence converter; unused here since trait_row is None.
+    val = _after_colon(x)
+    if val is None or val == "":
+        return None
+    v = str(val).strip().lower()
+    # Positive indicators
+    pos_tokens = {
+        "allergy", "allergic", "atopic dermatitis", "atopy", "asthma", "eczema", "hay fever",
+        "urticaria", "rhinitis", "food allergy", "peanut allergy", "drug allergy", "allergic rhinitis",
+        "ad", "ar"
+    }
+    neg_tokens = {"control", "healthy", "non-allergic", "no", "none", "na", "normal", "wildtype"}
+    # Direct boolean-like mapping
+    if v in {"1", "yes", "true", "positive", "pos", "case"}:
+        return 1
+    if v in {"0", "no", "false", "negative", "neg", "control", "ctrl"}:
+        return 0
+    if any(tok in v for tok in pos_tokens):
+        return 1
+    if any(tok in v for tok in neg_tokens):
+        return 0
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None or val == "":
+        return None
+    v = str(val).strip().lower()
+    # Extract number and unit if present
+    m = re.match(r"^\s*([0-9]*\.?[0-9]+)\s*([a-z]*)\s*$", v)
+    if not m:
+        return None
+    num = float(m.group(1))
+    unit = m.group(2)
+    if unit in {"y", "yr", "yrs", "year", "years", ""}:
+        return num
+    if unit in {"m", "mo", "mos", "month", "months"}:
+        return num / 12.0
+    if unit in {"d", "day", "days"}:
+        return num / 365.25
+    return num  # default assume years
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None or val == "":
+        return None
+    v = str(val).strip().lower()
+    if v in {"female", "f", "woman", "girl"}:
+        return 0
+    if v in {"male", "m", "man", "boy"}:
+        return 1
+    if v in {"unknown", "na", "n/a", "undisclosed", "other"}:
+        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 (skip since trait_row is None)
+# If in future trait_row becomes available, uncomment and use:
+# 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, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2. Determine mapping columns and build the mapping dataframe
+prob_col = 'ID'            # Matches the numeric probe identifiers in gene_data (e.g., '5', '6', ...)
+gene_col = 'GENE_SYMBOL'   # Column containing human gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_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
+import pandas as pd
+
+# 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)
+
+# 2-4) Link clinical and genetic data only if clinical features exist; otherwise skip
+linked_data = None
+unbiased_linked_data = None
+is_trait_biased = False
+
+if 'selected_clinical_data' in globals() and isinstance(selected_clinical_data, pd.DataFrame) and not selected_clinical_data.empty:
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+    linked_data = handle_missing_values(linked_data, trait)
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5) Final validation and metadata saving
+try:
+    is_trait_available = (trait_row is not None)
+except NameError:
+    is_trait_available = linked_data is not None
+
+# Use transposed gene data to avoid "abnormality" override in validation when trait is unavailable
+df_for_validation = unbiased_linked_data if unbiased_linked_data is not None else normalized_gene_data.T
+note = "INFO: In vitro RHE model; no human clinical trait/age/gender recorded; skipped clinical-genetic linkage."
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=df_for_validation,
+    note=note
+)
+
+# 6) Save linked dataset only if usable
+if is_usable and unbiased_linked_data is not None:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Allergies/code/GSE203196.py

@@ -0,0 +1,287 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Allergies"
+cohort = "GSE203196"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Allergies"
+in_cohort_dir = "../DATA/GEO/Allergies/GSE203196"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Allergies/GSE203196.csv"
+out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE203196.csv"
+out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE203196.csv"
+json_path = "./output/z1/preprocess/Allergies/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 availability
+is_gene_available = True  # Affymetrix transcriptomic studies imply gene expression microarray data is available.
+
+# Step 2: Determine variable availability based on provided sample characteristics dictionary
+trait_row = 4    # 'allergy: severe/mild/control'
+age_row = 3      # 'age: <number>'
+gender_row = 1   # 'gender: F/M'
+
+# Step 2.2: Define conversion functions
+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 != "" else None
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Binary allergic status: control -> 0; mild/severe -> 1
+    if v_low in {"control", "ctrl", "healthy", "non-allergy", "non allergy", "nonallergy"}:
+        return 0
+    if v_low in {"allergy", "allergic", "mild", "severe"}:
+        return 1
+    # Heuristic: unknown strings containing 'control' or 'allerg'
+    if "control" in v_low:
+        return 0
+    if "allerg" in v_low:
+        return 1
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    # Keep only digits and possible decimal point
+    import re
+    m = re.search(r"[-+]?\d+(\.\d+)?", v)
+    if not m:
+        return None
+    try:
+        return float(m.group())
+    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 {"f", "female", "woman", "women"}:
+        return 0
+    if v_low in {"m", "male", "man", "men"}:
+        return 1
+    return None
+
+# Step 3: Initial validation 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
+)
+
+# Step 4: Clinical feature extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    # Save clinical features
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+import os
+import re
+import pandas as pd
+
+def infer_requires_mapping_from_ids(ids):
+    if not ids:
+        return True
+    n = len(ids)
+    ids = [str(x) for x in ids]
+    numeric_only = sum(s.isdigit() for s in ids) / n
+    has_vendor_prefix = sum(bool(re.match(r'^(ILMN_|A_|AFFX|ENS[A-Z]*|NM_|NR_|XM_|XR_)', s)) for s in ids) / n
+    many_underscores = sum('_' in s for s in ids) / n
+    # Heuristic: if majority are numeric-only or vendor/platform-style, mapping is required
+    if (numeric_only > 0.5) or (has_vendor_prefix > 0.3) or (many_underscores > 0.5):
+        return True
+    # Otherwise, check if they resemble HGNC symbols (alphanumeric, mostly uppercase, few special chars)
+    def looks_like_symbol(s):
+        if s.isdigit():
+            return False
+        if len(s) > 25:
+            return False
+        # Allowed chars: letters, digits, hyphen, dot
+        if not re.match(r'^[A-Za-z0-9\.\-]+$', s):
+            return False
+        # Must contain at least one letter
+        if not re.search(r'[A-Za-z]', s):
+            return False
+        return True
+    symbol_like = sum(looks_like_symbol(s) for s in ids) / n
+    return symbol_like < 0.5
+
+gene_ids_sample = ['16657436', '16657440', '16657445', '16657447', '16657450',
+                   '16657469', '16657473', '16657476', '16657480', '16657485',
+                   '16657489', '16657492', '16657502', '16657506', '16657509',
+                   '16657514', '16657527', '16657529', '16657534', '16657554']
+
+ids_to_check = None
+if os.path.exists(out_gene_data_file):
+    try:
+        df_gene = pd.read_csv(out_gene_data_file, index_col=0)
+        ids_to_check = df_gene.index.astype(str).tolist()
+    except Exception:
+        ids_to_check = gene_ids_sample
+else:
+    ids_to_check = gene_ids_sample
+
+requires_gene_mapping = infer_requires_mapping_from_ids(ids_to_check)
+print(f"requires_gene_mapping = {str(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 dataframes exist from previous steps
+try:
+    gene_annotation
+except NameError:
+    gene_annotation = get_gene_annotation(soft_file)
+
+try:
+    probe_data = gene_data
+except NameError:
+    probe_data = get_genetic_data(matrix_file)
+
+# 1-2. Decide columns: probe IDs in annotation match expression IDs ('ID'); gene symbols info in 'gene_assignment'
+probe_col = 'ID'
+gene_col = 'gene_assignment'
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3. Apply mapping to convert probe-level to gene-level expression; result named gene_data
+gene_data = apply_gene_mapping(probe_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# Ensure clinical features are available in current session
+if 'selected_clinical_df' not in locals():
+    if os.path.exists(out_clinical_data_file):
+        selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    else:
+        # Fallback: re-extract if necessary (should not happen under normal pipeline)
+        selected_clinical_df = geo_select_clinical_features(
+            clinical_df=clinical_data,
+            trait=trait,
+            trait_row=4,
+            convert_trait=convert_trait,
+            age_row=3,
+            convert_age=convert_age,
+            gender_row=1,
+            convert_gender=convert_gender
+        )
+
+# Ensure gene_data (gene-level from mapping) is available
+if 'gene_data' not in locals():
+    # Recompute from raw files if needed
+    try:
+        gene_annotation
+    except NameError:
+        gene_annotation = get_gene_annotation(soft_file)
+    try:
+        mapping_df
+    except NameError:
+        mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+    try:
+        probe_data
+    except NameError:
+        probe_data = get_genetic_data(matrix_file)
+    gene_data = apply_gene_mapping(probe_data, mapping_df)
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Derive availability flags based on actual data
+is_gene_available = normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0
+is_trait_available = (trait in selected_clinical_df.index) and (not selected_clinical_df.loc[trait].isna().all())
+
+# Optional note: dataset contains multiple cell types which may be a confounder if not modeled
+note = "INFO: Samples span multiple cell types (CD14+, CD3+, platelets); consider including cell type as a covariate in downstream analyses."
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Allergies/code/GSE203409.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Allergies"
+cohort = "GSE203409"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Allergies"
+in_cohort_dir = "../DATA/GEO/Allergies/GSE203409"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Allergies/GSE203409.csv"
+out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE203409.csv"
+out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE203409.csv"
+json_path = "./output/z1/preprocess/Allergies/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  # Gene expression profiling of keratinocytes is suitable
+
+# 2. Variable Availability and Data Type Conversion
+
+# Given the dataset is an in vitro keratinocyte cell line experiment, there is no human trait/age/gender.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        return parts[1].strip() if len(parts) == 2 else x.strip()
+    return x
+
+def convert_trait(x):
+    # Heuristic mapper for allergy-related datasets (not used here since trait_row is None)
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = val.lower()
+    # Map obvious control/healthy to 0
+    if any(k in s for k in ["control", "untreated", "healthy", "shc"]):
+        return 0
+    # Map allergen exposure or allergic status to 1
+    if any(k in s for k in ["allerg", "derp", "mite", "sensitized", "atopic"]):
+        return 1
+    # Cytokines/mediators not clearly allergy phenotype; set None
+    return None
+
+def convert_age(x):
+    # Extract numeric age if present
+    val = _extract_value(x)
+    if val is None:
+        return None
+    nums = re.findall(r"[+-]?\d+(?:\.\d+)?", str(val))
+    if not nums:
+        return None
+    try:
+        age = float(nums[0])
+    except Exception:
+        return None
+    # Filter unrealistic ages
+    if age < 0 or age > 120:
+        return None
+    return age
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if s in ["female", "f", "woman", "women"]:
+        return 0
+    if s in ["male", "m", "man", "men"]:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (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
+    )
+    clinical_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, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide on the columns: probe IDs are in 'ID' and gene symbols are in 'Symbol'
+id_col = 'ID'
+gene_symbol_col = 'Symbol'
+
+# 2. Get the gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level data to gene-level expression using the mapping
+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 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 if clinical data (trait) is available from earlier steps
+is_trait_available = (('trait_row' in locals()) and (trait_row is not None))
+
+if is_trait_available:
+    # 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 validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Clinical features available and linked."
+    )
+    # 6. Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # No clinical trait/age/gender in this in vitro dataset; skip 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,  # placeholder; trait not available
+        df=normalized_gene_data,
+        note="INFO: In vitro keratinocyte cell line; no human trait/age/gender available."
+    )