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.gitattributes

@@ -2178,3 +2178,15 @@ p3/preprocess/Prostate_Cancer/gene_data/GSE201805.csv filter=lfs diff=lfs merge=
 p3/preprocess/lower_grade_glioma_and_glioblastoma/TCGA.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Prostate_Cancer/GSE209954.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Obesity/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Coronary_artery_disease/gene_data/GSE120774.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE48801.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Coronary_artery_disease/GSE109048.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Heart_rate/GSE34788.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Heart_rate/gene_data/GSE34788.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Height/GSE106800.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Height/gene_data/GSE106800.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Height/gene_data/GSE101710.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Hemochromatosis/gene_data/GSE50579.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Huntingtons_Disease/GSE26927.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Hypertension/GSE117261.csv filter=lfs diff=lfs merge=lfs -text
+output/preprocess/Crohns_Disease/GSE123086.csv filter=lfs diff=lfs merge=lfs -text

output/preprocess/Atrial_Fibrillation/code/TCGA.py

@@ -0,0 +1,56 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atrial_Fibrillation"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Atrial_Fibrillation/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Atrial_Fibrillation/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Atrial_Fibrillation/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Atrial_Fibrillation/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Discover available TCGA cohort directories
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Try to find a cohort relevant to atrial fibrillation (unlikely in TCGA cancer cohorts)
+keywords = ['atrial_fibrillation', 'atrial fibrillation', 'a-fib', 'afib', 'arrhythmia', 'cardiac', 'heart']
+candidates = []
+for d in subdirs:
+    name_l = d.lower()
+    score = sum(1 for k in keywords if k in name_l)
+    if score > 0:
+        candidates.append((score, d))
+
+selected_dir = None
+if candidates:
+    # Choose the highest scoring (most specific) match
+    candidates.sort(key=lambda x: (-x[0], len(x[1])))
+    selected_dir = candidates[0][1]
+
+if selected_dir is None:
+    # No suitable TCGA cohort for Atrial Fibrillation; record and skip
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print("No suitable TCGA cohort found for the trait. Skipping TCGA processing for this trait.")
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_path, sep='\t', index_col=0, low_memory=False)
+
+    print(clinical_df.columns.tolist())

output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE111175.csv

@@ -1,3 +1,3 @@
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output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE113842.csv

@@ -1,3 +1,3 @@
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output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE123302.csv

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output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE87847.csv

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output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE89594.csv

@@ -0,0 +1,4 @@
+,GSM2384988,GSM2384989,GSM2384990,GSM2384991,GSM2384992,GSM2384993,GSM2384994,GSM2384995,GSM2384996,GSM2384997,GSM2384998,GSM2384999,GSM2385000,GSM2385001,GSM2385002,GSM2385003,GSM2385004,GSM2385005,GSM2385006,GSM2385007,GSM2385008,GSM2385009,GSM2385010,GSM2385011,GSM2385012,GSM2385013,GSM2385014,GSM2385015,GSM2385016,GSM2385017,GSM2385018,GSM2385019,GSM2385020,GSM2385021,GSM2385022,GSM2385023,GSM2385024,GSM2385025,GSM2385026,GSM2385027,GSM2385028,GSM2385029,GSM2385030,GSM2385031,GSM2385032,GSM2385033,GSM2385034,GSM2385035,GSM2385036,GSM2385037,GSM2385038,GSM2385039,GSM2385040,GSM2385041,GSM2385042,GSM2385043,GSM2385044,GSM2385045,GSM2385046,GSM2385047,GSM2385048,GSM2385049,GSM2385050,GSM2385051,GSM2385052,GSM2385053,GSM2385054,GSM2385055,GSM2385056,GSM2385057,GSM2385058,GSM2385059,GSM2385060,GSM2385061,GSM2385062,GSM2385063,GSM2385064,GSM2385065,GSM2385066,GSM2385067,GSM2385068,GSM2385069,GSM2385070,GSM2385071,GSM2385072,GSM2385073,GSM2385074,GSM2385075,GSM2385076,GSM2385077,GSM2385078,GSM2385079,GSM2385080,GSM2385081
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+Gender,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0

output/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE111175.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+cohort = "GSE111175"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
+in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE111175"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/GSE111175.csv"
+out_gene_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE111175.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE111175.csv"
+json_path = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1. Gene expression data availability
+is_gene_available = True  # Leukocyte gene expression/transcriptomics per series description
+
+# 2. Variable availability and converters
+trait_row = 3  # 'diagnosis'
+age_row = 2    # 'age (months)'
+gender_row = None  # 'gender' is constant (all 'M'), so not useful
+
+def convert_trait(x):
+    if x is None:
+        return None
+    v = str(x)
+    if ':' in v:
+        v = v.split(':', 1)[1]
+    v = v.strip().lower()
+    # Map to ASD binary: ASD and PDDNOS considered ASD (1); others mapped to non-ASD (0)
+    if v in {'asd', 'pddnos'}:
+        return 1
+    if v in {'td', 'ld', 'preemienodelay', 'autfeat', 'control', 'typical', 'typical development', 'typical_development'}:
+        return 0
+    return None
+
+def convert_age(x):
+    if x is None:
+        return None
+    v = str(x)
+    if ':' in v:
+        v = v.split(':', 1)[1]
+    v = v.strip()
+    try:
+        return float(v)  # months as continuous numeric
+    except Exception:
+        return None
+
+def convert_gender(x):
+    if x is None:
+        return None
+    v = str(x)
+    if ':' in v:
+        v = v.split(':', 1)[1]
+    v = v.strip().lower()
+    if v in {'male', 'm'}:
+        return 1
+    if v in {'female', 'f'}:
+        return 0
+    return None
+
+# 3. Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical feature extraction (only if 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=None
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, 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
+# Identify the appropriate columns for probe IDs and gene symbols based on the preview:
+# Probe ID column: 'ID' (matches ILMN_* identifiers in gene_data)
+# Gene symbol column: 'Symbol'
+
+# 1-2. Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Ensure clinical data is available in memory; otherwise reload from disk
+if 'selected_clinical_df' not in globals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+processed_linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(processed_linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available = (trait in linked_data.columns)
+
+note = (
+    "INFO: Gender unavailable/constant (all male) per series; "
+    "Age recorded in months; Probe IDs mapped to gene symbols and normalized via NCBI synonyms."
+)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    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/Autism_spectrum_disorder_(ASD)/code/GSE113842.py

@@ -0,0 +1,362 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+cohort = "GSE113842"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
+in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE113842"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/GSE113842.csv"
+out_gene_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE113842.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE113842.csv"
+json_path = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+
+# 1) Determine data availability
+is_gene_available = True  # RNA fractions and hybridization batch strongly suggest gene expression microarray data
+
+# 2) Variable availability
+trait_row = 0  # 'group: ASD' vs 'group: CTRL'
+age_row = 2    # 'age: ...'
+gender_row = None  # No gender info present
+
+# 2.2) Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return str(x)
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if 'asd' in v or 'autism' in v:
+        return 1
+    if 'ctrl' in v or 'control' in v or 'neurotypical' in v or 'healthy' in v:
+        return 0
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = str(val).strip().lower()
+    for sep in ['/', '-', ',']:
+        v = v.replace(sep, ' ')
+    parts = [p for p in v.split() if p.replace('.', '', 1).isdigit()]
+    if not parts:
+        return None
+    nums = [float(p) for p in parts]
+    return sum(nums) / len(nums)
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = str(val).strip().lower()
+    if v in {'male', 'm', 'man', 'boy'}:
+        return 1
+    if v in {'female', 'f', 'woman', 'girl'}:
+        return 0
+    return None
+
+# 3) Save initial metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait 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
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print("Clinical preview:", clinical_preview)  # quick inspection aid
+    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
+# Affymetrix probe set IDs detected (e.g., 11715100_at, _s_at, _x_at), 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 columns for probe ID and gene symbol based on annotation preview:
+# - Probe identifiers: 'ID' (e.g., '1415670_PM_at')
+# - Gene symbols: 'Gene Symbol' (e.g., 'Copg', 'Atp6v0d1', ...)
+# Extract mapping and apply to convert probe-level data to gene-level expression
+
+# 1-2) Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Make symbols uppercase to improve extraction of gene tokens in downstream helper
+mapping_df['Gene'] = mapping_df['Gene'].astype(str).str.upper()
+
+# 3) Apply mapping to aggregate probes into genes
+probe_data = gene_data  # keep original probe-level data
+gene_data = apply_gene_mapping(probe_data, 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)
+
+# Load clinical data from previous step (ensure availability in current execution context)
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 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_flag = normalized_gene_data.shape[0] > 0
+is_trait_available_flag = trait in selected_clinical_df.index
+
+note_parts = []
+if not is_gene_available_flag:
+    note_parts.append("WARNING: Normalized gene data is empty after human gene symbol mapping; "
+                      "platform annotation suggests Mus musculus, causing symbol mismatch.")
+if 'Gender' not in selected_clinical_df.index:
+    note_parts.append("INFO: Gender not available in clinical annotations.")
+note = " ".join(note_parts) if note_parts else ""
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_flag,
+    is_trait_available=is_trait_available_flag,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. 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)
+
+# Step 8: 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 9: Gene Identifier Mapping
+import os
+import pandas as pd
+
+# Reload probe-level data to ensure correct overlap assessment
+probe_data = get_genetic_data(matrix_file)
+
+# Identify all SOFT files and select the annotation with maximal probe-ID overlap (prefer Homo sapiens when possible)
+soft_files = [f for f in os.listdir(in_cohort_dir) if 'soft' in f.lower()]
+best_soft_path = None
+best_overlap = -1
+best_annotation = None
+
+for sf in soft_files:
+    sf_path = os.path.join(in_cohort_dir, sf)
+    try:
+        ann = get_gene_annotation(sf_path)
+        if 'ID' not in ann.columns:
+            continue
+
+        # Prefer Homo sapiens annotation if available
+        candidates = []
+        if 'Species Scientific Name' in ann.columns:
+            ann_hs = ann[ann['Species Scientific Name'].astype(str).str.contains('Homo sapiens', case=False, na=False)]
+            if not ann_hs.empty:
+                candidates.append(ann_hs)
+        candidates.append(ann)
+
+        for cand in candidates:
+            overlap = pd.Index(cand['ID'].astype(str)).intersection(probe_data.index).size
+            if overlap > best_overlap:
+                best_overlap = overlap
+                best_soft_path = sf_path
+                best_annotation = cand
+    except Exception:
+        continue
+
+# Fallback to previously loaded annotation if needed
+if best_annotation is None:
+    best_annotation = gene_annotation
+
+# Determine the gene symbol column
+symbol_col = None
+for col in best_annotation.columns:
+    if col.strip().lower() == 'gene symbol':
+        symbol_col = col
+        break
+if symbol_col is None:
+    # Heuristic fallbacks
+    if 'Target Description' in best_annotation.columns:
+        symbol_col = 'Target Description'
+    else:
+        candidates = [c for c in best_annotation.columns if 'symbol' in c.lower()]
+        symbol_col = candidates[0] if candidates else best_annotation.columns[0]
+
+# Build mapping dataframe from the chosen annotation
+mapping_df = get_gene_mapping(best_annotation, prob_col='ID', gene_col=symbol_col)
+# Normalize gene tokens for robust extraction
+mapping_df['Gene'] = mapping_df['Gene'].astype(str).str.replace('///', ' ', regex=False).str.upper()
+
+# Apply mapping to aggregate probe-level measurements into gene-level expression
+gene_data = apply_gene_mapping(probe_data, mapping_df)
+
+# Step 10: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Normalize gene symbols and save normalized gene expression
+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)
+
+# Load clinical data from previous step to ensure availability in current context
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2) Link clinical and genetic data (conditionally, depending on whether gene data is available)
+is_gene_available_flag = normalized_gene_data.shape[0] > 0
+is_trait_available_flag = trait in selected_clinical_df.index
+
+def handle_missing_values_clinical_only(df: pd.DataFrame, trait_col: str) -> pd.DataFrame:
+    # Drop samples with missing trait
+    df = df.dropna(subset=[trait_col])
+    # Impute Age
+    if 'Age' in df.columns:
+        if df['Age'].notna().sum() > 0:
+            df['Age'] = df['Age'].fillna(df['Age'].mean())
+    # Impute Gender
+    if 'Gender' in df.columns:
+        mode_result = df['Gender'].mode()
+        if len(mode_result) > 0:
+            df['Gender'] = df['Gender'].fillna(mode_result[0])
+        else:
+            df = df.drop(columns='Gender')
+    return df
+
+if is_trait_available_flag and is_gene_available_flag:
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+    # 3) Handle missing values on linked data with genes
+    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)
+elif is_trait_available_flag:
+    # Clinical-only fallback: transpose to samples-as-rows
+    clinical_only = selected_clinical_df.T
+    clinical_only = handle_missing_values_clinical_only(clinical_only, trait)
+    # Assess bias on clinical-only data
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(clinical_only, trait)
+else:
+    # No trait available; create empty placeholders
+    linked_data = pd.DataFrame()
+    is_trait_biased, unbiased_linked_data = (False, linked_data)
+
+# 5) Final validation and cohort info with diagnostics
+note_parts = []
+if not is_gene_available_flag:
+    note_parts.append("WARNING: Normalized gene data is empty after mapping; possible species/platform mismatch (e.g., Mus musculus annotation).")
+if 'Gender' not in selected_clinical_df.index:
+    note_parts.append("INFO: Gender not available in clinical annotations.")
+
+# Add diagnostic about probe-annotation overlap and resulting gene_data size
+try:
+    probe_data_diag = get_genetic_data(matrix_file)
+    ann_diag = get_gene_annotation(soft_file)
+    if 'ID' in ann_diag.columns:
+        overlap = pd.Index(ann_diag['ID'].astype(str)).intersection(probe_data_diag.index).size
+        note_parts.append(f"DEBUG: Probe-annotation overlap={overlap}/{len(probe_data_diag)}; normalized gene rows={normalized_gene_data.shape[0]}.")
+    else:
+        note_parts.append(f"DEBUG: Annotation lacks 'ID' column; normalized gene rows={normalized_gene_data.shape[0]}.")
+except Exception as _e:
+    note_parts.append(f"DEBUG: Diagnostics failed; normalized gene rows={normalized_gene_data.shape[0]}.")
+
+note = " ".join(note_parts) if note_parts else ""
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_flag,
+    is_trait_available=is_trait_available_flag,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6) Save usable linked data
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE123302.py

@@ -0,0 +1,382 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+cohort = "GSE123302"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
+in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE123302"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/GSE123302.csv"
+out_gene_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE123302.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE123302.csv"
+json_path = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability (Affymetrix Human Gene 2.0 array => gene expression microarray)
+is_gene_available = True
+
+# 2) Variable availability and conversion
+# From the Sample Characteristics Dictionary:
+# 0: diagnosis (TD, Non-TD, ASD) -> trait
+# 1: Sex (male, female) -> gender
+# No age available
+trait_row = 0
+age_row = None
+gender_row = 1
+
+def _after_colon(x):
+    if pd.isna(x):
+        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 v is None:
+        return None
+    low = v.lower().strip()
+    # Map ASD to 1; Non-TD and TD to 0
+    if low == "asd":
+        return 1
+    if low in {"td", "non-td", "non td"}:
+        return 0
+    # Heuristics
+    if "asd" in low and not any(tok in low for tok in ["non", "not"]):
+        return 1
+    if "td" in low or "non" in low:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Extract first number (years assumed if not specified)
+    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 = _after_colon(x)
+    if v is None:
+        return None
+    low = v.lower().strip()
+    if low in {"male", "m"}:
+        return 1
+    if 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 (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("Clinical features preview:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import os
+import io
+import re
+import gzip
+import pandas as pd
+
+# Helper: pick gene symbol-like column
+def pick_gene_symbol_column(df: pd.DataFrame) -> str | None:
+    preferred_exact = [
+        'Gene Symbol', 'GENE_SYMBOL', 'gene_symbol', 'SYMBOL', 'ENTREZ_GENE_SYMBOL',
+        'Gene Symbol(s)', 'Gene symbol', 'GENE_SYMBOLS', 'entrez_gene_symbol',
+        'GENE_SYMBOLS_CHIP', 'Gene.Symbol'
+    ]
+    for c in preferred_exact:
+        if c in df.columns:
+            return c
+
+    # Common columns that embed symbols in descriptive text
+    alternatives = [
+        'gene_assignment', 'GENE_ASSIGNMENT', 'mrna_assignment',
+        'Gene Title', 'gene_title', 'Gene Name', 'Associated Gene Name',
+        'Associated Genes', 'Associated Gene', 'DESCRIPTION', 'desc'
+    ]
+    for c in alternatives:
+        if c in df.columns:
+            return c
+
+    # Pattern-based search
+    for c in df.columns:
+        if re.search(r'gene.*symbol|^symbol$|symbols?$', c, flags=re.I):
+            return c
+    return None
+
+# Helper: read GPL platform table between !platform_table_begin and !platform_table_end
+def read_gpl_platform_table(soft_path: str) -> pd.DataFrame | None:
+    opener = gzip.open if soft_path.lower().endswith('.gz') else open
+    with opener(soft_path, 'rt', errors='ignore') as f:
+        capturing = False
+        buf_lines = []
+        for line in f:
+            line = line.rstrip('\n')
+            if line.startswith('!platform_table_begin'):
+                capturing = True
+                buf_lines.clear()
+                continue
+            if line.startswith('!platform_table_end'):
+                capturing = False
+                break
+            if capturing:
+                buf_lines.append(line)
+    if not buf_lines:
+        return None
+    text = '\n'.join(buf_lines)
+    try:
+        df = pd.read_csv(io.StringIO(text), sep='\t', dtype=str, low_memory=False, on_bad_lines='skip')
+        return df
+    except Exception:
+        return None
+
+# Helper: find GPL soft files recursively
+def find_gpl_soft_files(root_dir: str) -> list[str]:
+    paths = []
+    for dirpath, _, filenames in os.walk(root_dir):
+        for fn in filenames:
+            low = fn.lower()
+            if ('gpl' in low) and ('soft' in low):
+                paths.append(os.path.join(dirpath, fn))
+    return paths
+
+# 1) Decide identifier and symbol columns from current annotation first (may not have symbols)
+id_col = 'ID'  # probe/feature IDs seen in gene_data index
+
+# Try to use the existing gene_annotation first
+gene_col = pick_gene_symbol_column(gene_annotation)
+
+platform_df = None
+platform_used_path = None
+
+# If not found, search GPL files across the entire trait directory
+if gene_col is None:
+    gpl_files = find_gpl_soft_files(in_trait_dir)
+    # Prioritize files that look like platform annotation
+    for gpl in gpl_files:
+        df_plat = read_gpl_platform_table(gpl)
+        if df_plat is None or df_plat.shape[1] < 2:
+            continue
+
+        # Choose ID column by maximizing overlap with our probe IDs
+        candidates_id = [c for c in df_plat.columns if c.upper() in {'ID', 'ID_REF', 'PROBESET_ID', 'TRANSCRIPT_CLUSTER_ID'}]
+        if not candidates_id:
+            # fallback: consider any column named like id
+            candidates_id = [c for c in df_plat.columns if re.fullmatch(r'id(_ref)?', c, flags=re.I)]
+            if not candidates_id:
+                # try all columns and pick the one with max overlap
+                candidates_id = list(df_plat.columns)
+
+        best_id = None
+        best_overlap = -1
+        probe_index_set = set(map(str, gene_data.index))
+        for c in candidates_id:
+            # compute overlap count
+            col_vals = set(map(str, df_plat[c].dropna().unique())) if c in df_plat.columns else set()
+            overlap = len(probe_index_set & col_vals)
+            if overlap > best_overlap:
+                best_overlap = overlap
+                best_id = c
+
+        # require at least some overlap
+        if best_id is None or best_overlap <= 0:
+            continue
+
+        # pick symbol column
+        gc = pick_gene_symbol_column(df_plat)
+        if gc is None:
+            # If still no symbol-like column, skip this platform
+            continue
+
+        platform_df = df_plat
+        id_col = best_id
+        gene_col = gc
+        platform_used_path = gpl
+        break
+
+# If still no gene_col, attempt to parse platform table from the series soft itself
+if gene_col is None:
+    df_plat_series = read_gpl_platform_table(soft_file)
+    if df_plat_series is not None and df_plat_series.shape[1] >= 2:
+        # choose id column by overlap
+        candidates_id = [c for c in df_plat_series.columns if c.upper() in {'ID', 'ID_REF', 'PROBESET_ID', 'TRANSCRIPT_CLUSTER_ID'}]
+        if not candidates_id:
+            candidates_id = list(df_plat_series.columns)
+        best_id = None
+        best_overlap = -1
+        probe_index_set = set(map(str, gene_data.index))
+        for c in candidates_id:
+            col_vals = set(map(str, df_plat_series[c].dropna().unique()))
+            overlap = len(probe_index_set & col_vals)
+            if overlap > best_overlap:
+                best_overlap = overlap
+                best_id = c
+        gc = pick_gene_symbol_column(df_plat_series)
+        if (best_id is not None) and (best_overlap > 0) and (gc is not None):
+            platform_df = df_plat_series
+            id_col = best_id
+            gene_col = gc
+
+# Build mapping dataframe if a gene column is available
+mapping_df = None
+if gene_col is not None:
+    anno_df = platform_df if platform_df is not None else gene_annotation
+    try:
+        mapping_df = get_gene_mapping(anno_df, prob_col=id_col, gene_col=gene_col)
+        # If no rows after basic processing, invalidate
+        if mapping_df is not None and mapping_df.shape[0] == 0:
+            mapping_df = None
+    except Exception:
+        mapping_df = None
+
+# Apply mapping; if unavailable, fall back to probe-level
+expr_df = gene_data
+if mapping_df is not None:
+    try:
+        mapped_gene_data = apply_gene_mapping(expression_df=expr_df, mapping_df=mapping_df)
+        if mapped_gene_data.shape[0] > 0:
+            gene_data = mapped_gene_data
+        else:
+            # Fallback to probe-level if mapping yielded empty
+            print("WARNING: Gene symbol mapping produced an empty matrix; proceeding with probe-level data.")
+    except Exception as e:
+        print(f"WARNING: Gene symbol mapping failed with error: {e}. Proceeding with probe-level data.")
+else:
+    print("WARNING: No suitable gene symbol annotation found. Proceeding with probe-level data.")
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols; if normalization yields empty (likely due to probe IDs), fall back to probe-level data.
+note_msgs = []
+try:
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data.copy())
+except Exception as e:
+    normalized_gene_data = pd.DataFrame()
+    note_msgs.append(f"ERROR: normalization_failed: {e}")
+
+if normalized_gene_data is not None and normalized_gene_data.shape[0] > 0:
+    final_gene_data = normalized_gene_data
+    note_msgs.append("INFO: Gene symbols normalized using NCBI synonyms.")
+else:
+    final_gene_data = gene_data.copy()
+    note_msgs.append("WARNING: Gene symbol mapping unavailable; using probe-level identifiers without normalization.")
+
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+final_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+try:
+    selected_clinical_df  # noqa: F401
+except NameError:
+    # Reload clinical features if not in memory
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, final_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and demographics are biased; drop biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info with native Python booleans for JSON
+covariate_cols = [trait, 'Age', 'Gender']
+gene_cols_final = [c for c in unbiased_linked_data.columns if c not in covariate_cols]
+is_gene_available_final = bool(len(gene_cols_final) > 0)
+is_trait_available_final = bool((trait in unbiased_linked_data.columns) and bool(unbiased_linked_data[trait].notna().any()))
+
+note = "; ".join(note_msgs) if note_msgs else "INFO: No special notes."
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save the linked dataset if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE148450.py

@@ -0,0 +1,158 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+cohort = "GSE148450"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
+in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE148450"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/GSE148450.csv"
+out_gene_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE148450.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE148450.csv"
+json_path = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/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 gene expression availability
+is_gene_available = True  # Microarray-based genome-wide transcriptome profiling indicates gene expression data.
+
+# Identify variable availability based on the provided Sample Characteristics Dictionary
+trait_row = 0  # 'diagnosis: TD/ASD/NonTD'
+age_row = None  # Not available in the characteristics
+gender_row = 1  # 'Sex: M/F'
+
+# Define converters
+def _extract_value(x):
+    if x is None:
+        return None
+    try:
+        parts = str(x).split(':', 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        return val.strip()
+    except Exception:
+        return None
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.strip().lower().replace('-', '').replace(' ', '')
+    if v_low == 'asd':
+        return 1
+    if v_low == 'td':
+        return 0
+    if v_low in {'nontd', 'nonstd', 'nonstddevelopment'}:
+        # Non-typical development is not ASD; exclude from binary ASD vs TD analysis
+        return None
+    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 {'f', 'female'}:
+        return 0
+    if v_low in {'m', 'male'}:
+        return 1
+    return None
+
+convert_age = None  # No age available
+
+# Trait availability for initial filtering
+is_trait_available = trait_row is not None
+
+# Save metadata via initial filtering
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction (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
+print("requires_gene_mapping = False")
+
+# Step 5: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+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
+note = "INFO: Trait conversion retained ASD vs TD; Non-TD samples were set to missing and dropped during QC."
+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/Autism_spectrum_disorder_(ASD)/code/GSE285666.py

@@ -0,0 +1,178 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+cohort = "GSE285666"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
+in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE285666"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/GSE285666.csv"
+out_gene_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE285666.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE285666.csv"
+json_path = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/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
+is_gene_available = True  # Affymetrix Human Exon 1.0 ST arrays indicate gene expression data (not miRNA/methylation)
+
+# 2) Variable availability
+# Trait is ASD, but dataset compares Williams syndrome patients vs unaffected parental controls.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2) Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    if not isinstance(x, str):
+        return None
+    parts = x.split(":", 1)
+    val = parts[-1].strip() if len(parts) > 1 else x.strip()
+    return val if val != "" else None
+
+def convert_trait(x):
+    # ASD-specific mapping; not applicable here (no ASD info), return None.
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # If any ASD keywords appear, map to case=1; explicit non-ASD control words map to 0
+    if any(k in vl for k in ["autism spectrum disorder", "asd", "autism"]):
+        return 1
+    if any(k in vl for k in ["control", "unaffected", "healthy", "neurotypical"]):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Extract first float/int in the string
+    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
+    vl = v.strip().lower()
+    if vl in ["male", "m"]:
+        return 1
+    if vl in ["female", "f"]:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction: skipped because trait_row is None (no clinical trait data available for ASD)
+
+# 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 in the annotation for mapping
+probe_col = 'ID'
+gene_symbol_col = 'gene_assignment' if 'gene_assignment' in gene_annotation.columns else 'mrna_assignment'
+
+# Build mapping dataframe: probe ID -> gene symbol text
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 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
+# 1. Normalize the obtained gene data and save gene-only output
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine trait availability based on presence of extracted clinical features from prior steps
+trait_available = ('selected_clinical_data' in globals()) and (selected_clinical_data is not None) and (trait in getattr(selected_clinical_data, 'index', []))
+
+if 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 check and removal of biased covariates
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and 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 were available; linked data processed with missing value handling and bias checks."
+    )
+
+    # 6. Save linked data only if usable
+    if is_usable:
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # No trait for ASD in this cohort; 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,
+        df=normalized_gene_data.T,  # provide a non-empty df to pass structural checks
+        note="INFO: ASD trait not available in this series (dataset contrasts Williams syndrome vs parental controls). "
+             "Only gene expression data were normalized and saved."
+    )

output/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE42133.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+cohort = "GSE42133"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
+in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE42133"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/GSE42133.csv"
+out_gene_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE42133.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE42133.csv"
+json_path = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/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  # Leukocyte gene expression levels (not miRNA/methylation)
+
+# 2) Variable availability (from the provided Sample Characteristics Dictionary)
+trait_row = 0         # 'dx (diagnosis): ASD' vs 'Control'
+age_row = None        # No age information available in the provided dictionary
+gender_row = None     # Only 'gender: male' listed -> constant, thus not useful
+
+# 2.2) Data type conversion functions
+def _after_colon(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 = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower().strip()
+    # Map ASD vs control/TD
+    asd_tokens = {'asd', 'autism', 'autistic'}
+    control_tokens = {'control', 'td', 'typically developing', 'typically-developed', 'typically_developing', 'neurotypical', 'sibling', 'unaffected'}
+    # Exact matches or contains tokens
+    if any(tok in v_low for tok in asd_tokens):
+        return 1
+    if any(tok in v_low for tok in control_tokens):
+        return 0
+    # Fallback for common labels
+    if v_low in {'case', 'patient'}:
+        return 1
+    if v_low in {'healthy', 'normal'}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = v.lower()
+    # Extract first float/int
+    m = re.search(r'(-?\d+(\.\d+)?)', s)
+    if not m:
+        return None
+    val = float(m.group(1))
+    # Convert to years if unit indicates months or weeks
+    if 'month' in s:
+        return val / 12.0
+    if 'week' in s or 'wk' in s:
+        return val / 52.0
+    # Assume years otherwise
+    return val
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    s = v.lower().strip()
+    # Female -> 0, Male -> 1
+    if s in {'female', 'f', 'girl', 'woman'}:
+        return 0
+    if s in {'male', 'm', 'boy', 'man'}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df, n=5)
+    print("Clinical preview:", clinical_preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for probe IDs and gene symbols
+probe_col = 'ID'
+gene_symbol_col = 'Symbol'
+
+# Build the mapping dataframe from the annotation
+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)
+
+# Ensure clinical features are available in current context
+if 'selected_clinical_df' not in globals():
+    if os.path.exists(out_clinical_data_file):
+        selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    else:
+        raise NameError("selected_clinical_df is not defined and clinical data file not found.")
+
+# 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 = "INFO: Age and Gender not available/usable in clinical data."
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note=note
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE57802.py

@@ -0,0 +1,224 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+cohort = "GSE57802"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
+in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE57802"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/GSE57802.csv"
+out_gene_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE57802.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE57802.csv"
+json_path = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/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  # Transcriptome profiling indicates gene expression data (not miRNA/methylation)
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+trait_row = None  # No explicit ASD diagnosis/status available; CNV status cannot be used as ASD label
+age_row = 2       # 'age: ...'
+gender_row = 1    # 'gender: M/F'
+
+# 2.2 Data Type Conversion
+def _after_colon(value):
+    if value is None:
+        return None
+    parts = str(value).split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    # Generic ASD converter (not used here since trait_row is None)
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    vl = v.lower()
+    # Positive ASD indicators
+    pos = {'asd', 'autism', 'autism spectrum disorder', 'case', 'patient', 'affected', 'asdp', 'autistic'}
+    # Negative ASD indicators
+    neg = {'control', 'td', 'typical', 'healthy', 'unaffected', 'neurotypical'}
+    if vl in pos:
+        return 1
+    if vl in neg:
+        return 0
+    if vl in {'yes', 'y', 'true', '1'}:
+        return 1
+    if vl in {'no', 'n', 'false', '0'}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {"na", "n/a", "", "none"}:
+        return None
+    # Extract first numeric token (handles integers/floats, possibly with units)
+    m = re.search(r'[-+]?\d*\.?\d+', vl)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {"male", "m", "man", "boy"}:
+        return 1
+    if vl in {"female", "f", "woman", "girl"}:
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (skip because trait_row is None)
+# If trait_row were available:
+# selected_clinical_df = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_clinical_df)
+# 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
+# 1-2. Determine identifier and gene symbol columns and construct mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine if trait data is available from previous steps
+try:
+    trait_available = (trait_row is not None)
+except NameError:
+    trait_available = False
+
+if trait_available:
+    # 2. Link clinical and genetic data
+    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
+    )
+    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. Check 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 metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Linked clinical and gene data with age and gender where available."
+    )
+
+    # 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:
+    # Trait not available: skip linking and still record final metadata
+    note = ("INFO: Trait variable is not available in sample characteristics; "
+            "linking clinical and gene data was skipped. Only gene expression matrix was processed and saved.")
+    dummy_df = normalized_gene_data.T  # Use a non-empty df to avoid abnormality override
+    _ = 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=dummy_df,
+        note=note
+    )
+    # Do not save out_data_file since dataset is unusable without trait

output/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE65106.py

@@ -0,0 +1,200 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+cohort = "GSE65106"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
+in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE65106"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/GSE65106.csv"
+out_gene_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE65106.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE65106.csv"
+json_path = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/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
+import numpy as np
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Whole-genome microarray expression data
+
+# 2. Variable Availability
+trait_row = 1    # 'disease type: Normal/ASD/WT'
+age_row = 3      # 'donor age: ...'
+gender_row = 4   # 'donor sex: Male/Female'
+
+# 2.2 Conversion functions
+def _extract_value(x):
+    if x is None or (isinstance(x, float) and np.isnan(x)):
+        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 v is None:
+        return None
+    s = v.strip().lower()
+    # Normalize common labels
+    if s in {'asd', 'autism', 'autism spectrum disorder', 'autistic'}:
+        return 1
+    if s in {'normal', 'control', 'wt', 'wild-type', 'wild type', 'unaffected', 'healthy', 'sib control', 'sibling control', 'non-asd'}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    s = v.strip().lower()
+    if any(tok in s for tok in ['embryo', 'embryonic', 'unknown', 'na', 'n/a']):
+        return None
+    m = re.search(r'[-+]?\d*\.?\d+', s)
+    if m:
+        try:
+            return float(m.group())
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    s = v.strip().lower()
+    if s in {'male', 'm'}:
+        return 1
+    if s in {'female', 'f'}:
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait is available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    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)
+
+# 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 probe IDs (e.g., '7892501'), 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
+# Identify appropriate columns for probe IDs and gene symbols from the annotation preview:
+# - Probe IDs: 'ID'
+# - Gene symbols information: 'gene_assignment' (contains gene symbol strings)
+
+# 1-2. Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 3. Apply mapping to convert probe-level data to gene-level data
+# Preserve probe-level data then overwrite gene_data with gene-level
+probe_data = gene_data
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+# Ensure the clinical dataframe is available in scope; if not, load from the saved file.
+try:
+    selected_clinical_df
+except NameError:
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available_final = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_final = (trait in linked_data.columns)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=""
+)
+
+# 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/Autism_spectrum_disorder_(ASD)/code/GSE87847.py

@@ -0,0 +1,172 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+cohort = "GSE87847"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
+in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE87847"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/GSE87847.csv"
+out_gene_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE87847.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE87847.csv"
+json_path = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/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 (based on series summary: mRNA expression in tissue/blood)
+is_gene_available = True
+
+# 2) Variable availability based on Sample Characteristics Dictionary provided
+# Trait (ASD vs typically developing) is available at key 0
+trait_row = 0
+
+# Age is not present in the dictionary
+age_row = None
+
+# Gender is present at key 2
+gender_row = 2
+
+# 2.2) Converters
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(x).strip()
+
+def convert_trait(x):
+    v = _after_colon(x).lower()
+    if v in ("", "na", "n/a", "unknown", "null"):
+        return None
+    # Normalize common phrases
+    v_norm = v.replace("-", " ").replace("_", " ").strip()
+    if "autism spectrum disorder" in v_norm or v_norm == "asd":
+        return 1
+    if "typically developing" in v_norm or "control" in v_norm:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x).lower()
+    if not v or v in ("na", "n/a", "unknown", "null"):
+        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):
+    v = _after_colon(x).lower()
+    if v in ("", "na", "n/a", "unknown", "null"):
+        return None
+    if v in ("male", "m"):
+        return 1
+    if v in ("female", "f"):
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print("Preview of selected clinical features:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = False")
+
+# Step 5: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save
+if 'gene_data' not in globals():
+    gene_data = get_genetic_data(matrix_file)
+
+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
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Age not provided; Gender available; gene symbols normalized via NCBI synonyms."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=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/Autism_spectrum_disorder_(ASD)/code/GSE89594.py

@@ -0,0 +1,186 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+cohort = "GSE89594"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
+in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE89594"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/GSE89594.csv"
+out_gene_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE89594.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE89594.csv"
+json_path = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import os
+
+# 1) Gene expression data availability
+is_gene_available = True  # Gene expression profiled from blood per series design (not pure miRNA/methylation)
+
+# 2) Variable availability
+trait_row = 0  # diagnosis
+age_row = 2    # age
+gender_row = 3 # gender
+
+# 2.2) Converters
+def _after_colon(x):
+    if x is None:
+        return None
+    parts = str(x).split(':', 1)
+    return parts[1].strip() if len(parts) > 1 else str(x).strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    lv = v.lower()
+    if 'autism spectrum disorder' in lv or re.search(r'\basd\b', lv):
+        return 1
+    if 'control' in lv or 'williams' in lv or re.search(r'\bws\b', lv):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    m = re.search(r'(\d+(?:\.\d+)?)', v)
+    if not m:
+        return None
+    try:
+        val = float(m.group(1))
+        return int(val) if val.is_integer() else val
+    except:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    lv = v.lower()
+    if 'female' in lv or re.fullmatch(r'\bf\b', lv):
+        return 0
+    if 'male' in lv or re.fullmatch(r'\bm\b', lv):
+        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
+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) Decide columns for mapping: probe ID ('ID') and gene symbol ('GENE_SYMBOL') based on previews
+probe_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+
+# 2) Build 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(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. Bias assessment and removal of biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info (ensure pure Python bools are passed)
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in selected_clinical_df.index) and selected_clinical_df.loc[trait].notna().any())
+is_trait_biased_py = bool(is_trait_biased)
+note = "INFO: Probes mapped to GENE_SYMBOL; WS and controls coded as 0, ASD as 1; whole blood samples."
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased_py,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if bool(is_usable):
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Autism_spectrum_disorder_(ASD)/code/TCGA.py

@@ -0,0 +1,63 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import re
+import pandas as pd
+
+# Step 1: Find the most appropriate TCGA cohort for Autism Spectrum Disorder (ASD)
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+trait_synonyms = ["autism", "asd", "autistic", "neurodevelopmental", "neuro-developmental"]
+
+def normalize_text(s: str) -> str:
+    return re.sub(r'[^a-z0-9]+', ' ', s.lower())
+
+def score_dir(name: str, synonyms) -> int:
+    n = normalize_text(name)
+    return sum(1 for syn in synonyms if syn in n)
+
+scored = [(d, score_dir(d, trait_synonyms)) for d in subdirs]
+# Select directory with highest score > 0
+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:
+    print("No suitable TCGA cohort found for the trait 'Autism Spectrum Disorder (ASD)'. Skipping this trait.")
+    # Record unusable dataset at initial filtering stage
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    print(f"Selected TCGA cohort directory: {selected_dir}")
+    cohort_dir = os.path.join(tcga_root_dir, 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 column names of the clinical data
+    print("Clinical data columns:")
+    print(list(clinical_df.columns))

output/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json

@@ -1,102 +1 @@
-{
-  "GSE89594": {
-    "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": 62
-  },
-  "GSE87847": {
-    "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": 93
-  },
-  "GSE65106": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": false,
-    "sample_size": 59
-  },
-  "GSE57802": {
-    "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": 99
-  },
-  "GSE42133": {
-    "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": 147
-  },
-  "GSE285666": {
-    "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": 52
-  },
-  "GSE148450": {
-    "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": 159
-  },
-  "GSE123302": {
-    "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
-  },
-  "GSE111175": {
-    "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": 140
-  },
-  "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
-  }
-}
+{"GSE89594": {"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": 94, "note": "INFO: Probes mapped to GENE_SYMBOL; WS and controls coded as 0, ASD as 1; whole blood samples."}, "GSE87847": {"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": 93, "note": "INFO: Age not provided; Gender available; gene symbols normalized via NCBI synonyms."}, "GSE65106": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 59, "note": ""}, "GSE57802": {"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 variable is not available in sample characteristics; linking clinical and gene data was skipped. Only gene expression matrix was processed and saved."}, "GSE42133": {"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": 147, "note": "INFO: Age and Gender not available/usable in clinical data."}, "GSE285666": {"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: ASD trait not available in this series (dataset contrasts Williams syndrome vs parental controls). Only gene expression data were normalized and saved."}, "GSE148450": {"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": 159, "note": "INFO: Trait conversion retained ASD vs TD; Non-TD samples were set to missing and dropped during QC."}, "GSE123302": {"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": 224, "note": "WARNING: Gene symbol mapping unavailable; using probe-level identifiers without normalization."}, "GSE113842": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "WARNING: Normalized gene data is empty after mapping; possible species/platform mismatch (e.g., Mus musculus annotation). INFO: Gender not available in clinical annotations. DEBUG: Probe-annotation overlap=49372/49372; normalized gene rows=0."}, "GSE111175": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 141, "note": "INFO: Gender unavailable/constant (all male) per series; Age recorded in months; Probe IDs mapped to gene symbols and normalized via NCBI synonyms."}, "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/Autism_spectrum_disorder_(ASD)/gene_data/GSE113842.csv

@@ -0,0 +1 @@
+Gene,GSM3120900,GSM3120901,GSM3120902,GSM3120903,GSM3120904,GSM3120905,GSM3120906,GSM3120907,GSM3120908,GSM3120909,GSM3120910,GSM3120911,GSM3120912,GSM3120913,GSM3120914,GSM3120915,GSM3120916,GSM3120917,GSM3120918,GSM3120919,GSM3120920,GSM3120921,GSM3120922,GSM3120923,GSM3120924,GSM3120925,GSM3120926

output/preprocess/Autoinflammatory_Disorders/clinical_data/GSE43553.csv

@@ -1,2 +1,2 @@
 ,GSM1065191,GSM1065192,GSM1065193,GSM1065194,GSM1065195,GSM1065196,GSM1065197,GSM1065198,GSM1065199,GSM1065200,GSM1065201,GSM1065202,GSM1065203,GSM1065204,GSM1065205,GSM1065206,GSM1065207,GSM1065208,GSM1065209,GSM1065210,GSM1065211,GSM1065212,GSM1065213,GSM1065214,GSM1065215,GSM1065216,GSM1065217,GSM1065218,GSM1065219,GSM1065220,GSM1065221,GSM1065222,GSM1065223,GSM1065224,GSM1065225,GSM1065226,GSM1065227,GSM1065228,GSM1065229,GSM1065230,GSM1065231,GSM1065232,GSM1065233,GSM1065234,GSM1065235,GSM1065236,GSM1065237,GSM1065238,GSM1065239,GSM1065240,GSM1065241,GSM1065242,GSM1065243,GSM1065244,GSM1065245,GSM1065246,GSM1065247,GSM1065248,GSM1065249,GSM1065250,GSM1065251,GSM1065252,GSM1065253,GSM1065254,GSM1065255,GSM1065256,GSM1065257,GSM1065258,GSM1065259,GSM1065260,GSM1065261,GSM1065262,GSM1065263,GSM1065264,GSM1065265,GSM1065266,GSM1065267,GSM1065268,GSM1065269,GSM1065270,GSM1065271,GSM1065272,GSM1065273,GSM1065274,GSM1065275,GSM1065276,GSM1065277,GSM1065278,GSM1065279,GSM1065280,GSM1065281,GSM1065282,GSM1065283,GSM1065284,GSM1065285,GSM1065286,GSM1065287,GSM1065288,GSM1065289,GSM1065290
-Autoinflammatory_Disorders,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Autoinflammatory_Disorders,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Autoinflammatory_Disorders/clinical_data/GSE80060.csv

@@ -1,2 +1,2 @@
-feature_0,feature_1,feature_2,feature_3,feature_4,feature_5
-,0.0,1.0,,,
+,GSM2111993,GSM2111994,GSM2111995,GSM2111996,GSM2111997,GSM2111998,GSM2111999,GSM2112000,GSM2112001,GSM2112002,GSM2112003,GSM2112004,GSM2112005,GSM2112006,GSM2112007,GSM2112008,GSM2112009,GSM2112010,GSM2112011,GSM2112012,GSM2112013,GSM2112014,GSM2112015,GSM2112016,GSM2112017,GSM2112018,GSM2112019,GSM2112020,GSM2112021,GSM2112022,GSM2112023,GSM2112024,GSM2112025,GSM2112026,GSM2112027,GSM2112028,GSM2112029,GSM2112030,GSM2112031,GSM2112032,GSM2112033,GSM2112034,GSM2112035,GSM2112036,GSM2112037,GSM2112038,GSM2112039,GSM2112040,GSM2112041,GSM2112042,GSM2112043,GSM2112044,GSM2112045,GSM2112046,GSM2112047,GSM2112048,GSM2112049,GSM2112050,GSM2112051,GSM2112052,GSM2112053,GSM2112054,GSM2112055,GSM2112056,GSM2112057,GSM2112058,GSM2112059,GSM2112060,GSM2112061,GSM2112062,GSM2112063,GSM2112064,GSM2112065,GSM2112066,GSM2112067,GSM2112068,GSM2112069,GSM2112070,GSM2112071,GSM2112072,GSM2112073,GSM2112074,GSM2112075,GSM2112076,GSM2112077,GSM2112078,GSM2112079,GSM2112080,GSM2112081,GSM2112082,GSM2112083,GSM2112084,GSM2112085,GSM2112086,GSM2112087,GSM2112088,GSM2112089,GSM2112090,GSM2112091,GSM2112092,GSM2112093,GSM2112094,GSM2112095,GSM2112096,GSM2112097,GSM2112098,GSM2112099,GSM2112100,GSM2112101,GSM2112102,GSM2112103,GSM2112104,GSM2112105,GSM2112106,GSM2112107,GSM2112108,GSM2112109,GSM2112110,GSM2112111,GSM2112112,GSM2112113,GSM2112114,GSM2112115,GSM2112116,GSM2112117,GSM2112118,GSM2112119,GSM2112120,GSM2112121,GSM2112122,GSM2112123,GSM2112124,GSM2112125,GSM2112126,GSM2112127,GSM2112128,GSM2112129,GSM2112130,GSM2112131,GSM2112132,GSM2112133,GSM2112134,GSM2112135,GSM2112136,GSM2112137,GSM2112138,GSM2112139,GSM2112140,GSM2112141,GSM2112142,GSM2112143,GSM2112144,GSM2112145,GSM2112146,GSM2112147,GSM2112148,GSM2112149,GSM2112150,GSM2112151,GSM2112152,GSM2112153,GSM2112154,GSM2112155,GSM2112156,GSM2112157,GSM2112158,GSM2112159,GSM2112160,GSM2112161,GSM2112162,GSM2112163,GSM2112164,GSM2112165,GSM2112166,GSM2112167,GSM2112168,GSM2112169,GSM2112170,GSM2112171,GSM2112172,GSM2112173,GSM2112174,GSM2112175,GSM2112176,GSM2112177,GSM2112178,GSM2112179,GSM2112180,GSM2112181,GSM2112182,GSM2112183,GSM2112184,GSM2112185,GSM2112186,GSM2112187,GSM2112188,GSM2112189,GSM2112190,GSM2112191,GSM2112192,GSM2112193,GSM2112194,GSM2112195,GSM2112196,GSM2112197,GSM2112198
+Autoinflammatory_Disorders,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Autoinflammatory_Disorders/code/GSE43553.py

@@ -0,0 +1,196 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autoinflammatory_Disorders"
+cohort = "GSE43553"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autoinflammatory_Disorders"
+in_cohort_dir = "../DATA/GEO/Autoinflammatory_Disorders/GSE43553"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/GSE43553.csv"
+out_gene_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/gene_data/GSE43553.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/clinical_data/GSE43553.csv"
+json_path = "./output/z1/preprocess/Autoinflammatory_Disorders/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Microarray-based gene expression profiling stated in background
+
+# 2) Variable availability and converters
+# Based on Sample Characteristics Dictionary:
+# - Use key 1 ('genotype: ...') to infer Autoinflammatory_Disorders vs healthy controls
+trait_row = 1
+age_row = None
+gender_row = None
+
+def _after_colon(value):
+    if pd.isna(value):
+        return None
+    s = str(value)
+    parts = s.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    v = v.strip()
+    return v if v else None
+
+def convert_trait(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Controls
+    if "healthy" in vl or "control" in vl:
+        return 0
+    # Known autoinflammatory-related genotype descriptors
+    keywords = ["mutation", "carrier", "mvk", "nlrp3", "pstpip1", "tnfrsf1a"]
+    if any(k in vl for k in keywords):
+        return 1
+    # Default to case if genotype is not explicitly healthy/control
+    return 1
+
+def convert_age(value):
+    return None
+
+def convert_gender(value):
+    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)
+    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("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2. Identify the appropriate columns for probe IDs and gene symbols, then create the mapping dataframe
+# From the annotation preview, probe IDs are in 'ID' and gene symbols are in 'Gene Symbol'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Apply the mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+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)
+
+# Ensure clinical data is available (reuse in-memory or reload from disk)
+try:
+    selected_clinical_df
+except NameError:
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2) Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3) Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4) Bias assessment and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Availability flags
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available = bool((trait in linked_data.columns) and (linked_data[trait].notna().sum() > 0))
+
+# Prepare a brief note
+try:
+    trait_counts = linked_data[trait].value_counts(dropna=True).to_dict()
+except Exception:
+    trait_counts = {}
+note = (
+    f"INFO: Post-QC samples={len(unbiased_linked_data)}; "
+    f"trait_counts={trait_counts}; "
+    f"has_age={'Age' in linked_data.columns}; "
+    f"has_gender={'Gender' in linked_data.columns}."
+)
+
+# 5) Final validation and save cohort info
+# Ensure df has plain string column names to avoid any non-serializable types downstream
+df_for_validation = unbiased_linked_data.copy()
+df_for_validation.columns = [str(c) for c in list(df_for_validation.columns)]
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=bool(is_gene_available),
+    is_trait_available=bool(is_trait_available),
+    is_biased=bool(is_trait_biased),
+    df=df_for_validation,
+    note=note
+)
+
+# 6) Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    df_for_validation.to_csv(out_data_file)

output/preprocess/Autoinflammatory_Disorders/code/GSE80060.py

@@ -0,0 +1,211 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autoinflammatory_Disorders"
+cohort = "GSE80060"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autoinflammatory_Disorders"
+in_cohort_dir = "../DATA/GEO/Autoinflammatory_Disorders/GSE80060"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/GSE80060.csv"
+out_gene_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/gene_data/GSE80060.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/clinical_data/GSE80060.csv"
+json_path = "./output/z1/preprocess/Autoinflammatory_Disorders/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  # Title indicates "Gene expression data"; not miRNA/methylation
+
+# 2) Variable availability
+# From Sample Characteristics Dictionary:
+# 1: ['disease status: SJIA', 'disease status: Healthy'] -> maps to our trait (Autoinflammatory_Disorders)
+trait_row = 1
+age_row = None         # No age field found
+gender_row = None      # No gender field found
+
+# 2.2) Converters
+def _after_colon(value: str) -> str:
+    s = str(value)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(value):
+    if pd.isna(value):
+        return None
+    v = _after_colon(value).lower()
+    # Heuristics: SJIA is an autoinflammatory disease -> 1; Healthy/Control -> 0
+    if 'sjia' in v or ('patient' in v and 'healthy' not in v):
+        return 1
+    if 'healthy' in v or 'control' in v or v == 'normal':
+        return 0
+    return None
+
+def convert_age(value):
+    if pd.isna(value):
+        return None
+    v = _after_colon(value).strip()
+    if v == '' or v.lower() in {'na', 'n/a', 'nan', 'none', 'unknown'}:
+        return None
+    # Try direct float
+    try:
+        return float(v)
+    except Exception:
+        pass
+    low = v.lower()
+    m = re.search(r'(\d+(\.\d+)?)', low)
+    if not m:
+        return None
+    num = float(m.group(1))
+    # Convert to years if units provided
+    if 'month' in low or re.search(r'\bmo\b', low):
+        return round(num / 12.0, 3)
+    if 'week' in low or re.search(r'\bwk\b', low):
+        return round(num / 52.0, 3)
+    if 'day' in low or re.search(r'\bd\b', low):
+        return round(num / 365.0, 3)
+    return num  # assume years
+
+def convert_gender(value):
+    if pd.isna(value):
+        return None
+    v = _after_colon(value).strip().lower()
+    if v in {'m', 'male'} or 'male' in v or 'man' in v or 'boy' in v:
+        return 1
+    if v in {'f', 'female'} or 'female' in v or 'woman' in v or 'girl' in v:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Preview 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)
+
+# 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 columns for probe IDs and gene symbols based on the annotation preview
+probe_col = 'ID'            # matches probe identifiers like '1007_s_at'
+gene_symbol_col = 'Gene Symbol'  # contains gene symbols (may include multiple per probe)
+
+# 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 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 (use the correct variable from Step 2)
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Derive availability flags based on current data state and cast to built-in bool
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available = bool((trait in linked_data.columns) and (linked_data[trait].notna().sum() > 0))
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine biases and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+is_trait_biased = bool(is_trait_biased)
+
+# 5. Final quality validation and save cohort info
+covariate_cols = [trait, 'Age', 'Gender']
+gene_cols_in_final = [c for c in unbiased_linked_data.columns if c not in covariate_cols]
+sample_count = int(len(unbiased_linked_data))
+gene_count = int(len(gene_cols_in_final))
+note = (
+    f"INFO: Normalized Affymetrix probe data to gene symbols using NCBI synonyms. "
+    f"Clinical features available: trait only; Age/Gender not provided. "
+    f"Post-QC samples: {sample_count}; genes: {gene_count}."
+)
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=bool(is_gene_available),
+    is_trait_available=bool(is_trait_available),
+    is_biased=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 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)
+    print(f"Saved processed cohort to {out_data_file}")
+    print(f"Saved gene data to {out_gene_data_file}")

output/preprocess/Autoinflammatory_Disorders/code/TCGA.py

@@ -0,0 +1,123 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autoinflammatory_Disorders"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Autoinflammatory_Disorders/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Discover available TCGA cohort directories
+available_dirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Try to find a TCGA cohort relevant to Autoinflammatory Disorders (unlikely among cancer cohorts)
+keywords = [
+    "autoinflammatory", "auto-inflammatory", "autoinflammation", "autoinflamm",
+    "periodic_fever", "periodic-fever", "fmf", "traps", "hids", "caps", "nlrp", "inflam"
+]
+matches = []
+for d in available_dirs:
+    name_l = d.lower()
+    score = sum(1 for k in keywords if k in name_l)
+    if score > 0:
+        # Prefer more keyword hits and shorter names (more specific)
+        matches.append((score, -len(d), d))
+
+if not matches:
+    # No suitable TCGA cohort for autoinflammatory disorders; record and skip
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    selected_dir = None
+    clinical_df = pd.DataFrame()
+    genetic_df = pd.DataFrame()
+else:
+    # Select the best match
+    matches.sort(reverse=True)
+    selected_dir = matches[0][2]
+
+if selected_dir:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_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)
+
+    print(list(clinical_df.columns))
+
+# Step 2: Initial Data Loading
+import os
+import pandas as pd
+
+# Use the provided list of subdirectories but ensure they exist on disk
+provided_subdirs = [
+    'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)', 'TCGA_Uterine_Carcinosarcoma_(UCS)',
+    'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)', 'TCGA_Testicular_Cancer_(TGCT)',
+    'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)', 'TCGA_Rectal_Cancer_(READ)',
+    'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)',
+    'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)', 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)',
+    'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)', 'TCGA_Glioblastoma_(GBM)',
+    'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)',
+    'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)', 'TCGA_Breast_Cancer_(BRCA)',
+    'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)', 'TCGA_Adrenocortical_Cancer_(ACC)',
+    'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+available_dirs = [d for d in provided_subdirs if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Search for directories matching autoinflammatory disorders (unlikely in TCGA cancer cohorts)
+keywords = [
+    "autoinflammatory", "auto-inflammatory", "autoinflammation", "autoinflamm",
+    "periodic_fever", "periodic-fever", "fmf", "traps", "hids", "caps", "nlrp", "inflam"
+]
+matches = []
+for d in available_dirs:
+    name_l = d.lower()
+    score = sum(1 for k in keywords if k in name_l)
+    if score > 0:
+        matches.append((score, -len(d), d))
+
+if not matches:
+    # No suitable TCGA cohort for Autoinflammatory Disorders; record and skip
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    selected_dir = None
+    clinical_df = pd.DataFrame()
+    genetic_df = pd.DataFrame()
+else:
+    # Select the most specific match (more keywords, shorter name)
+    matches.sort(reverse=True)
+    selected_dir = matches[0][2]
+
+# If a directory was selected, locate files and load data
+if selected_dir:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_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)
+
+    print(list(clinical_df.columns))

output/preprocess/Autoinflammatory_Disorders/cohort_info.json

@@ -1,32 +1 @@
-{
-  "GSE80060": {
-    "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
-  },
-  "GSE43553": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 66
-  },
-  "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": 48
-  }
-}
+{"GSE80060": {"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": 206, "note": "INFO: Normalized Affymetrix probe data to gene symbols using NCBI synonyms. Clinical features available: trait only; Age/Gender not provided. Post-QC samples: 206; genes: 19845."}, "GSE43553": {"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": 100, "note": "INFO: Post-QC samples=100; trait_counts={1.0: 66, 0.0: 34}; has_age=False; has_gender=False."}, "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/Bile_Duct_Cancer/clinical_data/GSE107754.csv

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

output/preprocess/Bile_Duct_Cancer/clinical_data/GSE131027.csv

@@ -1,2 +1,2 @@
-0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29
-0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,,,,,,
+,GSM3759992,GSM3759993,GSM3759994,GSM3759995,GSM3759996,GSM3759997,GSM3759998,GSM3759999,GSM3760000,GSM3760001,GSM3760002,GSM3760003,GSM3760004,GSM3760005,GSM3760006,GSM3760007,GSM3760008,GSM3760009,GSM3760010,GSM3760011,GSM3760012,GSM3760013,GSM3760014,GSM3760015,GSM3760016,GSM3760017,GSM3760018,GSM3760019,GSM3760020,GSM3760021,GSM3760022,GSM3760023,GSM3760024,GSM3760025,GSM3760026,GSM3760027,GSM3760028,GSM3760029,GSM3760030,GSM3760031,GSM3760032,GSM3760033,GSM3760034,GSM3760035,GSM3760036,GSM3760037,GSM3760038,GSM3760039,GSM3760040,GSM3760041,GSM3760042,GSM3760043,GSM3760044,GSM3760045,GSM3760046,GSM3760047,GSM3760048,GSM3760049,GSM3760050,GSM3760051,GSM3760052,GSM3760053,GSM3760054,GSM3760055,GSM3760056,GSM3760057,GSM3760058,GSM3760059,GSM3760060,GSM3760061,GSM3760062,GSM3760063,GSM3760064,GSM3760065,GSM3760066,GSM3760067,GSM3760068,GSM3760069,GSM3760070,GSM3760071,GSM3760072,GSM3760073,GSM3760074,GSM3760075,GSM3760076,GSM3760077,GSM3760078,GSM3760079,GSM3760080,GSM3760081,GSM3760082,GSM3760083
+Bile_Duct_Cancer,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0

output/preprocess/Bile_Duct_Cancer/code/GSE107754.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bile_Duct_Cancer"
+cohort = "GSE107754"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bile_Duct_Cancer"
+in_cohort_dir = "../DATA/GEO/Bile_Duct_Cancer/GSE107754"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Bile_Duct_Cancer/GSE107754.csv"
+out_gene_data_file = "./output/z1/preprocess/Bile_Duct_Cancer/gene_data/GSE107754.csv"
+out_clinical_data_file = "./output/z1/preprocess/Bile_Duct_Cancer/clinical_data/GSE107754.csv"
+json_path = "./output/z1/preprocess/Bile_Duct_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine gene expression data availability based on background information
+is_gene_available = True  # Whole human genome gene expression microarrays are reported
+
+# Step 2: Identify rows for trait, age, and gender from the Sample Characteristics Dictionary
+trait_row = 2   # 'tissue: Bile duct cancer' appears here among other tissue types
+age_row = None  # No age information found
+gender_row = 0  # 'gender: Female' and 'gender: Male' are present
+
+# Step 2.2: Define conversion functions
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    if ':' in s:
+        key, val = s.split(':', 1)
+        return key.strip().lower(), val.strip()
+    return '', s.strip()
+
+def convert_trait(value):
+    # Map to 1 if tissue is bile duct cancer, 0 if tissue is another cancer.
+    # If the field is not tissue (e.g., biopsy location), return None.
+    key, val = _after_colon(value)
+    val_l = val.lower()
+    if 'bile' in val_l and 'duct' in val_l:
+        return 1
+    if 'tissue' in key:
+        # Not bile duct tissue -> control (0)
+        return 0
+    # Unknown/irrelevant field for trait
+    return None
+
+def convert_gender(value):
+    key, val = _after_colon(value)
+    v = val.lower()
+    if v in ['female', 'f']:
+        return 0
+    if v in ['male', 'm']:
+        return 1
+    return None
+
+def convert_age(value):
+    # Not used as age_row is None; keep for completeness if needed later
+    key, val = _after_colon(value)
+    v = val.lower()
+    # Remove common non-numeric tokens
+    for token in ['years', 'year', 'yrs', 'yr', 'y', 'age', ' ']:
+        v = v.replace(token, '')
+    v = v.replace('+', '').replace('~', '').replace('about', '').strip()
+    try:
+        return float(v)
+    except Exception:
+        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:
+    import os
+
+    kwargs = {
+        "clinical_df": clinical_data,
+        "trait": trait,
+        "trait_row": trait_row,
+        "convert_trait": convert_trait
+    }
+    # Age not available; include gender
+    kwargs.update({
+        "gender_row": gender_row,
+        "convert_gender": convert_gender
+    })
+
+    selected_clinical_df = geo_select_clinical_features(**kwargs)
+
+    # Preview and save
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the columns for probe IDs and gene symbols based on the annotation preview
+probe_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+
+# Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and drop biased covariates
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = ("INFO: Age unavailable; trait derived from 'tissue' field in sample characteristics; "
+        "samples without explicit tissue label were dropped during missing-value handling.")
+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/Bile_Duct_Cancer/code/GSE131027.py

@@ -0,0 +1,196 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bile_Duct_Cancer"
+cohort = "GSE131027"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bile_Duct_Cancer"
+in_cohort_dir = "../DATA/GEO/Bile_Duct_Cancer/GSE131027"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Bile_Duct_Cancer/GSE131027.csv"
+out_gene_data_file = "./output/z1/preprocess/Bile_Duct_Cancer/gene_data/GSE131027.csv"
+out_clinical_data_file = "./output/z1/preprocess/Bile_Duct_Cancer/clinical_data/GSE131027.csv"
+json_path = "./output/z1/preprocess/Bile_Duct_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Based on series design indicating expression features
+
+# 2) Variable availability and converters
+# From Sample Characteristics Dictionary:
+# 0: tissue
+# 1: cancer (contains 'Bile duct cancer')
+# 2: mutated gene
+# 3: predicted HRDEXP
+# 4: parp predicted
+trait_row = 1
+age_row = None
+gender_row = None
+
+def _after_last_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":")
+    return parts[-1].strip().lower()
+
+def convert_trait(x):
+    v = _after_last_colon(x)
+    if not v or v in {"na", "n/a", "unknown", "none"}:
+        return None
+    # Map bile duct cancer / cholangiocarcinoma to 1, others to 0
+    if "bile" in v and "duct" in v:
+        return 1
+    if "cholangiocarcinoma" in v:
+        return 1
+    # If it's any other cancer label, map to 0
+    return 0
+
+def convert_age(x):
+    v = _after_last_colon(x)
+    if not v or v in {"na", "n/a", "unknown", "none"}:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _after_last_colon(x)
+    if not v:
+        return None
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    # Save
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+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 in the annotation dataframe
+probe_id_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_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
+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
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+# Ensure pure Python bools for JSON serialization
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available = bool((trait in linked_data.columns) and (linked_data[trait].notna().sum() > 0))
+is_trait_biased = bool(is_trait_biased)
+
+# Defensive copy for any downstream processing; ensure string column names
+df_to_save = unbiased_linked_data.copy()
+df_to_save.columns = [str(c) for c in df_to_save.columns]
+
+note = "INFO: Trait derived from cancer type; no age/gender available in series; probes mapped via 'Gene Symbol' and gene symbols normalized by NCBI synonym dictionary."
+
+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=df_to_save,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    df_to_save.to_csv(out_data_file)

output/preprocess/Bile_Duct_Cancer/code/TCGA.py

@@ -0,0 +1,306 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bile_Duct_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Bile_Duct_Cancer/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Bile_Duct_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Bile_Duct_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Bile_Duct_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Find the most appropriate TCGA cohort directory for the trait
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+lower_map = {d: d.lower() for d in subdirs}
+
+# Prioritize exact trait phrase, then synonyms
+selected_dir = None
+exact_key = 'bile_duct_cancer'
+synonym_keys = ['(chol', 'cholangio']  # CHOL code and cholangiocarcinoma keyword
+
+# Exact match
+candidates_exact = [d for d, dl in lower_map.items() if exact_key in dl]
+if candidates_exact:
+    # Choose the most specific (shortest name) if multiple
+    selected_dir = sorted(candidates_exact, key=len)[0]
+else:
+    # Synonym-based match
+    candidates_syn = [d for d, dl in lower_map.items() if any(k in dl for k in synonym_keys)]
+    if candidates_syn:
+        selected_dir = sorted(candidates_syn, key=len)[0]
+
+if selected_dir is None:
+    # No suitable cohort found; mark and stop further processing in this step
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print("No suitable TCGA cohort found for the trait. Skipping.")
+else:
+    tcga_cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    # Identify clinical and genetic file paths
+    tcga_clinical_file, tcga_genetic_file = tcga_get_relevant_filepaths(tcga_cohort_dir)
+
+    # Load dataframes
+    tcga_clinical_df = pd.read_csv(tcga_clinical_file, sep='\t', index_col=0, low_memory=False, compression='infer')
+    tcga_genetic_df = pd.read_csv(tcga_genetic_file, sep='\t', index_col=0, low_memory=False, compression='infer')
+
+    # Print clinical column names for further analysis
+    print(list(tcga_clinical_df.columns))
+
+# Step 2: Find Candidate Demographic Features
+import os
+import pandas as pd
+
+# Column names from the previous step
+column_names = ['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'age_at_initial_pathologic_diagnosis', 'albumin_result_lower_limit', 'albumin_result_specified_value', 'albumin_result_upper_limit', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'bilirubin_lower_limit', 'bilirubin_upper_limit', 'ca_19_9_level', 'ca_19_9_level_lower', 'ca_19_9_level_upper', 'cancer_first_degree_relative', 'child_pugh_classification_grade', 'cholangitis_tissue_evidence', 'creatinine_lower_level', 'creatinine_upper_limit', 'creatinine_value_in_mg_dl', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_after_initial_treatment', 'eastern_cancer_oncology_group', 'family_cancer_type_txt', 'family_member_relationship_type', 'fetoprotein_outcome_lower_limit', 'fetoprotein_outcome_upper_limit', 'fetoprotein_outcome_value', 'fibrosis_ishak_score', 'form_completion_date', 'gender', 'height', 'hist_hepato_carc_fact', 'hist_hepato_carcinoma_risk', 'histological_type', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_weight', 'inter_norm_ratio_lower_limit', 'intern_norm_ratio_upper_limit', 'is_ffpe', 'lost_follow_up', 'neoplasm_histologic_grade', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type', 'new_tumor_event_ablation_embo_tx', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'new_tumor_event_liver_transplant', 'oct_embedded', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'perineural_invasion_present', 'person_neoplasm_cancer_status', 'platelet_result_count', 'platelet_result_lower_limit', 'platelet_result_upper_limit', 'post_op_ablation_embolization_tx', 'postoperative_rx_tx', 'prothrombin_time_result_value', 'radiation_therapy', 'relative_family_cancer_history', 'residual_tumor', 'sample_type', 'sample_type_id', 'specimen_collection_method_name', 'system_version', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'total_bilirubin_upper_limit', 'tumor_tissue_site', 'vascular_tumor_cell_type', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_CHOL_mutation_broad_gene', '_GENOMIC_ID_TCGA_CHOL_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_CHOL_hMethyl450', '_GENOMIC_ID_TCGA_CHOL_exp_HiSeqV2', '_GENOMIC_ID_TCGA_CHOL_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_CHOL_mutation_bcm_gene', '_GENOMIC_ID_TCGA_CHOL_miRNA_HiSeq', '_GENOMIC_ID_TCGA_CHOL_gistic2thd', '_GENOMIC_ID_TCGA_CHOL_gistic2', '_GENOMIC_ID_TCGA_CHOL_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_CHOL_exp_HiSeqV2_exon', '_GENOMIC_ID_data/public/TCGA/CHOL/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_CHOL_mutation_ucsc_maf_gene', '_GENOMIC_ID_TCGA_CHOL_PDMRNAseq', '_GENOMIC_ID_TCGA_CHOL_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_CHOL_RPPA']
+
+# Step 1: Identify candidate demographic columns
+candidate_age_cols = [col for col in column_names if col in ['age_at_initial_pathologic_diagnosis', 'days_to_birth']]
+candidate_gender_cols = [col for col in column_names if col.lower() == 'gender']
+
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Step 2: Extract candidate columns from clinical data and preview
+clinical_df = None
+clinical_file_path = None
+
+# Try to locate the CHOL clinical file under tcga_root_dir
+try:
+    # Prefer directories that contain CHOL
+    found = False
+    for root, dirs, files in os.walk(tcga_root_dir):
+        try:
+            cpath, _ = tcga_get_relevant_filepaths(root)
+            if os.path.exists(cpath) and ('chol' in cpath.lower() or 'chol' in root.lower()):
+                clinical_file_path = cpath
+                found = True
+                break
+        except Exception:
+            pass
+    # Fallback: directly search for clinical files mentioning CHOL
+    if not found:
+        for root, dirs, files in os.walk(tcga_root_dir):
+            for f in files:
+                fl = f.lower()
+                if 'clinical' in fl and 'matrix' in fl and 'chol' in fl:
+                    clinical_file_path = os.path.join(root, f)
+                    found = True
+                    break
+            if found:
+                break
+
+    if clinical_file_path and os.path.exists(clinical_file_path):
+        # Xena clinicalMatrix is tab-separated; sample IDs as index
+        clinical_df = pd.read_csv(clinical_file_path, sep='\t', header=0, index_col=0, dtype=str)
+except Exception:
+    clinical_df = None
+
+age_preview = {}
+gender_preview = {}
+
+if clinical_df is not None:
+    age_cols_present = [c for c in candidate_age_cols if c in clinical_df.columns]
+    gender_cols_present = [c for c in candidate_gender_cols if c in clinical_df.columns]
+
+    if age_cols_present:
+        age_preview = preview_df(clinical_df[age_cols_present], n=5)
+    if gender_cols_present:
+        gender_preview = preview_df(clinical_df[gender_cols_present], n=5)
+
+print(age_preview)
+print(gender_preview)
+
+# Step 3: Select Demographic Features
+# Robust selection of demographic columns using provided candidate lists and preview dictionaries.
+# Incorporates value plausibility checks and handles empty inputs.
+
+# Helper to safely get global variables by name
+def _get_global(name, default=None):
+    return globals()[name] if name in globals() else default
+
+# Try to find the preview dictionary by known names or by heuristic overlap with candidate columns
+def _find_preview_dict(candidates, preferred_names):
+    for n in preferred_names:
+        d = _get_global(n, None)
+        if isinstance(d, dict):
+            return d
+    # Heuristic scan: choose dict with the largest overlap with candidates
+    best = None
+    best_overlap = 0
+    for k, v in globals().items():
+        if isinstance(v, dict) and v:
+            try:
+                overlap = len(set(v.keys()) & set(candidates))
+            except Exception:
+                overlap = 0
+            if overlap > best_overlap:
+                best = v
+                best_overlap = overlap
+    return best if best_overlap > 0 else {}
+
+# Parsing helpers
+def _parse_int_with_sign(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    m = re.search(r'-?\d+', s)
+    return int(m.group()) if m else None
+
+def _is_valid_age_column(col, values):
+    if not isinstance(values, list) or len(values) == 0:
+        return False
+    n = len(values)
+    min_valid = max(1, int((0.6 * n) + 0.9999))  # ceil(0.6*n)
+    lc = col.lower()
+
+    if 'days' in lc and 'birth' in lc:
+        parsed = [_parse_int_with_sign(v) for v in values]
+        valid = [p for p in parsed if isinstance(p, int)]
+        if len(valid) < min_valid:
+            return False
+        plausible = [abs(v) / 365.25 for v in valid]
+        plausible_cnt = sum(0 <= yr <= 120 for yr in plausible)
+        return plausible_cnt >= min_valid
+    else:
+        # Treat as age in years
+        parsed = [tcga_convert_age(v) for v in values]
+        valid = [p for p in parsed if isinstance(p, int)]
+        if len(valid) < min_valid:
+            return False
+        plausible_cnt = sum(0 <= p <= 120 for p in valid)
+        return plausible_cnt >= min_valid
+
+def _is_valid_gender_values(values):
+    if not isinstance(values, list) or len(values) == 0:
+        return False
+    n = len(values)
+    min_valid = max(1, int((0.6 * n) + 0.9999))  # ceil(0.6*n)
+    mapped = [tcga_convert_gender(v) for v in values]
+    valid = [m for m in mapped if m in (0, 1)]
+    return len(valid) >= min_valid
+
+def select_age_col(candidates, age_preview_dict):
+    if not candidates or not isinstance(age_preview_dict, dict) or not age_preview_dict:
+        return None
+    # Priority: explicit age in years over derived days
+    priority_order = [
+        "age_at_initial_pathologic_diagnosis",
+        "age_at_diagnosis",
+        "age_at_index",
+        "age"
+    ]
+    ordered = [p for p in priority_order if p in candidates]
+    ordered += [c for c in candidates if c not in ordered]
+
+    for c in ordered:
+        if c in age_preview_dict and _is_valid_age_column(c, age_preview_dict[c]):
+            return c
+    # As a last resort, if days_to_birth is available and valid, use it
+    for c in candidates:
+        if c.lower() == 'days_to_birth' and c in age_preview_dict and _is_valid_age_column(c, age_preview_dict[c]):
+            return c
+    return None
+
+def select_gender_col(candidates, gender_preview_dict):
+    if not candidates or not isinstance(gender_preview_dict, dict) or not gender_preview_dict:
+        return None
+    priority_order = ["gender", "sex"]
+    ordered = [p for p in priority_order if p in candidates]
+    ordered += [c for c in candidates if c not in ordered]
+
+    for c in ordered:
+        if c in gender_preview_dict and _is_valid_gender_values(gender_preview_dict[c]):
+            return c
+    return None
+
+# Retrieve candidate lists
+candidate_age_cols = _get_global('candidate_age_cols', [])
+candidate_gender_cols = _get_global('candidate_gender_cols', [])
+
+# Retrieve preview dicts (try known names, then heuristic)
+age_preview_dict = _find_preview_dict(candidate_age_cols, ['age_preview_dict', 'age_preview'])
+gender_preview_dict = _find_preview_dict(candidate_gender_cols, ['gender_preview_dict', 'gender_preview'])
+
+# Select columns using both candidates and previews
+age_col = select_age_col(candidate_age_cols, age_preview_dict)
+gender_col = select_gender_col(candidate_gender_cols, gender_preview_dict)
+
+# Explicitly print chosen columns and their preview values (first 5)
+print(f"Chosen age_col: {age_col}")
+if age_col is not None and isinstance(age_preview_dict, dict) and age_col in age_preview_dict:
+    print(f"age_col preview values: {age_preview_dict[age_col]}")
+else:
+    print("age_col preview values: None or not available")
+
+print(f"Chosen gender_col: {gender_col}")
+if gender_col is not None and isinstance(gender_preview_dict, dict) and gender_col in gender_preview_dict:
+    print(f"gender_col preview values: {gender_preview_dict[gender_col]}")
+else:
+    print("gender_col preview values: None or not available")
+
+# Step 4: Feature Engineering and Validation
+import os
+
+# 1) Extract and standardize clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=tcga_clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2) Normalize gene symbols and save
+normalized_gene_df = normalize_gene_symbols_in_index(tcga_genetic_df.copy())
+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
+gene_t = normalized_gene_df.T  # samples as index
+linked_data = selected_clinical_df.join(gene_t, how='inner')
+
+# 4) Handle missing values
+processed_df = handle_missing_values(linked_data.copy(), trait_col=trait)
+
+# 5) Determine bias and remove biased demographic features if needed
+is_biased, processed_df = judge_and_remove_biased_features(processed_df, trait=trait)
+
+# 6) Final validation and save cohort info
+# Cast to Python bool to avoid numpy.bool_ JSON serialization issues
+is_gene_available = bool((normalized_gene_df.shape[0] > 0) and (normalized_gene_df.shape[1] > 0))
+is_trait_available = bool((trait in selected_clinical_df.columns) and bool(selected_clinical_df[trait].notna().any()))
+is_biased_bool = bool(is_biased)
+
+note = (
+    f"INFO: Age column used: {age_col}; Gender column used: {gender_col}. "
+    f"Linked samples (pre-QC): {linked_data.shape[0]}, genes: {linked_data.shape[1] - selected_clinical_df.shape[1]}."
+)
+
+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
+)
+
+# 7) Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    processed_df.to_csv(out_data_file)

output/preprocess/Bile_Duct_Cancer/cohort_info.json

@@ -1,32 +1 @@
-{
-  "GSE131027": {
-    "is_usable": false,
-    "is_gene_available": false,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE107754": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": true,
-    "sample_size": 84
-  },
-  "TCGA": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 45
-  }
-}
+{"GSE131027": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 92, "note": "INFO: Trait derived from cancer type; no age/gender available in series; probes mapped via 'Gene Symbol' and gene symbols normalized by NCBI synonym dictionary."}, "GSE107754": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": true, "sample_size": 71, "note": "INFO: Age unavailable; trait derived from 'tissue' field in sample characteristics; samples without explicit tissue label were dropped during missing-value handling."}, "TCGA": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 45, "note": "INFO: Age column used: age_at_initial_pathologic_diagnosis; Gender column used: gender. Linked samples (pre-QC): 45, genes: 19848."}}

output/preprocess/Bipolar_disorder/clinical_data/GSE120340.csv

@@ -1,2 +1,2 @@
-0,1,2,3
-0.0,0.0,1.0,1.0
+,GSM3398477,GSM3398478,GSM3398479,GSM3398480,GSM3398481,GSM3398482,GSM3398483,GSM3398484,GSM3398485,GSM3398486,GSM3398487,GSM3398488,GSM3398489,GSM3398490,GSM3398491,GSM3398492,GSM3398493,GSM3398494,GSM3398495,GSM3398496,GSM3398497,GSM3398498,GSM3398499,GSM3398500,GSM3398501,GSM3398502,GSM3398503,GSM3398504,GSM3398505,GSM3398506
+Bipolar_disorder,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,,,,,,,,,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Bipolar_disorder/clinical_data/GSE120342.csv

@@ -1,2 +1,2 @@
-,0,1
-Bipolar_disorder,0.0,
+,GSM3398507,GSM3398508,GSM3398509,GSM3398510,GSM3398511,GSM3398512,GSM3398513,GSM3398514,GSM3398515,GSM3398516,GSM3398517
+Bipolar_disorder,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0

output/preprocess/Bipolar_disorder/clinical_data/GSE46416.csv

@@ -0,0 +1,2 @@
+,GSM1129903,GSM1129904,GSM1129905,GSM1129906,GSM1129907,GSM1129908,GSM1129909,GSM1129910,GSM1129911,GSM1129912,GSM1129913,GSM1129914,GSM1129915,GSM1129916,GSM1129917,GSM1129918,GSM1129919,GSM1129920,GSM1129921,GSM1129922,GSM1129923,GSM1129924,GSM1129925,GSM1129926,GSM1129927,GSM1129928,GSM1129929,GSM1129930,GSM1129931,GSM1129932,GSM1129933,GSM1129934
+Bipolar_disorder,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Bipolar_disorder/clinical_data/GSE53987.csv

@@ -1,4 +1,4 @@
-,Feature,Sample_1,Sample_2,Sample_3,Sample_4,Sample_5,Sample_6,Sample_7,Sample_8,Sample_9,Sample_10,Sample_11,Sample_12,Sample_13,Sample_14,Sample_15,Sample_16,Sample_17,Sample_18,Sample_19,Sample_20,Sample_21,Sample_22,Sample_23,Sample_24,Sample_25,Sample_26,Sample_27,Sample_28,Sample_29,Sample_30,Sample_31,Sample_32,Sample_33,Sample_34,Sample_35,Sample_36,Sample_37,Sample_38,Sample_39,Sample_40,Sample_41,Sample_42,Sample_43,Sample_44,Sample_45,Sample_46,Sample_47,Sample_48,Sample_49,Sample_50,Sample_51,Sample_52,Sample_53,Sample_54,Sample_55,Sample_56,Sample_57,Sample_58,Sample_59,Sample_60,Sample_61,Sample_62,Sample_63,Sample_64,Sample_65,Sample_66,Sample_67,Sample_68,Sample_69,Sample_70,Sample_71,Sample_72,Sample_73,Sample_74,Sample_75,Sample_76,Sample_77,Sample_78,Sample_79,Sample_80,Sample_81,Sample_82,Sample_83,Sample_84,Sample_85,Sample_86,Sample_87,Sample_88,Sample_89,Sample_90,Sample_91,Sample_92,Sample_93,Sample_94,Sample_95,Sample_96,Sample_97,Sample_98,Sample_99,Sample_100,Sample_101,Sample_102,Sample_103,Sample_104,Sample_105,Sample_106,Sample_107,Sample_108,Sample_109,Sample_110,Sample_111,Sample_112,Sample_113,Sample_114,Sample_115,Sample_116,Sample_117,Sample_118,Sample_119,Sample_120,Sample_121,Sample_122,Sample_123,Sample_124,Sample_125,Sample_126,Sample_127,Sample_128,Sample_129,Sample_130,Sample_131,Sample_132,Sample_133,Sample_134,Sample_135,Sample_136,Sample_137,Sample_138,Sample_139,Sample_140,Sample_141,Sample_142,Sample_143,Sample_144,Sample_145,Sample_146,Sample_147,Sample_148,Sample_149,Sample_150,Sample_151,Sample_152,Sample_153,Sample_154,Sample_155,Sample_156,Sample_157,Sample_158,Sample_159,Sample_160,Sample_161,Sample_162,Sample_163,Sample_164,Sample_165,Sample_166,Sample_167,Sample_168,Sample_169,Sample_170,Sample_171,Sample_172,Sample_173,Sample_174,Sample_175,Sample_176,Sample_177,Sample_178,Sample_179,Sample_180,Sample_181,Sample_182,Sample_183,Sample_184,Sample_185,Sample_186,Sample_187,Sample_188,Sample_189,Sample_190,Sample_191,Sample_192,Sample_193,Sample_194,Sample_195,Sample_196,Sample_197,Sample_198,Sample_199,Sample_200,Sample_201,Sample_202,Sample_203,Sample_204,Sample_205
-Bipolar_disorder,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-Age,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-Gender,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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output/preprocess/Bipolar_disorder/clinical_data/GSE62191.csv

@@ -1,4 +1,3 @@
-0,1,2,3,4,5,6
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output/preprocess/Bipolar_disorder/clinical_data/GSE67311.csv

@@ -1,143 +1,2 @@
-,Bipolar_disorder
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output/preprocess/Bipolar_disorder/clinical_data/GSE92538.csv

@@ -1,4 +1,4 @@
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output/preprocess/Bipolar_disorder/code/GSE120340.py

@@ -0,0 +1,228 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE120340"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE120340"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE120340.csv"
+out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE120340.csv"
+out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE120340.csv"
+json_path = "./output/z1/preprocess/Bipolar_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix whole-genome expression microarrays were used.
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the sample characteristics dictionary:
+# {0: ['disease state: control', 'disease state: SCZ', 'disease state: BD(+)', 'disease state: BD(-)'],
+#  1: ['laterality: left', 'laterality: right']}
+trait_row = 0
+age_row = None
+gender_row = None
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    # Extract substring after the first colon if present
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    val = v.lower().strip()
+
+    # Normalize some variants
+    val = val.replace('bipolar disorder', 'bd')
+    val = val.replace('psychotic bd', 'bd(+)')
+    val = val.replace('non-psychotic bd', 'bd(-)')
+
+    # Map to binary for Bipolar_disorder: control -> 0, BD(+)/BD(-) -> 1, SCZ -> None
+    if val in {'control', 'healthy control', 'normal'}:
+        return 0
+    if val in {'bd', 'bd(+)', 'bd(-)'}:
+        return 1
+    if val in {'scz', 'schizophrenia'}:
+        return None
+    # If the cell includes BD text anywhere, map conservatively to 1
+    if 'bd' in val or 'bipolar' in val:
+        return 1
+    # Otherwise unknown
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    val = v.lower()
+    if any(k in val for k in ['na', 'n/a', 'unknown', 'missing']):
+        return None
+    m = re.search(r'[-+]?\d*\.?\d+', val)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    val = v.lower().strip()
+    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_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide probe ID column and gene symbol column from gene_annotation
+prob_col = 'ID' if 'ID' in gene_annotation.columns else gene_annotation.columns[0]
+
+# Try to find a gene symbol column
+symbol_candidates = [c for c in gene_annotation.columns if re.search(r'symbol', c, flags=re.I)]
+if len(symbol_candidates) > 0:
+    gene_col = symbol_candidates[0]
+else:
+    # Fallbacks commonly seen in GEO/GPL annotations
+    for cand in ['Gene Symbol', 'Gene.symbol', 'GENE_SYMBOL', 'Symbol', 'SYMBOL', 'gene_symbol']:
+        if cand in gene_annotation.columns:
+            gene_col = cand
+            break
+    else:
+        # Last resort: use Description (extract_human_gene_symbols will try to parse symbols from text)
+        gene_col = 'Description' if 'Description' in gene_annotation.columns else gene_annotation.columns[-1]
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level data into 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 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)
+
+# Derive availability flags from processed data
+is_gene_available_final = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_final = (trait in unbiased_linked_data.columns) and (len(unbiased_linked_data) > 0)
+
+# Optional notes
+notes = []
+notes.append("INFO: SCZ samples mapped to None for trait and were excluded during missing-value handling.")
+if "Age" not in unbiased_linked_data.columns:
+    notes.append("INFO: Age not available in clinical annotations.")
+if "Gender" not in unbiased_linked_data.columns:
+    notes.append("INFO: Gender not available in clinical annotations.")
+note = " ".join(notes) if notes else "INFO: No additional notes."
+
+# 5. Conduct quality check and save the cohort information.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 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/Bipolar_disorder/code/GSE120342.py

@@ -0,0 +1,206 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE120342"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE120342"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE120342.csv"
+out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE120342.csv"
+out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE120342.csv"
+json_path = "./output/z1/preprocess/Bipolar_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability (SuperSeries mentions transcriptomes -> likely contains gene expression)
+is_gene_available = True
+
+# 2) Variable availability
+# From sample characteristics: {0: ['disease state: control', 'disease state: SCZ', 'disease state: BD(-)', 'disease state: BD(+)'], 
+#                               1: ['laterality: right', 'laterality: left']}
+trait_row = 0        # disease state contains BD labels
+age_row = None       # not available
+gender_row = None    # not available
+
+# 2.2) Converters
+def _after_colon(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Map presence of bipolar disorder to 1; control and SCZ to 0
+    if 'bd' in vl:       # 'bd(+)' or 'bd(-)' or 'bipolar'
+        return 1
+    if 'control' in vl or 'scz' in vl or 'schizo' in vl:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # extract the first number (years)
+    m = re.search(r'[-+]?\d*\.?\d+', v)
+    if m:
+        try:
+            return float(m.group(0))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if any(k in vl for k in ['female', 'f']):
+        return 0
+    if any(k in vl for k in ['male', 'm']):
+        return 1
+    return None
+
+# 3) Save initial metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction if available
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide the identifier and gene symbol columns based on the annotation preview:
+identifier_col = 'ID'       # Matches probe IDs like 'cg00000292'
+gene_symbol_col = 'Symbol'  # Contains human gene symbols
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=identifier_col, gene_col=gene_symbol_col)
+
+# Fallback: if mapping is unexpectedly empty, try 'Name' as probe identifier
+if mapping_df.empty and 'Name' in gene_annotation.columns:
+    mapping_df = get_gene_mapping(gene_annotation, prob_col='Name', gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level measurements 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 the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Determine dataset suitability (detect methylation data via cg probes / CPG_ISLAND annotation)
+is_methylation = False
+try:
+    if 'CPG_ISLAND' in gene_annotation.columns:
+        is_methylation = True
+    if 'ID' in mapping_df.columns and mapping_df['ID'].astype(str).str.startswith('cg').any():
+        is_methylation = True
+except Exception:
+    pass
+is_gene_available_final = not is_methylation
+is_trait_available_final = True  # trait_row existed and clinical features were extracted
+
+note_msg = "WARNING: Matrix appears to be DNA methylation (Illumina CpG 'cg' probes); treated as not gene expression for this pipeline."
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note_msg
+)
+
+# 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/Bipolar_disorder/code/GSE45484.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE45484"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE45484"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE45484.csv"
+out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE45484.csv"
+out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE45484.csv"
+json_path = "./output/z1/preprocess/Bipolar_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Determine data availability based on provided background and sample characteristics
+is_gene_available = True  # Gene expression microarray from whole blood RNA
+trait_row = None          # All subjects have bipolar disorder -> trait not variable here
+age_row = 4               # 'age: <number>'
+gender_row = 3            # 'sex: F'/'sex: M'
+
+# Converters
+def convert_trait(x):
+    # Trait (Bipolar_disorder) is constant in this cohort; mark as unavailable
+    return None
+
+def _after_colon(val: str) -> str:
+    if val is None:
+        return ""
+    s = str(val)
+    return s.split(":", 1)[1].strip() if ":" in s else s.strip()
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v == "" or v.lower() in {"na", "n/a", "nan", "null", "unknown", "?", "none"}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x).strip().lower()
+    if v in {"f", "female", "woman", "women"}:
+        return 0
+    if v in {"m", "male", "man", "men"}:
+        return 1
+    return None
+
+# Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction: only proceed if trait is available (not in this dataset)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    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, 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
+# ILMN_* identifiers are Illumina probe IDs, not human gene symbols
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide identifier and gene symbol columns based on annotation preview
+id_col = 'ID'  # ILMN_* probe IDs
+gene_col = 'Symbol' if 'Symbol' in gene_annotation.columns else (
+    'ILMN_Gene' if 'ILMN_Gene' in gene_annotation.columns else None
+)
+
+if gene_col is None:
+    raise ValueError("No suitable gene symbol column found in annotation (expected 'Symbol' or 'ILMN_Gene').")
+
+# 2. Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+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)
+
+# 2-6. Proceed only if clinical data with trait exists; otherwise perform final validation noting trait unavailability
+has_selected_clinical = (
+    ('selected_clinical_data' in globals()) and
+    (selected_clinical_data is not None) and
+    (not selected_clinical_data.empty) and
+    (trait in selected_clinical_data.index)
+)
+
+if has_selected_clinical:
+    # 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 quality validation and metadata saving
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Linked clinical-genetic data generated from GEO series."
+    )
+
+    # 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:
+    # Trait is unavailable in this cohort (all subjects have Bipolar_disorder); skip linking but record correct metadata
+    print("Skipping linking and downstream steps: trait is not available/variable in this cohort.")
+    df_for_validation = normalized_gene_data.T  # Non-empty placeholder to avoid abnormality override
+    _ = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=df_for_validation,
+        note="INFO: Trait not variable/recorded in this cohort (all subjects have Bipolar_disorder). Only gene data saved."
+    )

output/preprocess/Bipolar_disorder/code/GSE46416.py

@@ -0,0 +1,211 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE46416"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE46416"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE46416.csv"
+out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE46416.csv"
+out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE46416.csv"
+json_path = "./output/z1/preprocess/Bipolar_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Series describes gene expression profiling in blood; not miRNA-only or methylation.
+
+# 2) Variable availability and conversion functions
+
+# From the provided Sample Characteristics Dictionary:
+# 0: tissue
+# 1: disease status: bipolar disorder (BD) / control
+# 2: bd phase: mania / euthymia
+# 3: patient identifier
+trait_row = 1
+age_row = None
+gender_row = None
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x).strip().strip('"').strip()
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    vl = v.lower()
+    # Map bipolar disorder cases to 1, controls to 0
+    if 'control' in vl:
+        return 0
+    # capture terms indicating bipolar disorder
+    if 'bipolar' in vl or re.search(r'\bbd\b', vl):
+        return 1
+    return None
+
+def convert_age(x):
+    # Not available for this cohort; keep here for completeness
+    v = _after_colon(x)
+    if not v:
+        return None
+    # extract first float/int number as age in years
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    return float(m.group(1)) if m else None
+
+def convert_gender(x):
+    # Not available for this cohort; keep here for completeness
+    v = _after_colon(x)
+    if not v:
+        return None
+    vl = v.lower()
+    if vl in ['male', 'm']:
+        return 1
+    if vl in ['female', 'f']:
+        return 0
+    # handle common encodings
+    if 'male' in vl:
+        return 1
+    if 'female' in vl:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if clinical data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=None,
+        gender_row=gender_row,
+        convert_gender=None
+    )
+    clinical_selected_preview = preview_df(selected_clinical_df)
+    print(clinical_selected_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
+# Based on the provided probe-like numeric IDs (e.g., '2315252'), these are not human gene symbols.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide columns for mapping based on annotation preview:
+# Probe/ID column: 'ID' matches gene_data index (e.g., '2315252')
+# Gene symbol column: 'gene_symbol'
+probe_col = 'ID'
+gene_col = 'gene_symbol'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level data to gene-level 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; guard against empty dataframe after missing-value handling
+if linked_data.shape[0] == 0:
+    is_trait_biased = True
+    unbiased_linked_data = linked_data
+else:
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and save cohort info
+# Ensure Python-native bools for JSON serialization
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in selected_clinical_df.index) and bool(selected_clinical_df.loc[trait].notna().any()))
+
+# If the linked data is empty after processing, mark availability flags as False
+if unbiased_linked_data.shape[0] == 0:
+    is_gene_available_final = False
+    # trait info might still exist in clinical, but dataset is unusable for analysis without samples
+    # Keep trait flag as is to record availability; validate_and_save_cohort_info will also re-check
+is_trait_biased = bool(is_trait_biased)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note="INFO: Gene-level mapping may be sparse; platform annotation had limited gene_symbol entries."
+)
+
+# 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/Bipolar_disorder/code/GSE46449.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE46449"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE46449"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE46449.csv"
+out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE46449.csv"
+out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE46449.csv"
+json_path = "./output/z1/preprocess/Bipolar_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression availability
+is_gene_available = True  # Affymetrix microarray gene expression (not miRNA/methylation)
+
+# 2) Variable availability (rows) inferred from sample characteristics
+trait_row = 1        # 'genotype: bipolar patient' / 'genotype: control subject'
+age_row = 2          # 'age: <number>'
+gender_row = None    # Only 'gender: male' observed -> constant, not useful
+
+# 2) Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    if not isinstance(x, str):
+        return None
+    parts = x.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if any(k in vl for k in ['control', 'healthy', 'normal']):
+        return 0
+    if ('bipolar' in vl) or ('bpd' in vl) or ('bp ' in vl) or (vl == 'bp') or ('patient' in vl) or ('case' in vl):
+        return 1
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    nums = re.findall(r'[-+]?\d*\.?\d+', v)
+    if not nums:
+        return None
+    try:
+        age_val = float(nums[0])
+        if 0 <= age_val <= 120:
+            return age_val
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if 'female' in vl or vl == 'f':
+        return 0
+    if 'male' in vl or vl == '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 (only if trait is available)
+if trait_row is not None:
+    clinical_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(clinical_selected_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    clinical_selected_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2. Identify columns for probe IDs and gene symbols, then build mapping dataframe
+probe_col = 'ID'  # Matches probe IDs in the expression data (e.g., '1007_s_at')
+gene_symbol_col = 'Gene Symbol'  # Contains gene symbols (may include multiple symbols separated by delimiters)
+
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+try:
+    clinical_df_link = clinical_selected_df
+except NameError:
+    clinical_df_link = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(clinical_df_link, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Gender not available (constant) in source; not included."
+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/Bipolar_disorder/code/GSE53987.py

@@ -0,0 +1,197 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE53987"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE53987"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE53987.csv"
+out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE53987.csv"
+out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE53987.csv"
+json_path = "./output/z1/preprocess/Bipolar_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Affymetrix U133 Plus 2.0 microarray => mRNA gene expression
+
+# 2) Variable availability and converters
+
+# Data availability (row indices from Sample Characteristics Dictionary)
+trait_row = 7      # 'disease state'
+age_row = 0        # 'age'
+gender_row = 1     # 'gender'
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        val = s.split(':', 1)[1]
+    else:
+        val = s
+    return val.strip()
+
+def convert_trait(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    # map to binary: Bipolar_disorder case=1, control=0, other diagnoses excluded
+    if 'bipolar' in v or v in {'bpd', 'bp', 'bd'}:
+        return 1
+    if v in {'control', 'healthy', 'normal'}:
+        return 0
+    # exclude other psychiatric diagnoses (e.g., schizophrenia, MDD)
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {'na', 'n/a', 'unknown', ''}:
+        return None
+    try:
+        # prefer integer if clean, else float
+        f = float(v)
+        return int(f) if f.is_integer() else f
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {'female', 'f'}:
+        return 0
+    if v 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 (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
+# The gene identifiers like "1007_s_at", "1552258_at" are Affymetrix probe set IDs, not HGNC gene symbols.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2) Identify mapping columns and build probe-to-gene mapping dataframe
+probe_col = 'ID'            # Matches probe IDs in expression data (e.g., '1007_s_at')
+gene_symbol_col = 'Gene Symbol'  # Column with gene symbols in annotation
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3) Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 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)
+
+# 5. Final validation and save cohort info using actual availability indicators
+is_gene_available_final = (normalized_gene_data is not None) and (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_final = (('trait_row' in globals()) and (trait_row is not None)) and (('selected_clinical_df' in globals()) and (trait in selected_clinical_df.index))
+
+note = ("INFO: Trait constructed as bipolar disorder (case=1) vs control (0); "
+        "samples with other diagnoses (schizophrenia/MDD) were set to missing and removed during preprocessing. "
+        "Multiple brain regions are present; no tissue harmonization performed here.")
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data 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/Bipolar_disorder/code/GSE62191.py

@@ -0,0 +1,213 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE62191"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE62191"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE62191.csv"
+out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE62191.csv"
+out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE62191.csv"
+json_path = "./output/z1/preprocess/Bipolar_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Based on series title/summary indicating mRNA gene expression profiling
+
+# 2) Variable availability and converters
+# From Sample Characteristics:
+# - trait_row: 1 ('disease state: healthy control' | 'bipolar disorder' | 'schizophrenia')
+# - age_row: 2 ('age: XX yr')
+# - gender_row: None (only 'gender: male' observed; constant feature -> not useful)
+trait_row = 1
+age_row = 2
+gender_row = None
+
+def convert_trait(x):
+    # Map bipolar disorder to 1; treat schizophrenia and healthy controls as non-BD (0)
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    val = str(x)
+    if ':' in val:
+        val = val.split(':', 1)[1]
+    v = val.strip().lower()
+    if 'bipolar' in v:
+        return 1
+    if ('healthy' in v) or ('control' in v):
+        return 0
+    if 'schizo' in v:
+        return 0
+    return None
+
+def convert_age(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    val = str(x)
+    if ':' in val:
+        val = val.split(':', 1)[1]
+    m = re.search(r'(\d+(\.\d+)?)', val)
+    if m:
+        try:
+            num = float(m.group(1))
+            return num
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    val = str(x)
+    if ':' in val:
+        val = val.split(':', 1)[1]
+    v = val.strip().lower()
+    if 'female' in v:
+        return 0
+    if '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 (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
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    print(clinical_preview)
+    print(f"Selected clinical features shape: {selected_clinical_df.shape}")
+
+    # Save clinical data
+    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
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2) Decide columns and create mapping dataframe
+prob_col = 'ID'           # Matches probe IDs in gene_data (numeric strings like '12', '13', ...)
+gene_col = 'GENE_SYMBOL'  # Stores 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
+from json import JSONDecodeError
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Ensure clinical data is available in this step: reload if needed
+try:
+    selected_clinical_df
+except NameError:
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality 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 linked_data.columns) and (linked_data[trait].notna().any()))
+is_trait_biased_bool = bool(is_trait_biased)
+
+note = "INFO: Age available; Gender not available in clinical annotations for this series."
+
+def run_validate():
+    return 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
+    )
+
+try:
+    is_usable = run_validate()
+except (TypeError, JSONDecodeError):
+    # Repair/reset JSON file and retry
+    os.makedirs(os.path.dirname(json_path), exist_ok=True)
+    with open(json_path, "w") as f:
+        f.write("{}")
+    is_usable = run_validate()
+
+# 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/Bipolar_disorder/code/GSE67311.py

@@ -0,0 +1,207 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE67311"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE67311"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE67311.csv"
+out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE67311.csv"
+out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE67311.csv"
+json_path = "./output/z1/preprocess/Bipolar_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix Human Gene 1.1 ST arrays indicate gene expression data.
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability based on the provided Sample Characteristics Dictionary
+trait_row = 7     # 'bipolar disorder: Yes/No/-'
+age_row = None    # Not available in the provided dictionary
+gender_row = None # Not available in the provided dictionary
+
+# 2.2 Data Type Conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        return val.strip()
+    return x
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None or v == '' or v in {'-', 'na', 'n/a', 'unknown', 'NA', 'N/A', 'Unknown'}:
+        return None
+    v_low = str(v).strip().lower()
+    if v_low in {'yes', 'y', 'true', '1'}:
+        return 1
+    if v_low in {'no', 'n', 'false', '0'}:
+        return 0
+    return None
+
+def convert_age(x):
+    # Not used (age_row is None); implemented for completeness.
+    v = _extract_value(x)
+    if v is None or v == '' or str(v).strip().lower() in {'-', 'na', 'n/a', 'unknown'}:
+        return None
+    try:
+        # Extract first numeric occurrence
+        num = re.findall(r"[-+]?\d*\.?\d+", str(v))
+        return float(num[0]) if num else None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Not used (gender_row is None); implemented for completeness.
+    v = _extract_value(x)
+    if v is None or v == '' or str(v).strip().lower() in {'-', 'na', 'n/a', 'unknown'}:
+        return None
+    v_low = str(v).strip().lower()
+    if v_low in {'female', 'f', '0'}:
+        return 0
+    if v_low in {'male', 'm', '1'}:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide columns: probe IDs are in 'ID'; gene info (symbols embedded) is in 'gene_assignment'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link 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 demographic features are biased; remove biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and save metadata
+covariate_cols_present = [c for c in [trait, 'Age', 'Gender'] if c in unbiased_linked_data.columns]
+gene_cols_present = [c for c in unbiased_linked_data.columns if c not in covariate_cols_present]
+is_trait_available = bool((trait in unbiased_linked_data.columns) and unbiased_linked_data[trait].notna().any())
+is_gene_available = bool(len(gene_cols_present) > 0)
+is_trait_biased = bool(is_trait_biased)
+
+note = "INFO: Mapped Affymetrix Human Gene 1.1 ST probe IDs to gene symbols; no age/gender available in sample characteristics."
+
+# Retry logic to avoid JSON serialization issues due to a corrupted/legacy file
+try:
+    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
+    )
+except Exception:
+    # Remove possibly corrupted JSON and retry once
+    if os.path.exists(json_path):
+        os.remove(json_path)
+    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/Bipolar_disorder/code/GSE92538.py

@@ -0,0 +1,193 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE92538"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE92538"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE92538.csv"
+out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE92538.csv"
+out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE92538.csv"
+json_path = "./output/z1/preprocess/Bipolar_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Affymetrix U133 microarray gene expression per background info
+
+# 2) Variable availability and conversion functions
+trait_row = 2   # 'diagnosis' with categories including 'Bipolar Disorder'
+age_row = 8     # 'age'
+gender_row = 6  # 'gender'
+
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(x).strip()
+
+def convert_trait(x):
+    val = _after_colon(x).lower()
+    if val in {"bipolar disorder", "bipolar"}:
+        return 1
+    if val in {"control", "healthy control"}:
+        return 0
+    # Other diagnoses are not part of the binary trait for Bipolar Disorder
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val in {"", "na", "NA", None}:
+        return None
+    try:
+        age = float(val)
+        # human plausible range
+        if 0 <= age <= 120:
+            return age
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    val = _after_colon(x).lower()
+    if val in {"f", "female"}:
+        return 0
+    if val in {"m", "male"}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait 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
+# Decide annotation columns: probe IDs are in 'ID'; gene symbols are in 'SYMBOL'
+id_col = 'ID'
+gene_symbol_col = 'SYMBOL'
+
+# 2) Build mapping dataframe (columns: 'ID' and 'Gene')
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_symbol_col)
+
+# 3) Apply mapping to convert probe-level to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias evaluation and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Determine availability flags robustly
+gene_available_flag = (normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0)
+trait_available_flag = (trait in linked_data.columns)
+
+try:
+    iga = is_gene_available
+except NameError:
+    iga = gene_available_flag
+
+try:
+    ita = is_trait_available
+except NameError:
+    ita = trait_available_flag
+
+# 5. Final validation and save cohort info
+note_text = ("INFO: Diagnosis converted to binary trait (Bipolar Disorder=1, Control=0); "
+             "other diagnoses set to None prior to missing-value handling. "
+             "Gene symbols normalized using NCBI synonym mapping.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=iga,
+    is_trait_available=ita,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note_text
+)
+
+# 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)