Add files using upload-large-folder tool

output/preprocess/Endometrioid_Cancer/clinical_data/GSE65986.csv

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output/preprocess/Endometrioid_Cancer/code/GSE120490.py

@@ -0,0 +1,164 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE120490"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE120490"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE120490.csv"
+out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE120490.csv"
+out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE120490.csv"
+json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix U133 Plus 2.0 microarray platform indicates gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+trait_row = 0  # 'matastasis: No/Yes' relates to cancer metastasis status
+age_row = None  # No age information available in sample characteristics
+gender_row = None  # No gender information available (endometrial cancer typically affects females)
+
+def convert_trait(value):
+    """Convert metastasis status to binary: No=0, Yes=1"""
+    if value is None:
+        return None
+    value_str = str(value).split(':')[-1].strip().lower()
+    if value_str == 'no':
+        return 0
+    elif value_str == 'yes':
+        return 1
+    else:
+        return None
+
+def convert_age(value):
+    """Age conversion function (not used as age data not available)"""
+    return None
+
+def convert_gender(value):
+    """Gender conversion function (not used as gender data not available)"""
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+save_cohort_info = 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 is_trait_available:
+    selected_clinical_data = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    print("Preview of selected clinical data:")
+    print(preview_df(selected_clinical_data))
+    
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_data.to_csv(out_clinical_data_file)
+    print(f"Clinical data saved to {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
+# Analyze the gene identifiers from the previous step output
+gene_identifiers = ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',
+                   '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',
+                   '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',
+                   '1552263_at', '1552264_a_at', '1552266_at']
+
+print("Sample gene identifiers:")
+for identifier in gene_identifiers[:10]:
+    print(f"  {identifier}")
+
+# These identifiers follow the Affymetrix probe ID pattern with suffixes like "_at", "_s_at", "_a_at", etc.
+# They are not human gene symbols (which would be like TP53, BRCA1, EGFR, etc.)
+# Therefore, they require mapping to gene symbols
+
+requires_gene_mapping = True
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1. Identify mapping columns: 'ID' contains probe IDs, 'Gene Symbol' contains gene symbols
+probe_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get gene mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+print(f"Gene expression data shape after mapping: {gene_data.shape}")
+print("Sample gene symbols:")
+print(gene_data.index[:10].tolist())
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, 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.
+is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Endometrioid_Cancer/code/GSE40785.py

@@ -0,0 +1,160 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE40785"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE40785"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE40785.csv"
+out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE40785.csv"
+out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE40785.csv"
+json_path = "./output/z2/preprocess/Endometrioid_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 pandas as pd
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # RNA expression data from Illumina platform
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+trait_row = 1  # histology information is in key 1
+age_row = None  # no age information available
+gender_row = None  # no gender information available
+
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    """Convert histology to binary endometrioid cancer (1) vs others (0)"""
+    if value is None:
+        return None
+    
+    # Extract value after colon
+    if ':' in str(value):
+        histology = str(value).split(':', 1)[1].strip()
+    else:
+        histology = str(value).strip()
+    
+    # Check if it's endometrioid
+    if 'Endometrioid' in histology:
+        return 1
+    else:
+        return 0
+
+def convert_age(value):
+    """Age conversion function (not used as age data not available)"""
+    return None
+
+def convert_gender(value):
+    """Gender conversion function (not used as gender data not available)"""
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_data = geo_select_clinical_features(
+        clinical_data, trait, trait_row, convert_trait,
+        age_row, convert_age, gender_row, convert_gender
+    )
+    
+    print("Selected clinical data preview:")
+    print(preview_df(selected_clinical_data))
+    
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_data.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
+# Review the gene identifiers from the previous step output
+# The identifiers follow the pattern 'ILMN_' followed by numbers
+# These are Illumina probe IDs, not human gene symbols
+# Human gene symbols would be names like TP53, BRCA1, MYC, etc.
+
+print("Gene identifiers observed: ILMN_1343291, ILMN_1343295, ILMN_1651199, etc.")
+print("These are Illumina probe IDs that need to be mapped to gene symbols.")
+
+requires_gene_mapping = True
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1. Identify the relevant columns in gene annotation
+# 'ID' column contains Illumina probe IDs matching the gene expression data
+# 'Symbol' column contains the gene symbols
+prob_col = 'ID'
+gene_col = 'Symbol'
+
+# 2. Get gene mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+print(f"Gene expression data shape after mapping: {gene_data.shape}")
+print(f"First few gene symbols: {list(gene_data.index[:10])}")
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, 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.
+is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Endometrioid_Cancer/code/GSE65986.py

@@ -0,0 +1,168 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE65986"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE65986"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE65986.csv"
+out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE65986.csv"
+out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE65986.csv"
+json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True  # This dataset uses Affymetrix U133plus2 array for gene expression
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+trait_row = 0  # histology field contains Endometrioid vs other cancer types
+age_row = 1    # age field contains age values
+gender_row = None  # no gender information available in sample characteristics
+
+# 2.2 Data Type Conversion Functions
+
+def convert_trait(value):
+    """Convert trait to binary: 1 for Endometrioid, 0 for others"""
+    if value is None:
+        return None
+    # Extract value after colon
+    val = value.split(':')[-1].strip() if ':' in value else value.strip()
+    if val == 'Endometrioid':
+        return 1
+    elif val in ['Clear', 'Serous']:
+        return 0
+    else:
+        return None
+
+def convert_age(value):
+    """Convert age to continuous numeric value"""
+    if value is None:
+        return None
+    # Extract value after colon
+    val = value.split(':')[-1].strip() if ':' in value else value.strip()
+    try:
+        return float(val)
+    except (ValueError, TypeError):
+        return None
+
+def convert_gender(value):
+    """Not applicable - gender data not available"""
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+save_result = 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_data = geo_select_clinical_features(
+        clinical_data, trait, trait_row, convert_trait,
+        age_row, convert_age, gender_row, convert_gender
+    )
+    
+    # Preview the output dataframe
+    print("Preview of selected clinical data:")
+    print(preview_df(selected_clinical_data))
+    
+    # Save to CSV file
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_data.to_csv(out_clinical_data_file, index=False)
+    print(f"Clinical data saved to {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("Sample gene identifiers from the dataset:")
+print(gene_data.index[:20].tolist())
+
+# These identifiers follow Affymetrix probe ID format (numbers + suffixes like _at, _s_at, _g_at, _i_at, _a_at)
+# They are not human gene symbols, which would be in format like BRCA1, TP53, etc.
+# Therefore, they need to be mapped to gene symbols
+
+requires_gene_mapping = True
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1. Identify the correct columns for mapping
+# 'ID' contains the probe identifiers matching the gene expression data
+# 'Gene Symbol' contains the gene symbols we want to map to
+probe_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get gene mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+print(f"Original probe data shape: {get_genetic_data(matrix_file).shape}")
+print(f"Mapped gene data shape: {gene_data.shape}")
+print(f"First few gene symbols: {gene_data.index[:10].tolist()}")
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, 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.
+is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)
+
+# 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/Endometrioid_Cancer/code/GSE66667.py

@@ -0,0 +1,160 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE66667"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE66667"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE66667.csv"
+out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE66667.csv"
+out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE66667.csv"
+json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Microarrays were employed to elucidate global transcription
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+trait_row = 0  # histology information available, including 'Endometrioid'
+age_row = None  # No age information in sample characteristics
+gender_row = None  # No gender information in sample characteristics
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert histology to binary: 1 for Endometrioid, 0 for others"""
+    if value is None:
+        return None
+    # Extract value after colon
+    if ':' in str(value):
+        histology_type = str(value).split(':')[1].strip()
+    else:
+        histology_type = str(value).strip()
+    
+    return 1 if histology_type == 'Endometrioid' else 0
+
+def convert_age(value):
+    """Age conversion function (not used since age_row is None)"""
+    return None
+
+def convert_gender(value):
+    """Gender conversion function (not used since gender_row is None)"""
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+save_cohort_info = 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_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
+    )
+    
+    # Preview the output
+    print("Clinical data preview:")
+    print(preview_df(selected_clinical_data))
+    
+    # Save to CSV
+    selected_clinical_data.to_csv(out_clinical_data_file)
+    print(f"Clinical data saved to {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
+# These identifiers appear to be Affymetrix probe set IDs (e.g., '1007_s_at', '1053_at')
+# They follow the typical pattern of numbers followed by probe set suffixes like '_at', '_s_at', '_a_at'
+# Human gene symbols would be alphabetic names like 'BRCA1', 'TP53', 'EGFR'
+# Therefore, these probe IDs need to be mapped to human gene symbols
+
+requires_gene_mapping = True
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1. Identify the columns for mapping
+# From the preview, 'ID' contains the probe identifiers (same as gene_data index)
+# 'Gene Symbol' contains the gene symbols we need to map to
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get gene mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+print(f"Original probe data shape: {get_genetic_data(matrix_file).shape}")
+print(f"Mapped gene data shape: {gene_data.shape}")
+print(f"First 10 gene symbols: {list(gene_data.index[:10])}")
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, 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.
+is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Endometrioid_Cancer/code/GSE68600.py

@@ -0,0 +1,159 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE68600"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE68600"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE68600.csv"
+out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE68600.csv"
+out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE68600.csv"
+json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True  # This is an Affymetrix gene expression microarray dataset
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+trait_row = 4  # histology information is in key 4
+age_row = None  # No age information available
+gender_row = None  # All samples are female (constant), not useful
+
+# 2.2 Data Type Conversion Functions
+
+def convert_trait(value):
+    """Convert histology to binary (0/1) for endometrioid cancer presence"""
+    if ':' in value:
+        histology = value.split(':')[1].strip().lower()
+        # Check if endometrioid is mentioned in the histology
+        if 'endometrioid' in histology:
+            return 1
+        else:
+            return 0
+    return None
+
+def convert_age(value):
+    """Convert age - not applicable since age data is not available"""
+    return None
+
+def convert_gender(value):
+    """Convert gender - not applicable since all samples are female"""
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+is_usable = 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_data = geo_select_clinical_features(
+        clinical_data, 
+        trait, 
+        trait_row, 
+        convert_trait, 
+        age_row, 
+        convert_age, 
+        gender_row, 
+        convert_gender
+    )
+    
+    print("Preview of selected clinical data:")
+    print(preview_df(selected_clinical_data))
+    
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_data.to_csv(out_clinical_data_file)
+    print(f"Clinical data saved to {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
+# Examine the gene identifiers shown in the previous step output
+# The identifiers follow patterns like 'A28102_at', 'AB000114_at', 'AB000381_s_at'
+# These are Affymetrix microarray probe set identifiers, not human gene symbols
+# Human gene symbols would be names like 'BRCA1', 'TP53', 'EGFR', etc.
+# The '_at' and '_s_at' suffixes are characteristic of Affymetrix probe nomenclature
+
+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. Based on the analysis, 'ID' column contains probe identifiers matching gene expression data,
+# and 'Gene Symbol' column contains the human gene symbols we need
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Extract gene mapping dataframe with probe ID to gene symbol mapping
+gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+print(f"Gene expression data shape after mapping: {gene_data.shape}")
+print(f"First 10 gene symbols: {list(gene_data.index[:10])}")
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, 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.
+is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Endometrioid_Cancer/code/GSE73551.py

@@ -0,0 +1,147 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE73551"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73551"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE73551.csv"
+out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE73551.csv"
+out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE73551.csv"
+json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True  # This appears to be gene expression data for compositional analysis
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+trait_row = 0  # Cell type information is in row 0, includes ENDOMETRIOID
+age_row = None  # No age information available
+gender_row = None  # No gender information available
+
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    """Convert cell type to binary: 1 for ENDOMETRIOID, 0 for others"""
+    if pd.isna(value):
+        return None
+    value_str = str(value).upper()
+    if ':' in value_str:
+        value_str = value_str.split(':', 1)[1].strip()
+    return 1 if 'ENDOMETRIOID' in value_str else 0
+
+def convert_age(value):
+    """Convert age to continuous (not available in this dataset)"""
+    return None
+
+def convert_gender(value):
+    """Convert gender to binary (not available in this dataset)"""
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, 
+                             is_gene_available=is_gene_available, 
+                             is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_data = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait, 
+                                                        age_row, convert_age, gender_row, convert_gender)
+    print("Preview of selected clinical data:")
+    print(preview_df(selected_clinical_data))
+    
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_data.to_csv(out_clinical_data_file)
+    print(f"Clinical data saved to {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
+# Examine the gene identifiers to determine if mapping is required
+print("Gene identifiers observed:")
+print("Index(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',")
+print("       '14', '15', '16', '17', '18', '19', '20'],")
+print("      dtype='object', name='ID')")
+print("\nThese are numerical identifiers, not human gene symbols.")
+print("Human gene symbols are typically alphanumeric (e.g., TP53, BRCA1, EGFR).")
+print("These numerical IDs likely represent probe IDs or platform-specific identifiers.")
+
+requires_gene_mapping = True
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1. Identify the correct columns for mapping
+# The 'ID' column matches the gene identifiers in gene expression data
+# The 'GeneSymbol' column contains the actual gene symbols
+prob_col = 'ID'
+gene_col = 'GeneSymbol'
+
+# 2. Get gene mapping dataframe using the get_gene_mapping function
+gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+print(f"Gene expression data shape after mapping: {gene_data.shape}")
+print(f"First 10 genes: {list(gene_data.index[:10])}")
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, 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.
+is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Endometrioid_Cancer/code/GSE73614.py

@@ -0,0 +1,181 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE73614"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73614"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE73614.csv"
+out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE73614.csv"
+out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE73614.csv"
+json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+trait_row = None  # No trait information available in sample characteristics
+age_row = None    # No age information available in sample characteristics  
+gender_row = None # No gender information available in sample characteristics
+
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    """Convert trait values to binary (0 for non-endometrioid, 1 for endometrioid)"""
+    if value is None:
+        return None
+    value_str = str(value).lower()
+    if 'endometrioid' in value_str:
+        return 1
+    else:
+        return 0
+
+def convert_age(value):
+    """Convert age to continuous values"""
+    if value is None:
+        return None
+    try:
+        if ':' in str(value):
+            age_str = str(value).split(':')[1].strip()
+        else:
+            age_str = str(value).strip()
+        return float(age_str)
+    except (ValueError, IndexError):
+        return None
+
+def convert_gender(value):
+    """Convert gender to binary (0 for female, 1 for male)"""
+    if value is None:
+        return None
+    value_str = str(value).lower()
+    if ':' in value_str:
+        value_str = value_str.split(':')[1].strip()
+    
+    if 'female' in value_str or 'f' == value_str:
+        return 0
+    elif 'male' in value_str or 'm' == value_str:
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+save_cohort_info = 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 this step since trait_row is None (clinical data not available)
+
+# 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
+# Examine the gene identifiers from the previous step
+gene_identifiers_sample = ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056',
+                          'A_23_P100074', 'A_23_P100092', 'A_23_P100103', 'A_23_P100111',
+                          'A_23_P100127', 'A_23_P100133', 'A_23_P100141', 'A_23_P100156',
+                          'A_23_P100177', 'A_23_P100189', 'A_23_P100196', 'A_23_P100203',
+                          'A_23_P100220', 'A_23_P100240', 'A_23_P10025', 'A_23_P100263']
+
+print("Sample gene identifiers:")
+for i, identifier in enumerate(gene_identifiers_sample[:5]):
+    print(f"  {identifier}")
+
+# Analysis: These identifiers follow the pattern "A_23_P" + numbers
+# This is the standard format for Agilent microarray probe IDs
+# The "A_23_P" prefix indicates Agilent platform probe identifiers
+# These are not human gene symbols (which would be like BRCA1, TP53, etc.)
+# Therefore, they need to be mapped to gene symbols for meaningful analysis
+
+print("\nAnalysis: These are Agilent microarray probe IDs (A_23_P prefix)")
+print("They are not human gene symbols and require mapping to gene symbols.")
+
+requires_gene_mapping = True
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1. Identify the mapping columns
+# Gene identifiers in expression data match 'ID' column in annotation
+# Gene symbols are in 'GENE_SYMBOL' column
+prob_col = 'ID'
+gene_col = 'GENE_SYMBOL'
+
+# 2. Get gene mapping dataframe 
+gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+print(f"Gene expression data shape after mapping: {gene_data.shape}")
+print(f"First 5 gene symbols: {list(gene_data.index[:5])}")
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Since no clinical data is available (trait_row was None), create empty clinical dataframe
+empty_clinical_data = pd.DataFrame()
+linked_data = geo_link_clinical_genetic_data(empty_clinical_data, normalized_gene_data)
+
+# 5. Conduct quality check and save the cohort information
+os.makedirs(os.path.dirname(json_path), exist_ok=True)
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort, 
+    info_path=json_path, 
+    is_gene_available=True, 
+    is_trait_available=False, 
+    is_biased=False,  # Placeholder value since no trait data available
+    df=linked_data, 
+    note="INFO: Dataset contains gene expression data but no trait information available for analysis"
+)
+
+# 6. Since no trait data is available, the dataset is not usable - do not save linked data
+print("Dataset not saved - no trait information available for associational study")

output/preprocess/Endometrioid_Cancer/code/GSE73637.py

@@ -0,0 +1,203 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE73637"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73637"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE73637.csv"
+out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE73637.csv"
+out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE73637.csv"
+json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True
+
+# 2.1 Data Availability
+trait_row = 3  # histopathology data contains endometrioid cancer information
+age_row = None  # Not available for cell lines
+gender_row = None  # Not available for cell lines
+
+# 2.2 Data Type Conversion functions
+def convert_trait(value):
+    """Convert histopathology to binary endometrioid cancer status"""
+    if value is None:
+        return None
+    # Extract value after colon
+    if ':' in str(value):
+        histology = str(value).split(':')[1].strip()
+    else:
+        histology = str(value).strip()
+    
+    # Check if it contains "Endometrioid" (including "Endometroid" variant)
+    if "Endometrioid" in histology or "Endometroid" in histology:
+        return 1
+    else:
+        return 0
+
+def convert_age(value):
+    """Age conversion function (not used since age_row is None)"""
+    return None
+
+def convert_gender(value):
+    """Gender conversion function (not used since gender_row is None)"""
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, 
+                             is_gene_available=is_gene_available, is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_data = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
+                                                        age_row, convert_age, gender_row, convert_gender)
+    print("Clinical data extracted:")
+    print(preview_df(selected_clinical_data))
+    
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_data.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
+# Examine the gene identifiers from the previous step output
+gene_identifiers = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20']
+
+print("Sample gene identifiers:", gene_identifiers[:10])
+print("These appear to be numeric probe/array identifiers, not human gene symbols")
+print("Human gene symbols are typically alphanumeric like BRCA1, TP53, GAPDH, etc.")
+print("These numeric identifiers will need to be mapped to actual gene symbols")
+
+requires_gene_mapping = True
+
+# Step 5: Gene Annotation
+# First, let's examine the SOFT file structure to understand what we're dealing with
+print("Examining SOFT file structure:")
+with gzip.open(soft_file, 'rt') as f:
+    lines = []
+    for i, line in enumerate(f):
+        lines.append(line.strip())
+        if i >= 50:  # Read first 50 lines
+            break
+
+# Show first 20 lines to understand the structure
+for i, line in enumerate(lines[:20]):
+    print(f"Line {i}: {line[:100]}...")  # Show first 100 chars of each line
+
+print("\n" + "="*50)
+
+# Look for gene annotation section - typically starts after platform info
+annotation_start = None
+for i, line in enumerate(lines):
+    if line.startswith('!platform_table_begin'):
+        annotation_start = i + 1
+        print(f"Found annotation section starting at line {annotation_start}")
+        break
+
+if annotation_start:
+    print("Sample annotation lines:")
+    for i in range(annotation_start, min(annotation_start + 10, len(lines))):
+        if i < len(lines):
+            print(f"Line {i}: {lines[i]}")
+
+# Try alternative approach to get gene annotation
+try:
+    with gzip.open(soft_file, 'rt') as f:
+        content = f.read()
+    
+    # Find the platform table section
+    if '!platform_table_begin' in content and '!platform_table_end' in content:
+        start_marker = '!platform_table_begin'
+        end_marker = '!platform_table_end'
+        start_idx = content.find(start_marker) + len(start_marker)
+        end_idx = content.find(end_marker)
+        
+        table_content = content[start_idx:end_idx].strip()
+        
+        # Parse as CSV
+        gene_annotation = pd.read_csv(io.StringIO(table_content), delimiter='\t', low_memory=False)
+        
+        print("\nSuccessfully extracted gene annotation data!")
+        print("Gene annotation preview:")
+        print(preview_df(gene_annotation))
+        
+    else:
+        print("Could not find platform table markers in SOFT file")
+        
+except Exception as e:
+    print(f"Alternative parsing also failed: {e}")
+    # If all parsing fails, we may need to work without gene annotation
+    gene_annotation = None
+
+# Step 6: Gene Identifier Mapping
+# 1. Identify the columns for gene identifiers and gene symbols
+# The 'ID' column matches the gene expression data identifiers (numeric: 1, 2, 3, ...)
+# The 'GeneSymbol' column contains the actual gene symbols (PRPF8, CAPNS1, etc.)
+
+# 2. Get gene mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'GeneSymbol')
+
+# 3. Apply gene mapping to convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+print(f"Gene expression data shape after mapping: {gene_data.shape}")
+print(f"Sample gene symbols: {list(gene_data.index[:10])}")
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, 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.
+is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)
+
+# 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)
+    print(f"Successfully saved processed data to {out_data_file}")
+else:
+    print("Dataset is not usable for analysis - not saved")

output/preprocess/Endometrioid_Cancer/code/GSE94523.py

@@ -0,0 +1,172 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE94523"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE94523"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE94523.csv"
+out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE94523.csv"
+out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE94523.csv"
+json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Series mentions "Microarray Expression" and "Gene expression profiling"
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+trait_row = None  # Only one unique value 'tissue: endometrioid adenocarcinoma', which is constant
+age_row = None    # No age information available in sample characteristics
+gender_row = None # No gender information available in sample characteristics
+
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    """Convert trait values to binary (0/1)"""
+    if value is None:
+        return None
+    value = str(value).split(':')[-1].strip().lower()
+    if 'endometrioid' in value or 'adenocarcinoma' in value:
+        return 1
+    else:
+        return 0
+
+def convert_age(value):
+    """Convert age to continuous numeric values"""
+    if value is None:
+        return None
+    try:
+        value = str(value).split(':')[-1].strip()
+        return float(value)
+    except:
+        return None
+
+def convert_gender(value):
+    """Convert gender to binary (0=female, 1=male)"""
+    if value is None:
+        return None
+    value = str(value).split(':')[-1].strip().lower()
+    if 'female' in value or 'f' in value:
+        return 0
+    elif 'male' in value or 'm' in value:
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+save_cohort_info = 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
+# Skipping this step since trait_row is None (no clinical data available)
+
+# 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
+# Examine the gene identifiers from the previous step output
+gene_identifiers = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20']
+
+# Check if these look like human gene symbols
+# Gene symbols are typically alphanumeric strings with letters, not just numbers
+# Examples: TP53, BRCA1, EGFR, MYC, etc.
+print("Sample gene identifiers:", gene_identifiers[:10])
+
+# These are clearly numeric identifiers (as strings), not gene symbols
+# Gene symbols would contain letters and be more descriptive
+# These appear to be probe IDs or platform-specific identifiers
+
+requires_gene_mapping = True
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1. Identify mapping columns: 'ID' matches gene expression identifiers, 'HUGO' contains gene symbols
+prob_col = 'ID'
+gene_col = 'HUGO'
+
+# 2. Get gene mapping dataframe using the library function
+gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+print(f"Gene expression data shape after mapping: {gene_data.shape}")
+print(f"First few gene symbols: {gene_data.index[:10].tolist()}")
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Since no clinical data is available (trait_row = None from Step 2), create empty clinical dataframe
+# and link only genetic data
+clinical_data_empty = pd.DataFrame()
+linked_data = normalized_gene_data.T  # Transpose to have samples as rows
+
+# 3. Since no trait data is available, skip missing value handling for clinical features
+# Only handle missing values in genetic data
+linked_data = linked_data.fillna(linked_data.mean())
+
+# 4. Since no trait data exists, the dataset is biased/unusable for associative studies
+# All samples have the same constant trait value (endometrioid adenocarcinoma)
+is_trait_biased = True
+unbiased_linked_data = linked_data
+
+# 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=True, 
+    is_trait_available=False,  # No usable trait data available
+    is_biased=is_trait_biased, 
+    df=unbiased_linked_data,
+    note="INFO: Dataset contains only constant trait values (all endometrioid adenocarcinoma), not suitable for associative studies"
+)
+
+# 6. Since dataset is not usable, do not save the linked data file
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Endometrioid_Cancer/code/GSE94524.py

@@ -0,0 +1,148 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE94524"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE94524"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE94524.csv"
+out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE94524.csv"
+out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE94524.csv"
+json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# 1. Gene Expression Data Availability
+is_gene_available = True  # The study focuses on differential enhancer activity, suggesting gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+# trait: Only one unique value 'tissue: endometrioid adenocarcinoma' - constant feature, not useful
+trait_row = None  
+age_row = None    # No age information available
+gender_row = None # No gender information available
+
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    """Convert trait values to binary"""
+    if value is None:
+        return None
+    return None  # Not used since trait_row is None
+
+def convert_age(value):
+    """Convert age values to continuous"""
+    if value is None:
+        return None
+    return None  # Not used since age_row is None
+
+def convert_gender(value):
+    """Convert gender values to binary (0=female, 1=male)"""
+    if value is None:
+        return None
+    return None  # Not used since gender_row is None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, 
+                               is_gene_available=is_gene_available, 
+                               is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+# Skip this step since trait_row is None (no clinical data available)
+
+# 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("Examining gene identifiers...")
+print("Sample identifiers:", gene_data.index[:10].tolist())
+
+# These are numeric identifiers (1, 2, 3, etc.), not human gene symbols
+# Human gene symbols are typically alphanumeric strings like BRCA1, TP53, GAPDH, etc.
+# These numeric IDs likely represent probe IDs or other database identifiers that need mapping
+
+requires_gene_mapping = True
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1. Identify the mapping columns
+# 'ID' column matches the gene expression data identifiers (numeric IDs)
+# 'HUGO' column contains gene symbols, though it has some NaN values
+prob_col = 'ID'
+gene_col = 'HUGO'
+
+# 2. Get gene mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+# Normalize gene symbols to ensure consistency
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+print(f"Gene expression data shape after mapping: {gene_data.shape}")
+print(f"Sample gene names: {gene_data.index[:10].tolist()}")
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save it
+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)
+
+# Since no clinical data with variable traits is available (trait_row was None in Step 2),
+# this dataset is not suitable for association studies
+print("No variable clinical trait data available - dataset not suitable for association studies")
+
+# Create empty dataframe to represent unavailable linked data
+linked_data = pd.DataFrame()
+
+# 5. Conduct final quality validation
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort, 
+    info_path=json_path, 
+    is_gene_available=True, 
+    is_trait_available=False,
+    is_biased=True,  # Dataset is biased/unusable due to constant trait values
+    df=linked_data,
+    note="INFO: Dataset contains only constant trait values (all endometrioid adenocarcinoma), no variable clinical features for association analysis"
+)
+
+# 6. Since the dataset is not usable for association studies, do not save linked data file
+print(f"Dataset usability: {is_usable}")

output/preprocess/Endometrioid_Cancer/code/TCGA.py

@@ -0,0 +1,149 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+# Select the most relevant subdirectory for Endometrioid Cancer
+selected_cohort = "TCGA_Endometrioid_Cancer_(UCEC)"
+cohort_path = os.path.join(tcga_root_dir, selected_cohort)
+
+print(f"Selected cohort: {selected_cohort}")
+
+# Get file paths for clinical and genetic data
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_path)
+
+print(f"Clinical data file: {clinical_file_path}")
+print(f"Genetic data file: {genetic_file_path}")
+
+# Load clinical data
+clinical_data = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+
+# Load genetic data  
+genetic_data = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+print(f"\nClinical data shape: {clinical_data.shape}")
+print(f"Genetic data shape: {genetic_data.shape}")
+
+print(f"\nClinical data column names:")
+print(clinical_data.columns.tolist())
+
+# Step 2: Find Candidate Demographic Features
+# Identify candidate demographic columns
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
+candidate_gender_cols = ['gender']
+
+# Load clinical data to extract and preview candidate columns
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, "TCGA_Endometrioid_Cancer_(UCEC)"))
+clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)
+
+# Extract age candidate columns
+if candidate_age_cols:
+    age_data = clinical_df[candidate_age_cols]
+    print("Age candidate columns preview:")
+    print(preview_df(age_data, n=5))
+    print()
+
+# Extract gender candidate columns
+if candidate_gender_cols:
+    gender_data = clinical_df[candidate_gender_cols]
+    print("Gender candidate columns preview:")
+    print(preview_df(gender_data, n=5))
+
+# Step 3: Select Demographic Features
+# Based on the previous step output, select the best columns
+# Age candidate columns had: age_at_initial_pathologic_diagnosis (direct age values) and days_to_birth (negative values, some missing)
+# Gender candidate columns had: gender (clear FEMALE/MALE values)
+
+# Choose age column - age_at_initial_pathologic_diagnosis has direct age values with no missing data
+age_col = 'age_at_initial_pathologic_diagnosis'
+
+# Choose gender column - gender has clear gender values
+gender_col = 'gender'
+
+print(f"Chosen age column: {age_col}")
+print(f"Chosen gender column: {gender_col}")
+
+# Step 4: Feature Engineering and Validation
+# Extract and standardize clinical features
+clinical_features = tcga_select_clinical_features(
+    clinical_data, 
+    trait=trait,
+    age_col=age_col, 
+    gender_col=gender_col
+)
+
+print(f"Clinical features shape: {clinical_features.shape}")
+print(f"Clinical features columns: {clinical_features.columns.tolist()}")
+
+# Normalize gene symbols in genetic data
+normalized_genetic_data = normalize_gene_symbols_in_index(genetic_data)
+print(f"Normalized genetic data shape: {normalized_genetic_data.shape}")
+
+# Save normalized genetic data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_genetic_data.to_csv(out_gene_data_file)
+print(f"Saved normalized genetic data to {out_gene_data_file}")
+
+# Link clinical and genetic data - transpose genetic data first to have samples as rows
+genetic_data_transposed = normalized_genetic_data.T
+print(f"Transposed genetic data shape: {genetic_data_transposed.shape}")
+
+# Align by common sample IDs and concatenate
+common_samples = clinical_features.index.intersection(genetic_data_transposed.index)
+print(f"Common samples between clinical and genetic data: {len(common_samples)}")
+
+clinical_aligned = clinical_features.loc[common_samples]
+genetic_aligned = genetic_data_transposed.loc[common_samples]
+
+linked_data = pd.concat([clinical_aligned, genetic_aligned], axis=1)
+print(f"Linked data shape: {linked_data.shape}")
+
+# Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+print(f"Data shape after handling missing values: {linked_data.shape}")
+
+# Check if features are severely biased and remove biased demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+print(f"Final data shape: {linked_data.shape}")
+print(f"Final columns: {linked_data.columns.tolist()[:10]}...")  # Show first 10 columns
+
+# Validate data quality and determine if dataset is usable
+is_gene_available = len([col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]) > 0
+is_trait_available = trait in linked_data.columns and not linked_data[trait].isna().all()
+
+# Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="INFO: TCGA Endometrioid Cancer cohort processed successfully"
+)
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_features.to_csv(out_clinical_data_file)
+print(f"Saved clinical data to {out_clinical_data_file}")
+
+# Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+    print(f"Dataset is usable. Saved linked data to {out_data_file}")
+else:
+    print("Dataset is not usable. Linked data was not saved.")

output/preprocess/Endometrioid_Cancer/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE94524": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 111
-  },
-  "GSE94523": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 111
-  },
-  "GSE73637": {
-    "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
-  },
-  "GSE73614": {
-    "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
-  },
-  "GSE73551": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 50
-  },
-  "GSE68600": {
-    "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": 113
-  },
-  "GSE66667": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 36
-  },
-  "GSE65986": {
-    "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": 55
-  },
-  "GSE40785": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 37
-  },
-  "GSE120490": {
-    "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": 145
-  },
-  "TCGA": {
-    "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": 201
-  }
-}
+{"GSE94524": {"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": "INFO: Dataset contains only constant trait values (all endometrioid adenocarcinoma), no variable clinical features for association analysis"}, "GSE94523": {"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: Dataset contains only constant trait values (all endometrioid adenocarcinoma), not suitable for associative studies"}, "GSE73637": {"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, "note": ""}, "GSE73614": {"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: Dataset contains gene expression data but no trait information available for analysis"}, "GSE73551": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 50, "note": ""}, "GSE68600": {"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": 113, "note": ""}, "GSE66667": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 36, "note": ""}, "GSE65986": {"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": 55, "note": ""}, "GSE40785": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 37, "note": ""}, "GSE120490": {"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": 145, "note": ""}, "TCGA": {"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": 201, "note": "INFO: TCGA Endometrioid Cancer cohort processed successfully"}}

output/preprocess/Endometriosis/clinical_data/GSE120103.csv

@@ -1,2 +1,2 @@
-0,1,2,3
-0.0,1.0,0.0,1.0
+,GSM3393491,GSM3393492,GSM3393493,GSM3393494,GSM3393495,GSM3393496,GSM3393497,GSM3393498,GSM3393499,GSM3393500,GSM3393501,GSM3393502,GSM3393503,GSM3393504,GSM3393505,GSM3393506,GSM3393507,GSM3393508,GSM3393509,GSM3393510,GSM3393511,GSM3393512,GSM3393513,GSM3393514,GSM3393515,GSM3393516,GSM3393517,GSM3393518,GSM3393519,GSM3393520,GSM3393521,GSM3393522,GSM3393523,GSM3393524,GSM3393525,GSM3393526
+Endometriosis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Endometriosis/clinical_data/GSE145701.csv

@@ -1,2 +1,2 @@
-,0,1,2,3,4,5,6,7,8,9,10,11
-Endometriosis,0.0,1.0,1.0,,,,,,,,,
+,GSM4331199,GSM4331200,GSM4331201,GSM4331202,GSM4331203,GSM4331204,GSM4331205,GSM4331206,GSM4331207,GSM4331208,GSM4331209,GSM4331210,GSM4331211,GSM4331212,GSM4331213,GSM4331214,GSM4331215,GSM4331216,GSM4331217,GSM4331218,GSM4331219,GSM4331220,GSM4331221,GSM4331222,GSM4331223,GSM4331224,GSM4331225,GSM4331226,GSM4331227,GSM4331228,GSM4331229,GSM4331230,GSM4331231,GSM4331232,GSM4331233,GSM4331234,GSM4331235,GSM4331236,GSM4331237,GSM4331238,GSM4331239,GSM4331240,GSM4331241,GSM4331242,GSM4331243,GSM4331244,GSM4331245,GSM4331246
+Endometriosis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Endometriosis/clinical_data/GSE73622.csv

@@ -1,3 +1,3 @@
-,Sample_1,Sample_2
-Endometriosis,1.0,1.0
-Age,29.0,39.0
+GSM1899589,GSM1899590,GSM1899591,GSM1899592,GSM1899593,GSM1899594,GSM1899595,GSM1899596,GSM1899597,GSM1899598,GSM1899599,GSM1899600,GSM1899601,GSM1899602,GSM1899603,GSM1899604,GSM1899605,GSM1899606,GSM1899607,GSM1899608,GSM1899609,GSM1899610,GSM1899611,GSM1899612,GSM1899613,GSM1899614,GSM1899615,GSM1899616,GSM1899617,GSM1899618,GSM1899619,GSM1899620,GSM1899621,GSM1899622,GSM1899623,GSM1899624,GSM1899625,GSM1899626,GSM1899627,GSM1899628,GSM1899629,GSM1899630,GSM1899631,GSM1899632,GSM1899633,GSM1899634,GSM1899635,GSM1899636,GSM1899637,GSM1899638
+1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0
+29.0,39.0,47.0,35.0,50.0,27.0,21.0,31.0,26.0,29.0,29.0,36.0,31.0,47.0,35.0,24.0,28.0,28.0,41.0,29.0,31.0,36.0,47.0,24.0,28.0,27.0,21.0,29.0,31.0,36.0,28.0,27.0,28.0,21.0,29.0,31.0,36.0,47.0,24.0,28.0,28.0,21.0,29.0,31.0,36.0,47.0,24.0,28.0,28.0,21.0

output/preprocess/Endometriosis/code/GSE111974.py

@@ -0,0 +1,127 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE111974"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE111974"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Endometriosis/GSE111974.csv"
+out_gene_data_file = "./output/z3/preprocess/Endometriosis/gene_data/GSE111974.csv"
+out_clinical_data_file = "./output/z3/preprocess/Endometriosis/clinical_data/GSE111974.csv"
+json_path = "./output/z3/preprocess/Endometriosis/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 and trait availability based on provided background and characteristics
+is_gene_available = True  # RNA expression profiling implies gene expression data is available
+trait_row = None          # No Endometriosis status available; dataset focuses on RIF vs fertile controls
+age_row = None            # No age information in sample characteristics
+gender_row = None         # No gender information in sample characteristics
+
+is_trait_available = trait_row is not None
+
+# Conversion functions
+def _parse_after_colon(x):
+    if x is None:
+        return None
+    try:
+        s = str(x)
+    except Exception:
+        return None
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) == 2 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    # Binary: Endometriosis case = 1, control = 0
+    val = _parse_after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    # Positive indications
+    positives = {"endometriosis", "endo", "case"}
+    if any(p in v for p in positives):
+        # If the phrase explicitly negates endometriosis, handle below
+        if "no endometriosis" in v or "without endometriosis" in v:
+            return 0
+        return 1
+    # Negative/control indications
+    negatives = {"control", "healthy", "no endometriosis", "without endometriosis"}
+    if any(n in v for n in negatives):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous: extract first number as age in years
+    val = _parse_after_colon(x)
+    if val is None:
+        return None
+    import re
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    if not m:
+        return None
+    try:
+        return float(m.group())
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female=0, male=1
+    val = _parse_after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# Save initial metadata
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction is skipped because trait_row is None
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Endometriosis/code/GSE120103.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE120103"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE120103"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Endometriosis/GSE120103.csv"
+out_gene_data_file = "./output/z3/preprocess/Endometriosis/gene_data/GSE120103.csv"
+out_clinical_data_file = "./output/z3/preprocess/Endometriosis/clinical_data/GSE120103.csv"
+json_path = "./output/z3/preprocess/Endometriosis/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  # Whole genome expression arrays (mRNA) per background info
+
+# 2) Variable availability and converters
+trait_row = 1   # 'sample group' encodes Endometriosis vs Control
+age_row = None  # No age information found
+gender_row = None  # Gender present but constant (all Female), thus not useful
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if not v:
+        return None
+    vl = v.lower()
+    # Cases (Endometriosis)
+    if 'endometriosis' in vl:
+        return 1
+    # Controls (No Endometriosis)
+    if 'disease free' in vl or 'disease-free' in vl or 'without endometriosis' in vl or 'control' in vl:
+        return 0
+    # Fallback by group labels if present
+    if 'group 2' in vl:
+        return 1
+    if 'group 1' in vl:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if not v:
+        return None
+    # Extract a number possibly with decimal (e.g., '35', '35.0', '35 years')
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if not v:
+        return None
+    vl = v.lower()
+    if vl in {'f', 'female', 'woman', 'women'}:
+        return 0
+    if vl in {'m', 'male', 'man', 'men'}:
+        return 1
+    return None
+
+# 3) Initial filtering metadata save
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+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 the identifier and gene symbol columns based on previews:
+#    - Probe/ID column: 'ID' matches expression data identifiers like 'A_23_P100001'
+#    - Gene symbol column: 'GENE_SYMBOL'
+
+# 2. Build the 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
+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 if any
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = ("INFO: Gender was constant (all female) and excluded; Age not available in annotations. "
+        "Probe-to-gene mapping used GENE_SYMBOL; gene symbols normalized with 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 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/Endometriosis/code/GSE138297.py

@@ -0,0 +1,155 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE138297"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE138297"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Endometriosis/GSE138297.csv"
+out_gene_data_file = "./output/z3/preprocess/Endometriosis/gene_data/GSE138297.csv"
+out_clinical_data_file = "./output/z3/preprocess/Endometriosis/clinical_data/GSE138297.csv"
+json_path = "./output/z3/preprocess/Endometriosis/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 (microarray on biopsies -> gene expression)
+is_gene_available = True
+
+# 2) Variable availability from the sample characteristics:
+# Trait (Endometriosis) is not present; dataset is IBS FMT trial -> not available for current trait
+trait_row = None
+# Age and Gender rows identified from the dictionary
+age_row = 3
+gender_row = 1
+
+# 2.2) Converters
+def _after_colon(x):
+    s = str(x)
+    return s.split(":", 1)[1].strip() if ":" in s else s.strip()
+
+def convert_trait(x):
+    # Binary: 1 = Endometriosis present (case), 0 = control
+    v = _after_colon(x).lower()
+    if v in {"1", "yes", "case", "patient", "disease"}:
+        return 1
+    if v in {"0", "no", "control", "healthy", "normal"}:
+        return 0
+    # keyword-based heuristic
+    if "endometriosis" in v or re.search(r"\bendo\b", v):
+        return 1
+    if "non-endometriosis" in v or "nonendo" in v or "non endo" in v:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    m = re.search(r"-?\d+\.?\d*", v)
+    if m:
+        try:
+            val = float(m.group())
+            return int(val) if val.is_integer() else val
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    v = _after_colon(x).lower()
+    # Dataset encodes female=1, male=0; invert to our standard
+    if v in {"0", "1"}:
+        try:
+            num = int(v)
+            return 1 if num == 0 else 0  # male(0)->1, female(1)->0
+        except Exception:
+            pass
+    if "male" in v:
+        return 1
+    if "female" 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 (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# 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 gene identifiers (numeric probe-like IDs), 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 probe IDs and gene symbols from the annotation dataframe
+probe_id_col = 'ID' if 'ID' in gene_annotation.columns else ('probeset_id' if 'probeset_id' in gene_annotation.columns else None)
+gene_symbol_col = 'gene_assignment' if 'gene_assignment' in gene_annotation.columns else ('mrna_assignment' if 'mrna_assignment' in gene_annotation.columns else None)
+
+if probe_id_col is None or gene_symbol_col is None:
+    raise ValueError("Required columns for mapping not found in gene annotation.")
+
+# 2. Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

output/preprocess/Endometriosis/code/GSE145701.py

@@ -0,0 +1,195 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE145701"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE145701"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Endometriosis/GSE145701.csv"
+out_gene_data_file = "./output/z3/preprocess/Endometriosis/gene_data/GSE145701.csv"
+out_clinical_data_file = "./output/z3/preprocess/Endometriosis/clinical_data/GSE145701.csv"
+json_path = "./output/z3/preprocess/Endometriosis/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
+from typing import Optional
+
+# 1. Gene Expression Data Availability
+# Affymetrix Human Gene 1.0 ST arrays indicate gene expression microarray data.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# Keys identified from the provided Sample Characteristics Dictionary:
+# 0: gender (but all Female -> constant -> not available)
+# 2: disease state (Normal, Endometriosis Stage I/IV) -> trait
+trait_row: Optional[int] = 2
+age_row: Optional[int] = None
+gender_row: Optional[int] = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    if not isinstance(x, str):
+        x = str(x)
+    # Split on ':' and take the part after the last colon to be robust
+    parts = x.split(':', 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    """
+    Binary: 0 = control/normal; 1 = endometriosis (any stage).
+    Maps disease state strings accordingly.
+    """
+    val = _extract_value(x)
+    if val is None or val == '':
+        return None
+    low = val.lower()
+    # Normal controls
+    if 'normal' in low or 'nup' in low:
+        return 0
+    # Endometriosis (any stage)
+    if 'endometriosis' in low:
+        return 1
+    return None
+
+def convert_age(x):
+    # Age not available in this dataset
+    return None
+
+def convert_gender(x):
+    """
+    Binary gender mapping: female->0, male->1.
+    Not used here since gender is constant (all female), but provided for completeness.
+    """
+    val = _extract_value(x)
+    if val is None or val == '':
+        return None
+    low = val.lower()
+    if low in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if low 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,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Clinical features preview:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    # Preserve index to keep feature names
+    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 observed identifiers (e.g., '7892501') are numeric probe IDs (e.g., Illumina), 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 the appropriate columns for probe IDs and gene symbols in the annotation
+id_col = 'ID' if 'ID' in gene_annotation.columns else gene_annotation.columns[0]
+gene_symbol_col_candidates = ['gene_assignment', 'mrna_assignment', 'GB_LIST']
+gene_col = next((c for c in gene_symbol_col_candidates if c in gene_annotation.columns), None)
+if gene_col is None:
+    raise ValueError("No suitable gene symbol column found in gene_annotation.")
+
+# Build the mapping dataframe (probe ID -> gene annotation text)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level data to gene-level data
+# Preserve original probe-level data, then overwrite gene_data with gene-level expression as required
+probe_level_df = gene_data
+gene_data = apply_gene_mapping(probe_level_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 (use the correct variable name from Step 2)
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Optional sanity check
+print("Normalized gene data shape:", normalized_gene_data.shape)
+print("Linked data shape before missing handling:", linked_data.shape)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and saving cohort info
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data
+)
+
+# 6. Conditionally save linked data
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Endometriosis/code/GSE145702.py

@@ -0,0 +1,197 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE145702"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE145702"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Endometriosis/GSE145702.csv"
+out_gene_data_file = "./output/z3/preprocess/Endometriosis/gene_data/GSE145702.csv"
+out_clinical_data_file = "./output/z3/preprocess/Endometriosis/clinical_data/GSE145702.csv"
+json_path = "./output/z3/preprocess/Endometriosis/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 availability
+is_gene_available = True  # Based on series context indicating gene transcription; not miRNA-only or methylation-only.
+
+# Step 2: Variable availability and conversion functions
+# Sample Characteristics Dictionary suggests:
+# - trait (Endometriosis) at row 2: ['disease state: Normal', 'disease state: Endometriosis Stage I', 'disease state: Endometriosis Stage IV']
+# - age: not available
+# - gender: only 'Female' => constant, treat as unavailable
+
+trait_row = 2
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None or v == '':
+        return None
+    vl = v.lower()
+    # Heuristic: any mention of endometriosis indicates case; normal/control indicates control
+    if 'normal' in vl or 'control' in vl:
+        return 0
+    if 'endometriosis' in vl or 'endo' in vl:
+        return 1
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None or v == '':
+        return None
+    # Extract first numeric value as age (e.g., "35 years")
+    try:
+        import re
+        m = re.search(r'(\d+(\.\d+)?)', v)
+        return float(m.group(1)) if m else None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None or v == '':
+        return None
+    vl = v.lower()
+    if vl.startswith('f'):
+        return 0
+    if vl.startswith('m'):
+        return 1
+    return None
+
+# Step 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
+)
+
+# Step 4: Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=None,
+        gender_row=gender_row,
+        convert_gender=None
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    import os
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Map probe IDs to gene symbols and convert probe-level data to gene-level data
+
+# 1. Decide columns: probe identifiers are in 'ID' and gene symbols info is in 'gene_assignment'
+probe_col = 'ID'
+gene_col = 'gene_assignment'
+
+# 2. Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3. Apply mapping to convert probe-level expression to gene-level expression
+probe_data = gene_data  # preserve original probe-level data
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# Ensure we have the clinical features in memory; otherwise load from the saved CSV
+try:
+    selected_clinical_df
+except NameError:
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 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 checking and removal of biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = ("INFO: Gender effectively constant female and Age unavailable in this cohort. "
+        "Samples are eutopic endometrial stromal fibroblasts with hormone treatments; "
+        "trait derived from 'disease state'.")
+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/Endometriosis/code/GSE165004.py

@@ -0,0 +1,148 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE165004"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE165004"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Endometriosis/GSE165004.csv"
+out_gene_data_file = "./output/z3/preprocess/Endometriosis/gene_data/GSE165004.csv"
+out_clinical_data_file = "./output/z3/preprocess/Endometriosis/clinical_data/GSE165004.csv"
+json_path = "./output/z3/preprocess/Endometriosis/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 (RNA expression study -> likely gene expression data)
+is_gene_available = True
+
+# 2) Variable availability and converters
+# Based on sample characteristics:
+# {0: ['subject status/group: Control', 'subject status/group: patient with RPL', 'subject status/group: patient with UIF'],
+#  1: ['tissue: Endometrial tissue']}
+# No endometriosis group; study explicitly excluded endometriosis -> trait would be constant negative. No age/gender fields listed.
+trait_row = None   # Constantly absent for Endometriosis in this cohort
+age_row = None     # Not provided
+gender_row = None  # Not provided (all participants are women; constant feature and not explicitly recorded)
+
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    # Binary: Endometriosis present (1) vs. absent (0)
+    val = _after_colon(x).lower()
+    if not val:
+        return None
+    # Positive if explicit endometriosis present
+    if "endometriosis" in val or "endometriotic" in val:
+        return 1
+    # Negative if clearly a non-endometriosis cohort label
+    if any(k in val for k in ["control", "rpl", "recurrent pregnancy loss", "uif", "ui", "unexplained infertility"]):
+        return 0
+    # If the string is clearly unrelated metadata (e.g., tissue), return None
+    if any(k in val for k in ["tissue", "endometrial tissue"]):
+        return None
+    return None
+
+def convert_age(x):
+    # Continuous: extract numeric age if present
+    val = _after_colon(x)
+    if not val:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    return float(m.group()) if m else None
+
+def convert_gender(x):
+    # Binary: female=0, male=1
+    val = _after_colon(x).lower()
+    if not val:
+        return None
+    if "female" in val or val == "f":
+        return 0
+    if "male" in val or val == "m":
+        return 1
+    return None
+
+# 3) Save metadata with initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+if trait_row is not None and 'clinical_data' in globals():
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = 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=False)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the appropriate columns for mapping: probe IDs and gene symbols
+probe_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+
+# 2. Create the mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply the mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

output/preprocess/Endometriosis/code/GSE37837.py

@@ -0,0 +1,169 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE37837"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE37837"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Endometriosis/GSE37837.csv"
+out_gene_data_file = "./output/z3/preprocess/Endometriosis/gene_data/GSE37837.csv"
+out_clinical_data_file = "./output/z3/preprocess/Endometriosis/clinical_data/GSE37837.csv"
+json_path = "./output/z3/preprocess/Endometriosis/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  # Agilent whole human genome oligo microarray => gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+
+# Trait: Endometriosis - all subjects are endometriosis patients; no controls => not available
+trait_row = None
+
+# Age: available at key 0
+age_row = 0
+
+# Gender: only "female (fertile)" => constant => not available
+gender_row = None
+
+def _after_colon(s: str) -> str:
+    if s is None:
+        return ""
+    parts = str(s).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(s).strip()
+
+def convert_trait(x):
+    v = _after_colon(x).lower()
+    if not v:
+        return None
+    # Generic heuristics (not used here since trait_row is None)
+    if any(k in v for k in ["control", "healthy", "normal", "no endometriosis"]):
+        return 0
+    if any(k in v for k in ["endometriosis", "endometrioma", "case", "patient"]):
+        return 1
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if not v:
+        return None
+    nums = re.findall(r"[-+]?\d*\.?\d+", v)
+    if not nums:
+        return None
+    try:
+        return float(nums[0])
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x).lower()
+    if not v:
+        return None
+    if "female" in v or v == "f":
+        return 0
+    if "male" in v or v == "m":
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    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. Decide identifier and gene symbol columns, and create mapping dataframe
+probe_col = 'ID'            # Matches probe identifiers like 'A_23_P100001' in gene_data index
+gene_symbol_col = 'GENE_SYMBOL'  # Contains human gene symbols
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Since trait data is unavailable for this cohort (Step 2), skip linking and downstream steps.
+is_trait_available = False
+note = "WARNING: Trait not available (all subjects are endometriosis patients; no control/trait labels)."
+
+# 5. Final metadata logging. Pass gene data (transposed) just to avoid abnormality override.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=is_trait_available,
+    is_biased=False,
+    df=normalized_gene_data.T,
+    note=note
+)
+
+# 6. Do not save linked data since trait is unavailable and dataset is not usable for association analysis.

output/preprocess/Endometriosis/code/GSE51981.py

@@ -0,0 +1,193 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE51981"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE51981"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Endometriosis/GSE51981.csv"
+out_gene_data_file = "./output/z3/preprocess/Endometriosis/gene_data/GSE51981.csv"
+out_clinical_data_file = "./output/z3/preprocess/Endometriosis/clinical_data/GSE51981.csv"
+json_path = "./output/z3/preprocess/Endometriosis/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 series design
+is_gene_available = True  # Whole genome microarrays indicate gene expression data
+
+# Step 2: Variable availability and conversion functions
+# From the Sample Characteristics Dictionary:
+# - Trait (Endometriosis status) is at key 1 with values "Endometriosis" vs "Non-Endometriosis"
+trait_row = 1
+
+# Age and Gender are not present; study population is women and likely constant/absent in metadata here.
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        return parts[1].strip() if len(parts) == 2 else x.strip()
+    return x
+
+def convert_trait(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    # Map common representations to binary
+    if s in {"endometriosis", "endo", "case", "yes", "y", "1"}:
+        return 1
+    if s in {"non-endometriosis", "no endometriosis", "control", "no", "n", "0"}:
+        return 0
+    return None
+
+def convert_age(x):
+    # Not used (age_row is None), but provided for completeness
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = str(val).lower()
+    # Extract first numeric token as age
+    import re
+    m = re.search(r"(\d+(\.\d+)?)", s)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    # Not used (gender_row is None), but provided for completeness
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if s in {"female", "f", "woman", "women"}:
+        return 0
+    if s in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# Step 3: Save initial metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print("Preview of selected clinical features:", preview)
+
+    # Save clinical features
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# 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 appropriate columns for probe IDs and gene symbols based on the preview:
+# Probe ID column: "ID"
+# Gene symbol column: "Gene Symbol"
+
+# 2. Get mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save
+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)
+
+# Ensure clinical dataframe is available (fallback to loading from saved CSV if needed)
+try:
+    selected_clinical_df  # noqa: F401
+except NameError:
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 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 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 = (
+    f"INFO: Probes mapped to gene symbols and normalized by NCBI synonyms. "
+    f"Age and Gender unavailable in clinical data. "
+    f"Linked samples: {len(unbiased_linked_data)}; "
+    f"Features (incl. trait): {len(unbiased_linked_data.columns)}."
+)
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, 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/Endometriosis/code/GSE73622.py

@@ -0,0 +1,244 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE73622"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE73622"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Endometriosis/GSE73622.csv"
+out_gene_data_file = "./output/z3/preprocess/Endometriosis/gene_data/GSE73622.csv"
+out_clinical_data_file = "./output/z3/preprocess/Endometriosis/clinical_data/GSE73622.csv"
+json_path = "./output/z3/preprocess/Endometriosis/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  # Transcriptome analysis indicates mRNA gene expression data
+
+# 2) Variable availability
+trait_row = 0   # disease: No Endometriosis / Endometriosis
+age_row = 3     # age: numbers
+gender_row = None  # Endometrial tissue from women; gender is constant (female) and not explicitly recorded -> not available
+
+# 2.2) Converters
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    # Positive cases
+    if vl in {"endometriosis", "case"} or ("endo" in vl and "no" not in vl):
+        return 1
+    # Controls/negatives
+    if vl in {"no endometriosis", "control"} or ("no" in vl and "endometriosis" in vl):
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    # Extract first integer/float in the string
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        num = float(m.group())
+        return num
+    except Exception:
+        return None
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    # Female -> 0, Male -> 1
+    if vl in {"female", "f", "woman", "women"}:
+        return 0
+    if vl 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,
+        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, index=False)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+import os
+import re
+import pandas as pd
+
+def is_symbol_like(s: str) -> bool:
+    # Heuristics: human gene symbols are typically alphanumeric with letters, may include '-' or '.',
+    # rarely include underscores, and are not purely numeric. Exclude common non-symbol prefixes.
+    s = s.strip()
+    if not s or s.isdigit():
+        return False
+    if "_" in s:
+        return False
+    if any(s.upper().startswith(p) for p in ["ENSG", "ENST", "NM_", "XM_", "NR_", "ILMN", "AFFX", "A_"]):
+        return False
+    return bool(re.match(r"^[A-Za-z][A-Za-z0-9\-.]{1,}$", s))
+
+requires_gene_mapping = True  # default to conservative choice
+
+if os.path.exists(out_gene_data_file):
+    try:
+        gdf = pd.read_csv(out_gene_data_file, index_col=0)
+        ids = gdf.index.astype(str).tolist()
+        sample_ids = ids[: min(200, len(ids))]
+        if sample_ids:
+            symbol_like_count = sum(is_symbol_like(x) for x in sample_ids)
+            digit_only_count = sum(x.isdigit() for x in sample_ids)
+            # Decide based on majority pattern
+            if symbol_like_count / len(sample_ids) >= 0.6:
+                requires_gene_mapping = False
+            elif digit_only_count / len(sample_ids) >= 0.6:
+                requires_gene_mapping = True
+            else:
+                # If mixed, require mapping to ensure consistency
+                requires_gene_mapping = True
+    except Exception:
+        # If reading fails, keep conservative default
+        requires_gene_mapping = True
+
+print(f"requires_gene_mapping = {str(requires_gene_mapping)}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide columns for mapping based on annotation preview:
+# - Probe/ID column: 'ID' (matches numeric probe IDs seen in gene_data index)
+# - Gene symbol information: 'gene_assignment' (contains gene symbols in the annotation text)
+
+gene_col = 'gene_assignment' if 'gene_assignment' in gene_annotation.columns else 'mrna_assignment'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', 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
+import pandas as pd
+
+# 1. Normalize the obtained gene data and save
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+pre_norm_gene_count = gene_data.shape[0] if isinstance(gene_data, pd.DataFrame) else 0
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+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)
+is_trait_biased = bool(is_trait_biased)
+
+# Flags for final validation (ensure native Python bools)
+is_gene_available_flag = bool(normalized_gene_data.shape[0] > 0)
+is_trait_available_flag = bool((trait in selected_clinical_df.index) and bool(selected_clinical_df.loc[trait].notna().any()))
+
+# Prepare note
+note = (
+    f"INFO: Probes mapped to genes. Genes before normalization: {int(pre_norm_gene_count)}, "
+    f"after normalization: {int(normalized_gene_data.shape[0])}. "
+    f"Samples before filtering: {int(selected_clinical_df.shape[1])}, "
+    f"after filtering: {int(unbiased_linked_data.shape[0])}. "
+    f"Gender not available (all female)."
+)
+
+# 5. Conduct quality check and save the cohort information
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_flag,
+    is_trait_available=is_trait_available_flag,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. If the linked data is usable, save it as a CSV file
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Endometriosis/code/GSE75427.py

@@ -0,0 +1,133 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE75427"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE75427"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Endometriosis/GSE75427.csv"
+out_gene_data_file = "./output/z3/preprocess/Endometriosis/gene_data/GSE75427.csv"
+out_clinical_data_file = "./output/z3/preprocess/Endometriosis/clinical_data/GSE75427.csv"
+json_path = "./output/z3/preprocess/Endometriosis/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 (from series title: "Expression profiles ...")
+is_gene_available = True
+
+# 2) Variable availability and converters inferred from the sample characteristics dictionary shown:
+# {0: ['cell type: proliferative phase normal endometrium'],
+#  1: ['gender: Female'],
+#  2: ['age: 37y', 'age: 47y', 'age: 53y', 'age: 41y'],
+#  3: ['treatment: 12d 10% charcoal-stripped heat-inactivated FBS', 'treatment: 12d dibutyryl-cAMP and dienogest']}
+
+# - Trait (Endometriosis): Only "cell type: proliferative phase normal endometrium" is present -> constant -> not available.
+trait_row = None
+
+# - Age: multiple values available under key 2.
+age_row = 2
+
+# - Gender: only 'Female' under key 1 -> constant -> not available.
+gender_row = None
+
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    # handle values that may not contain a colon
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(x).strip()
+
+# 2.2 Converters
+def convert_trait(x):
+    # Binary: case (Endometriosis) = 1, control = 0
+    v = _after_colon(x).lower()
+    if not v:
+        return None
+    # Heuristics for case vs control
+    case_markers = ["endometriosis", "endometriotic", "ecsc", "ectopic", "lesion", "ovarian endometrioma"]
+    control_markers = ["normal", "nesc", "control", "healthy", "non-endometriosis", "euploid"]
+    if any(m in v for m in case_markers):
+        return 1
+    if any(m in v for m in control_markers):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous: years
+    v = _after_colon(x)
+    if not v:
+        return None
+    # extract first number (int or float)
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if not m:
+        return None
+    try:
+        val = float(m.group(1))
+        return val
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    v = _after_colon(x).lower()
+    if not v:
+        return None
+    if v in ["f", "female"]:
+        return 0
+    if v 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 (skip if trait not available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Endometriosis/code/TCGA.py

@@ -0,0 +1,337 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Endometriosis/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Endometriosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Endometriosis/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Endometriosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# List available TCGA subdirectories
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Scoring-based selection for directories related to Endometriosis
+keywords = [
+    ("endometrioid", 5),
+    ("endometri", 4),
+    ("endometrial", 3),
+    ("uterine", 2),
+    ("ovarian", 2),
+    ("ovary", 2),
+]
+best_dir = None
+best_score = 0
+for d in subdirs:
+    name = d.lower()
+    score = sum(weight for kw, weight in keywords if kw in name)
+    if score > best_score:
+        best_score = score
+        best_dir = d
+
+if best_dir is None or best_score == 0:
+    # No suitable directory found; record and stop further processing
+    print("No suitable TCGA cohort directory found for the trait. Skipping.")
+    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: {best_dir}")
+    cohort_dir = os.path.join(tcga_root_dir, best_dir)
+
+    # Identify clinical and genetic files
+    clinical_fp, genetic_fp = tcga_get_relevant_filepaths(cohort_dir)
+    print(f"Clinical file: {clinical_fp}")
+    print(f"Genetic file: {genetic_fp}")
+
+    # Load data
+    clinical_df = pd.read_csv(clinical_fp, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_fp, sep='\t', index_col=0, low_memory=False)
+
+    # Print clinical column names for further analysis
+    print(list(clinical_df.columns))
+
+# Step 2: Find Candidate Demographic Features
+import os
+import re
+import pandas as pd
+
+# Locate clinical file
+cohort_dir = os.path.join(tcga_root_dir, "TCGA_Endometrioid_Cancer_(UCEC)")
+clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load clinical DataFrame
+try:
+    clinical_df = pd.read_csv(clinical_file_path, sep="\t", index_col=0, dtype=str)
+except Exception:
+    clinical_df = pd.read_csv(clinical_file_path, sep=",", index_col=0, dtype=str)
+
+def token_match(s: str, token: str) -> bool:
+    # Match token as a standalone word or underscore-separated token
+    return re.search(rf'(?<![A-Za-z0-9]){re.escape(token)}(?![A-Za-z0-9])', s) is not None
+
+candidate_age_cols = []
+candidate_gender_cols = []
+
+for c in clinical_df.columns:
+    cl = c.lower()
+    # Age candidates: 'age' as a token, plus specific whitelist like 'days_to_birth'
+    if token_match(cl, 'age') or cl == 'days_to_birth':
+        candidate_age_cols.append(c)
+    # Gender candidates: 'gender' or 'sex' as tokens
+    if token_match(cl, 'gender') or token_match(cl, 'sex'):
+        candidate_gender_cols.append(c)
+
+# Print candidates in the required strict format
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Preview extracted data (first 5 values) as dictionaries
+if candidate_age_cols:
+    age_preview = preview_df(clinical_df[candidate_age_cols], n=5)
+    print(age_preview)
+
+if candidate_gender_cols:
+    gender_preview = preview_df(clinical_df[candidate_gender_cols], n=5)
+    print(gender_preview)
+
+# Step 3: Select Demographic Features
+# Select the most suitable demographic columns based on preview dictionaries (if available) and simple heuristics.
+
+# Try to retrieve preview dictionaries created in previous steps
+def _get_preview_dict(possible_names):
+    for name in possible_names:
+        if name in globals() and isinstance(globals()[name], dict):
+            return globals()[name]
+    return None
+
+age_preview_dict = _get_preview_dict([
+    'age_preview_dict', 'age_values_dict', 'age_candidate_dict', 'age_dict', 'candidate_age_dict'
+])
+gender_preview_dict = _get_preview_dict([
+    'gender_preview_dict', 'gender_values_dict', 'gender_candidate_dict', 'gender_dict', 'candidate_gender_dict'
+])
+
+def _to_list(v):
+    if v is None:
+        return []
+    if isinstance(v, list):
+        return v
+    try:
+        return list(v)
+    except Exception:
+        return [v]
+
+import math
+import re
+
+def _extract_number(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    if s.lower() in ("nan", "na", "none", ""):
+        return None
+    m = re.search(r'-?\d+\.?\d*', s)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def _evaluate_age_column(col, preview_vals):
+    # Returns (valid_age_count, non_missing_count, total_count, preference_bonus)
+    vals = _to_list(preview_vals)
+    total = len(vals)
+    non_missing = 0
+    valid = 0
+    for v in vals:
+        num = _extract_number(v)
+        if num is None:
+            continue
+        non_missing += 1
+        # If this looks like days_to_birth (often negative), convert to years
+        if ('day' in col.lower()) or ('birth' in col.lower()):
+            age_years = abs(num) / 365.25
+        else:
+            age_years = num
+        if 0 < age_years < 120:
+            valid += 1
+    # Preference bonus for clearer column names
+    bonus = 0.0
+    col_lower = col.lower()
+    if 'age' in col_lower:
+        bonus += 1.0
+    if 'days' in col_lower or 'birth' in col_lower:
+        bonus += 0.2
+    return valid, non_missing, total, bonus
+
+def _evaluate_gender_column(col, preview_vals):
+    # Returns (valid_gender_count, non_missing_count, total_count)
+    vals = _to_list(preview_vals)
+    total = len(vals)
+    non_missing = 0
+    valid = 0
+    for v in vals:
+        if v is None:
+            continue
+        s = str(v).strip().lower()
+        if s in ("nan", "na", "none", ""):
+            continue
+        non_missing += 1
+        if s in ("male", "female", "m", "f", "0", "1"):
+            valid += 1
+    return valid, non_missing, total
+
+# Attempt to fetch previews from dicts; if unavailable and clinical_df exists, create previews from clinical_df
+def _get_preview_for_col(col, preview_dict):
+    if preview_dict and isinstance(preview_dict.get(col, None), list):
+        return preview_dict[col]
+    # Fallback: sample from clinical_df if available
+    if 'clinical_df' in globals():
+        try:
+            return clinical_df[col].head(5).tolist()
+        except Exception:
+            return []
+    return []
+
+# Initialize selections
+age_col = None
+gender_col = None
+
+# Select age column
+age_candidates = candidate_age_cols if 'candidate_age_cols' in globals() else []
+best_age_score = (-1, -1, -1, -1.0)  # tuple to compare: (valid, non_missing, total, bonus)
+best_age_col = None
+
+if isinstance(age_candidates, list) and len(age_candidates) > 0:
+    for col in age_candidates:
+        preview = _get_preview_for_col(col, age_preview_dict)
+        valid, non_missing, total, bonus = _evaluate_age_column(col, preview)
+        score = (valid, non_missing, total, bonus)
+        if score > best_age_score:
+            best_age_score = score
+            best_age_col = col
+
+    # Decide if best candidate is acceptable
+    valid, non_missing, total, bonus = best_age_score
+    if total > 0 and valid >= max(1, min(3, non_missing)):  # require at least some valid values in preview
+        age_col = best_age_col
+    else:
+        age_col = None
+else:
+    age_col = None
+
+# Select gender column
+gender_candidates = candidate_gender_cols if 'candidate_gender_cols' in globals() else []
+best_gender_score = (-1, -1, -1)  # (valid, non_missing, total)
+best_gender_col = None
+
+if isinstance(gender_candidates, list) and len(gender_candidates) > 0:
+    for col in gender_candidates:
+        preview = _get_preview_for_col(col, gender_preview_dict)
+        valid, non_missing, total = _evaluate_gender_column(col, preview)
+        score = (valid, non_missing, total)
+        if score > best_gender_score:
+            best_gender_score = score
+            best_gender_col = col
+
+    valid_g, non_missing_g, total_g = best_gender_score
+    if total_g > 0 and valid_g >= max(1, min(3, non_missing_g)):
+        gender_col = best_gender_col
+    else:
+        gender_col = None
+else:
+    gender_col = None
+
+# Explicitly print chosen columns and preview info
+def _print_preview_info(col, preview_dict):
+    print(col)
+    if col is None:
+        print("None")
+        return
+    preview_vals = _get_preview_for_col(col, preview_dict)
+    if preview_vals:
+        print(preview_vals)
+    else:
+        print("Preview unavailable")
+
+print("Selected age_col and preview:")
+_print_preview_info(age_col, age_preview_dict)
+
+print("Selected gender_col and preview:")
+_print_preview_info(gender_col, gender_preview_dict)
+
+# Step 4: Feature Engineering and Validation
+import os
+import pandas as pd
+
+# 1) Extract and standardize clinical features (trait, age, gender)
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df,
+    trait=trait,
+    age_col=age_col if 'age_col' in globals() else None,
+    gender_col=gender_col if 'gender_col' in globals() else None
+)
+
+# 2) Normalize gene symbols, drop unrecognized, aggregate duplicates; save normalized gene data
+gene_df_norm = normalize_gene_symbols_in_index(genetic_df.copy())
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_df_norm.to_csv(out_gene_data_file)
+
+# 3) Link clinical and genetic data on sample IDs
+common_samples = selected_clinical_df.index.intersection(gene_df_norm.columns)
+linked_data = pd.concat(
+    [selected_clinical_df.loc[common_samples], gene_df_norm.T.loc[common_samples]],
+    axis=1
+)
+
+# 4) Handle missing values systematically
+processed_df = handle_missing_values(linked_data.copy(), trait_col=trait)
+
+# 5) Determine bias in trait and demographic features; remove biased demographics
+trait_biased, processed_df = judge_and_remove_biased_features(processed_df, trait)
+
+# 6) Final validation and save cohort info
+# Explicitly cast to native Python bools to avoid JSON serialization issues
+is_gene_available = bool((gene_df_norm.shape[0] > 0) and (len(common_samples) > 0))
+is_trait_available = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+trait_biased = bool(trait_biased)
+
+# Note about cohort and trait distribution before processing
+try:
+    trait_counts = linked_data[trait].value_counts(dropna=True).to_dict()
+except Exception:
+    trait_counts = {}
+
+note = f"INFO: Cohort=TCGA UCEC; common_samples={int(len(common_samples))}; trait_counts={trait_counts}."
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=trait_biased,
+    df=processed_df,
+    note=note
+)
+
+# 7) Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    processed_df.to_csv(out_data_file)

output/preprocess/Endometriosis/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE75427": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": true,
-    "has_gender": false,
-    "sample_size": 8
-  },
-  "GSE73622": {
-    "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
-  },
-  "GSE51981": {
-    "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": 148
-  },
-  "GSE37837": {
-    "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
-  },
-  "GSE165004": {
-    "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": 72
-  },
-  "GSE145702": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 48
-  },
-  "GSE145701": {
-    "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
-  },
-  "GSE138297": {
-    "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
-  },
-  "GSE120103": {
-    "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
-  },
-  "GSE111974": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "TCGA": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": false,
-    "sample_size": 201
-  }
-}
+{"GSE75427": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE73622": {"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": 50, "note": "INFO: Probes mapped to genes. Genes before normalization: 117474, after normalization: 24229. Samples before filtering: 50, after filtering: 50. Gender not available (all female)."}, "GSE51981": {"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": 148, "note": "INFO: Probes mapped to gene symbols and normalized by NCBI synonyms. Age and Gender unavailable in clinical data. Linked samples: 148; Features (incl. trait): 19846."}, "GSE37837": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "WARNING: Trait not available (all subjects are endometriosis patients; no control/trait labels)."}, "GSE165004": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE145702": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 48, "note": "INFO: Gender effectively constant female and Age unavailable in this cohort. Samples are eutopic endometrial stromal fibroblasts with hormone treatments; trait derived from 'disease state'."}, "GSE145701": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 48, "note": ""}, "GSE138297": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE120103": {"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": 36, "note": "INFO: Gender was constant (all female) and excluded; Age not available in annotations. Probe-to-gene mapping used GENE_SYMBOL; gene symbols normalized with NCBI synonyms."}, "GSE111974": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 201, "note": "INFO: Cohort=TCGA UCEC; common_samples=201; trait_counts={1: 177, 0: 24}."}}

output/preprocess/Epilepsy/code/GSE123993.py

@@ -0,0 +1,118 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Epilepsy"
+cohort = "GSE123993"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Epilepsy"
+in_cohort_dir = "../DATA/GEO/Epilepsy/GSE123993"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Epilepsy/GSE123993.csv"
+out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE123993.csv"
+out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE123993.csv"
+json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine data availability
+is_gene_available = True  # Affymetrix HuGene 2.1 ST arrays indicate gene expression data
+# Trait of interest is Epilepsy; this dataset is a vitamin D supplementation study with no epilepsy phenotype recorded
+trait_row = None
+
+# No explicit age field in the sample characteristics; background says all >65 (constant, not useful)
+age_row = None
+
+# Gender is available under 'Sex'
+gender_row = 1
+
+# Step 2: Define converters
+import re
+import pandas as pd
+
+def _after_colon(val: str) -> str:
+    if val is None:
+        return ""
+    s = str(val)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    # Binary mapping for Epilepsy trait if present in other datasets; not used here (trait_row is None)
+    v = _after_colon(x).lower()
+    if v in {"epilepsy", "epileptic", "case", "patient", "seizure", "seizures"}:
+        return 1
+    if v in {"control", "healthy", "normal", "non-epilepsy", "none", "na"}:
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous mapping; extract numeric age in years if present
+    v = _after_colon(x)
+    if not v:
+        return None
+    # Find a number (integer or float)
+    m = re.search(r"(\d+(?:\.\d+)?)", v)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary mapping: female->0, male->1
+    v = _after_colon(x).lower()
+    if v in {"male", "m"}:
+        return 1
+    if v in {"female", "f"}:
+        return 0
+    return None
+
+# Step 3: Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction (skip if trait not available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+    clinical_preview = preview_df(selected_clinical_df, n=5)
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)

output/preprocess/Epilepsy/code/GSE143272.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Epilepsy"
+cohort = "GSE143272"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Epilepsy"
+in_cohort_dir = "../DATA/GEO/Epilepsy/GSE143272"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Epilepsy/GSE143272.csv"
+out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE143272.csv"
+out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE143272.csv"
+json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+
+# 1) Assess gene expression data availability (mRNA expression per background info)
+is_gene_available = True
+
+# 2) Identify rows for trait, age, and gender based on the provided Sample Characteristics Dictionary
+trait_row = 2    # 'epilepsy type: -/Idiopathic/Cryptogenic/Symptomatic'
+age_row = 0      # 'age (in years): <number>'
+gender_row = 1   # 'Sex: Male/Female'
+
+# 2.2) Define conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    parts = str(x).split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    # Map to epilepsy presence: 1 for any specified epilepsy type, 0 for '-' or controls
+    if v in {"-", "", "na", "n/a", "control", "healthy", "normal"}:
+        return 0
+    if any(k in v for k in ["idiopathic", "cryptogenic", "symptomatic", "epilepsy"]):
+        return 1
+    return None
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    val = val.strip()
+    try:
+        return float(val)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _extract_value(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 initial metadata (filtering) using the library
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# ILMN_* are Illumina probe IDs (array probes), not human gene symbols; mapping is required.
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide columns for mapping: probe IDs and gene symbols
+probe_id_col_candidates = ['ID', 'Probe_Id', 'Array_Address_Id']
+gene_symbol_col_candidates = ['Symbol', 'ILMN_Gene', 'Gene Symbol', 'GeneSymbol', 'Gene_Symbol']
+
+probe_col = next((c for c in probe_id_col_candidates if c in gene_annotation.columns), None)
+gene_col = next((c for c in gene_symbol_col_candidates if c in gene_annotation.columns), None)
+
+if probe_col is None or gene_col is None:
+    raise ValueError("Could not find appropriate columns for probe IDs and gene symbols in the annotation.")
+
+# 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 (existing gene_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 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
+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 (cast to native bools to avoid JSON issues)
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+note = "INFO: ILMN probe IDs mapped to gene symbols via SOFT (ID->Symbol). Trait derived from 'epilepsy type'; age and gender included."
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Epilepsy/code/GSE199759.py

@@ -0,0 +1,303 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Epilepsy"
+cohort = "GSE199759"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Epilepsy"
+in_cohort_dir = "../DATA/GEO/Epilepsy/GSE199759"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Epilepsy/GSE199759.csv"
+out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE199759.csv"
+out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE199759.csv"
+json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression availability based on background information
+is_gene_available = True  # Agilent LncRNA+mRNA Human Gene Expression Microarray V3.0 suggests mRNA expression data is available.
+
+# 2) Variable availability based on the provided Sample Characteristics Dictionary
+trait_row = None  # No explicit epilepsy status in the provided characteristics; groups (GRE vs GNE) not listed.
+age_row = 2       # 'age: ...y'
+gender_row = 1    # 'gender: Male/Female'
+
+# 2.2) Conversion functions
+def _after_colon(val):
+    if val is None:
+        return None
+    parts = str(val).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(val).strip()
+
+def convert_trait(x):
+    """
+    Generic epilepsy status converter (not used here since trait_row is None).
+    Maps epilepsy-related indications to binary: epilepsy=1, non-epilepsy=0.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Heuristics for GRE vs GNE if ever encountered
+    if any(k in v_low for k in ["gre", "with epilepsy", "epilepsy", "glioma-related epilepsy"]):
+        if any(k in v_low for k in ["gne", "without epilepsy", "no epilepsy"]):
+            return None  # Ambiguous
+        return 1
+    if any(k in v_low for k in ["gne", "without epilepsy", "no epilepsy", "nonepilepsy", "non-epilepsy"]):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Extract integer/float from strings like "47y", "47", "47 years"
+    nums = re.findall(r"\d+\.?\d*", v)
+    if not nums:
+        return None
+    try:
+        val = float(nums[0])
+        return int(val) if val.is_integer() else val
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if "male" in v_low:
+        return 1
+    if "female" in v_low:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (skip because trait_row is None)
+# If trait_row were available:
+# selected_clinical_df = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_clinical_df)
+# selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import os
+import pandas as pd
+
+# 1) Identify the correct SOFT (platform) file and the appropriate columns for probe IDs and gene symbols
+soft_files = [os.path.join(in_cohort_dir, f) for f in os.listdir(in_cohort_dir) if 'soft' in f.lower()]
+
+best_soft = None
+best_probe_col = None
+best_symbol_col = None
+best_match_count = -1  # number of probes matched to gene_data.index
+
+# Use a subset of probe IDs for quick matching
+probe_index = pd.Index(gene_data.index.astype(str))
+subset_probe = probe_index[:min(5000, len(probe_index))]
+
+# Candidate columns to prioritize for gene symbols
+symbol_priority = [
+    'Gene Symbol', 'GENE_SYMBOL', 'GeneSymbol', 'Symbol', 'SYMBOL', 'gene_symbol',
+    'GENESYMBOL', 'Gene Name', 'GENE_NAME', 'gene_assignment', 'DESCRIPTION',
+    'Description', 'Entrez Gene Symbol', 'ENTREZ_GENE_SYMBOL'
+]
+
+for sf in soft_files:
+    try:
+        ann = get_gene_annotation(sf)
+        if ann is None or not isinstance(ann, pd.DataFrame) or ann.empty:
+            continue
+
+        # Identify which column in this annotation best matches our probe IDs
+        probe_col_candidate = None
+        probe_match_counts = {}
+        for col in ann.columns:
+            try:
+                match_count = ann[col].astype(str).isin(subset_probe).sum()
+                probe_match_counts[col] = match_count
+            except Exception:
+                continue
+
+        if not probe_match_counts:
+            continue
+
+        # Choose the column with maximum matches
+        candidate_col, candidate_count = max(probe_match_counts.items(), key=lambda x: x[1])
+
+        # Require at least some reasonable overlap to consider this platform relevant
+        if candidate_count > best_match_count and candidate_count > 0:
+            # Find a gene symbol column in this annotation
+            symbol_col = None
+            # First try priority list
+            for c in symbol_priority:
+                if c in ann.columns:
+                    symbol_col = c
+                    break
+            # If none from priority list, choose the column with the most extractable human symbols
+            if symbol_col is None:
+                non_empty_counts = {}
+                for col in ann.columns:
+                    try:
+                        symbols_extracted = ann[col].astype(str).map(extract_human_gene_symbols)
+                        non_empty_counts[col] = symbols_extracted.map(lambda x: len(x) > 0).sum()
+                    except Exception:
+                        continue
+                if non_empty_counts:
+                    symbol_col = max(non_empty_counts.items(), key=lambda x: x[1])[0]
+
+            if symbol_col is not None:
+                best_soft = sf
+                best_probe_col = candidate_col
+                best_symbol_col = symbol_col
+                best_match_count = candidate_count
+    except Exception:
+        continue
+
+# 2) Build mapping dataframe and 3) apply mapping to convert probe-level data to gene-level data
+if best_soft is not None and best_probe_col is not None and best_symbol_col is not None:
+    selected_annotation = get_gene_annotation(best_soft)
+    mapping_df = get_gene_mapping(selected_annotation, prob_col=best_probe_col, gene_col=best_symbol_col)
+    gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+else:
+    # If no suitable mapping was found, keep gene_data unchanged (probe-level) to avoid crashing downstream steps.
+    # This branch should be rare; it indicates annotation mismatch (e.g., miRNA platform vs mRNA probes).
+    gene_data = gene_data
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene matrix
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Helper flags and note assembly
+is_gene_available_fin = not normalized_gene_data.empty
+notes = []
+if not is_gene_available_fin:
+    notes.append("WARNING: Normalized gene matrix is empty after symbol normalization; "
+                 "probe->gene mapping likely failed due to platform annotation mismatch (e.g., miRNA vs mRNA).")
+
+linked_data = None  # default
+
+# 2-6. Proceed only if clinical features were extracted (i.e., selected_clinical_data exists and contains the trait)
+if 'selected_clinical_data' in globals() and isinstance(selected_clinical_data, pd.DataFrame) and not selected_clinical_data.empty:
+    try:
+        # 2. Link clinical and genetic data
+        linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+        # 3. Handle missing values
+        linked_data = handle_missing_values(linked_data, trait)
+
+        # 4. Bias checking and removal of biased covariates
+        is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+        # 5. Final validation and metadata saving
+        os.makedirs(os.path.dirname(json_path), exist_ok=True)
+        is_usable = validate_and_save_cohort_info(
+            is_final=True,
+            cohort=cohort,
+            info_path=json_path,
+            is_gene_available=is_gene_available_fin,
+            is_trait_available=True,
+            is_biased=is_trait_biased,
+            df=unbiased_linked_data,
+            note=(" ".join(notes) if notes else "INFO: Clinical features linked and processed.")
+        )
+
+        # 6. Save linked data only if usable
+        if is_usable:
+            os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+            unbiased_linked_data.to_csv(out_data_file)
+
+    except Exception as e:
+        # If anything fails during linking due to unexpected shapes/availability, record as unavailable
+        notes.append(f"ERROR: Linking/processing failed with error: {e}")
+        os.makedirs(os.path.dirname(json_path), exist_ok=True)
+        _ = validate_and_save_cohort_info(
+            is_final=True,
+            cohort=cohort,
+            info_path=json_path,
+            is_gene_available=is_gene_available_fin,
+            is_trait_available=False,
+            is_biased=False,
+            df=pd.DataFrame(),
+            note=" ".join(notes)
+        )
+else:
+    # Trait not available (as in this cohort), skip linking and mark dataset unusable
+    notes.append("INFO: Clinical trait labels (Epilepsy) not available in series matrix; "
+                 "skipping linking and marking dataset as unusable.")
+    os.makedirs(os.path.dirname(json_path), exist_ok=True)
+    _ = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available_fin,
+        is_trait_available=False,
+        is_biased=False,
+        df=pd.DataFrame(),
+        note=" ".join(notes)
+    )

output/preprocess/Epilepsy/code/GSE273630.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Epilepsy"
+cohort = "GSE273630"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Epilepsy"
+in_cohort_dir = "../DATA/GEO/Epilepsy/GSE273630"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Epilepsy/GSE273630.csv"
+out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE273630.csv"
+out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE273630.csv"
+json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine gene expression availability
+is_gene_available = True  # NanoString digital transcript panel indicates gene expression data
+
+# Step 2: Variable availability and conversion functions
+
+# Availability from the provided Sample Characteristics Dictionary and background info:
+# - Trait (Epilepsy): Excluded by design; no sample-level field -> not available
+# - Age: No per-sample age field provided -> not available
+# - Gender: All participants are males by design (constant) -> not available
+trait_row = None
+age_row = None
+gender_row = None
+
+# Conversion functions (defined for interface completeness; they won't be used since rows are None)
+def _extract_value(cell):
+    if cell is None:
+        return None
+    if isinstance(cell, str):
+        parts = cell.split(":", 1)
+        return parts[1].strip() if len(parts) == 2 else cell.strip()
+    return cell
+
+def convert_trait(x):
+    # Map epilepsy-related information to binary (0/1); default to None if unknown
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    # Positive indications
+    positive_kw = ["epilep", "seizure", "ictal", "sz"]
+    # Negative phrases
+    negative_kw = ["no epilepsy", "non-epilep", "without epilepsy", "seizure-free", "no seizure", "none"]
+    if any(k in s for k in negative_kw):
+        return 0
+    if any(k in s for k in positive_kw):
+        # avoid false positives if explicitly negated
+        if "no " in s or "not " in s:
+            return 0
+        return 1
+    return None
+
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = str(val)
+    # Extract first number (integer or float)
+    import re
+    m = re.search(r"(-?\d+\.?\d*)", s)
+    if m:
+        try:
+            return float(m.group(1))
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    s = str(val).strip().lower()
+    if s in ["male", "m", "man", "boy"]:
+        return 1
+    if s in ["female", "f", "woman", "girl"]:
+        return 0
+    # Try to infer from single letters embedded
+    if "male" in s:
+        return 1
+    if "female" in s:
+        return 0
+    return None
+
+# Step 3: Save metadata using initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(is_final=False,
+                                  cohort=cohort,
+                                  info_path=json_path,
+                                  is_gene_available=is_gene_available,
+                                  is_trait_available=is_trait_available)
+
+# Step 4: Clinical feature extraction
+# Skipped because trait_row is None (no clinical trait data available for Epilepsy in this dataset)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = False
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. Proceed only if clinical data with trait exists (guard safely via locals().get)
+if (locals().get('selected_clinical_data') is not None) and (locals().get('trait_row') is not None):
+    # Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # Bias check and remove biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Clinical features extracted and linked successfully."
+    )
+
+    # Save linked data only if usable
+    if is_usable:
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # No clinical trait data available; only gene data saved in this step
+    print("No clinical trait data available; skipping linking and final validation for this cohort.")

output/preprocess/Epilepsy/code/GSE29796.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Epilepsy"
+cohort = "GSE29796"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Epilepsy"
+in_cohort_dir = "../DATA/GEO/Epilepsy/GSE29796"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Epilepsy/GSE29796.csv"
+out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE29796.csv"
+out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE29796.csv"
+json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Expression profiling of human glial cells; not miRNA/methylation only.
+
+# 2) Variable Availability
+# From the provided characteristics dictionary, epilepsy appears under "pathology" at key 1.
+trait_row = 1
+age_row = None
+gender_row = None
+
+# 2.2) Data Type Conversion
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    val = _after_colon(x)
+    if val is None or val == '':
+        return None
+    v = val.strip().lower()
+    # Treat common unknown tokens as missing
+    if v in {'na', 'n/a', 'unknown', 'not available', 'nan', 'none', 'missing', 'null', 'undetermined'}:
+        return None
+    # Positive if explicitly labeled epilepsy; otherwise negative.
+    if v in {'epilepsy', 'epileptic'}:
+        return 1
+    # For other pathologies (tumor types etc.), map to 0
+    return 0
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None or val == '':
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+", str(val))
+    return float(m.group()) if m else None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None or val == '':
+        return None
+    v = val.strip().lower()
+    if v in {'female', 'f', 'woman', 'women', 'girl'}:
+        return 0
+    if v in {'male', 'm', 'man', 'men', 'boy'}:
+        return 1
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    # Ensure output directory exists and save
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+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 in the annotation for mapping:
+# - Probe/feature identifiers: 'ID' (matches gene_data index like '1007_s_at')
+# - Gene symbols: 'Gene Symbol'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(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
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+covariate_cols = [trait] + [c for c in ['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]
+
+# Ensure Python-native bools for JSON serialization
+is_gene_available_final = bool(len(gene_cols_present) > 0)
+is_trait_available_final = bool((trait in unbiased_linked_data.columns) and bool(unbiased_linked_data[trait].notna().any()))
+
+note = ("INFO: Trait (Epilepsy) inferred from 'pathology' field; Affymetrix probe IDs mapped to gene symbols using "
+        "platform annotation, then normalized via NCBI gene synonym table. Missing values handled per protocol.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked 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/Epilepsy/code/GSE42986.py

@@ -0,0 +1,120 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Epilepsy"
+cohort = "GSE42986"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Epilepsy"
+in_cohort_dir = "../DATA/GEO/Epilepsy/GSE42986"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Epilepsy/GSE42986.csv"
+out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE42986.csv"
+out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE42986.csv"
+json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression availability
+is_gene_available = True  # Affymetrix Human Exon 1.0 ST -> mRNA expression, not miRNA/methylation
+
+# 2) Variable availability based on provided Sample Characteristics Dictionary
+trait_row = None  # No epilepsy-related field available in this dataset
+age_row = 3       # 'age (years): ...'
+gender_row = 2    # 'gender: F/M'
+
+# 2.2) Conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    return s.strip()
+
+def convert_trait(x):
+    # Trait is Epilepsy; dataset does not provide epilepsy info -> return None unless explicitly stated
+    s = _after_colon(x)
+    if s is None:
+        return None
+    sl = s.lower()
+    if 'epilepsy' in sl or 'epileptic' in sl:
+        return 1
+    if 'control' in sl or 'healthy' in sl or 'non-epilepsy' in sl or 'non-epileptic' in sl or 'no epilepsy' in sl:
+        return 0
+    return None
+
+def convert_age(x):
+    s = _after_colon(x)
+    if s is None:
+        return None
+    sl = s.lower()
+    if sl in {'na', 'n/a', 'not obtained', 'unknown', ''}:
+        return None
+    s = s.replace('years', '').strip()
+    try:
+        return float(s)
+    except Exception:
+        m = re.search(r'[-+]?\d*\.?\d+', s)
+        return float(m.group()) if m else None
+
+def convert_gender(x):
+    s = _after_colon(x)
+    if s is None:
+        return None
+    sl = s.lower()
+    if sl in {'f', 'female', 'woman', 'women', 'girl'}:
+        return 0
+    if sl in {'m', 'male', 'man', 'men', 'boy'}:
+        return 1
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If trait_row becomes available in future steps, uncomment below:
+# if trait_row is not None:
+#     selected_clinical_df = geo_select_clinical_features(
+#         clinical_df=clinical_data,
+#         trait=trait,
+#         trait_row=trait_row,
+#         convert_trait=convert_trait,
+#         age_row=age_row,
+#         convert_age=convert_age,
+#         gender_row=gender_row,
+#         convert_gender=convert_gender
+#     )
+#     _ = preview_df(selected_clinical_df)
+#     os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+#     selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Epilepsy/code/GSE63808.py

@@ -0,0 +1,150 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Epilepsy"
+cohort = "GSE63808"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Epilepsy"
+in_cohort_dir = "../DATA/GEO/Epilepsy/GSE63808"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Epilepsy/GSE63808.csv"
+out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE63808.csv"
+out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE63808.csv"
+json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Series describes mRNA expression in human hippocampus biopsies; not miRNA-only or methylation.
+
+# 2) Variable Availability and Data Type Conversion
+
+# From the provided characteristics:
+# {0: ['tissue: hippocampal formation'], 1: ['phenotype: epilepsy']}
+# - Trait appears constant ("epilepsy") with no controls -> not usable for association.
+# - Age/Gender not present.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Data type choices
+trait_type = 'binary'
+age_type = 'continuous'
+gender_type = 'binary'
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(':', 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    """Map epilepsy-related statuses to binary: control=0, epilepsy/TLE=1; unknown -> None."""
+    val = _after_colon(x)
+    if val is None or val == '':
+        return None
+    v = val.lower()
+    # Positive (epilepsy) indicators
+    pos_terms = [
+        'epilepsy', 'temporal lobe epilepsy', 'tle', 'seizure', 'patient', 'case',
+        'pharmacoresistant', 'intractable'
+    ]
+    # Negative (control) indicators
+    neg_terms = ['control', 'healthy', 'non-epilepsy', 'nonepilepsy', 'normal', 'non epilepsy']
+    if any(t in v for t in pos_terms):
+        return 1
+    if any(t in v for t in neg_terms):
+        return 0
+    # binary flags like "yes"/"no"
+    if v in {'yes', 'y', 'true', '1'}:
+        return 1
+    if v in {'no', 'n', 'false', '0'}:
+        return 0
+    return None
+
+def convert_age(x):
+    """Extract age in years as float. Supports 'years' or 'months' (converted to years)."""
+    val = _after_colon(x)
+    if val is None or val == '':
+        return None
+    v = val.lower()
+    # Find first numeric token
+    m = re.search(r'(\d+(?:\.\d+)?)', v)
+    if not m:
+        return None
+    num = float(m.group(1))
+    # Determine unit
+    if any(u in v for u in ['month', 'months', 'mo', 'mth', 'mths']):
+        return round(num / 12.0, 3)
+    # Default to years if unspecified or contains year terms
+    return num
+
+def convert_gender(x):
+    """Map gender to binary: female=0, male=1; unknown -> None."""
+    val = _after_colon(x)
+    if val is None or val == '':
+        return None
+    v = val.strip().lower()
+    # Common mappings
+    if v in {'male', 'm', 'man', 'boy'}:
+        return 1
+    if v in {'female', 'f', 'woman', 'girl'}:
+        return 0
+    # Sometimes coded as 0/1 or True/False
+    if v in {'1', 'true'}:
+        return 1
+    if v in {'0', 'false'}:
+        return 0
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (skip because trait_row is None)
+if (trait_row is not None) and ('clinical_data' in globals()):
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+    _ = preview_df(selected_clinical_df)
+    # Save clinical features
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Epilepsy/code/GSE64123.py

@@ -0,0 +1,79 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Epilepsy"
+cohort = "GSE64123"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Epilepsy"
+in_cohort_dir = "../DATA/GEO/Epilepsy/GSE64123"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Epilepsy/GSE64123.csv"
+out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE64123.csv"
+out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE64123.csv"
+json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Determine data availability based on provided background and sample characteristics
+is_gene_available = True  # Gene expression profiling is likely (hESC neurotox assay with multiple time points/exposures)
+trait_row = None          # No human Epilepsy phenotype; dataset is drug exposure in hESCs
+age_row = None            # Not human subject data
+gender_row = None         # Not human subject data
+
+# Converters (not used since corresponding rows are None)
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ":" in s:
+        return s.split(":", 1)[1].strip()
+    return s.strip()
+
+def convert_trait(x):
+    # No epilepsy trait available in this cohort
+    _ = _extract_after_colon(x)
+    return None
+
+def convert_age(x):
+    # No human age data
+    _ = _extract_after_colon(x)
+    return None
+
+def convert_gender(x):
+    # No human gender data
+    _ = _extract_after_colon(x)
+    return None
+
+# Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction is skipped because trait_row is None (no clinical data available)

output/preprocess/Epilepsy/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE74571": {
-    "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
-  },
-  "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
-  },
-  "GSE64123": {
-    "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
-  },
-  "GSE63808": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 129
-  },
-  "GSE42986": {
-    "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
-  },
-  "GSE29796": {
-    "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": 72
-  },
-  "GSE273630": {
-    "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
-  },
-  "GSE199759": {
-    "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
-  },
-  "GSE143272": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 142
-  },
-  "GSE123993": {
-    "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": 44
-  },
-  "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": 702
-  }
-}
+{"GSE74571": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE65106": {"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 'Epilepsy' is not recorded in this ASD cohort; clinical linking skipped."}, "GSE64123": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE63808": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE42986": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE29796": {"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": 72, "note": "INFO: Trait (Epilepsy) inferred from 'pathology' field; Affymetrix probe IDs mapped to gene symbols using platform annotation, then normalized via NCBI gene synonym table. Missing values handled per protocol."}, "GSE273630": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE199759": {"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": "WARNING: Normalized gene matrix is empty after symbol normalization; probe->gene mapping likely failed due to platform annotation mismatch (e.g., miRNA vs mRNA). INFO: Clinical trait labels (Epilepsy) not available in series matrix; skipping linking and marking dataset as unusable."}, "GSE143272": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 142, "note": "INFO: ILMN probe IDs mapped to gene symbols via SOFT (ID->Symbol). Trait derived from 'epilepsy type'; age and gender included."}, "GSE123993": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Glioblastoma/code/GSE148949.py

@@ -0,0 +1,264 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE148949"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE148949"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Glioblastoma/GSE148949.csv"
+out_gene_data_file = "./output/z3/preprocess/Glioblastoma/gene_data/GSE148949.csv"
+out_clinical_data_file = "./output/z3/preprocess/Glioblastoma/clinical_data/GSE148949.csv"
+json_path = "./output/z3/preprocess/Glioblastoma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+import os
+
+# 1. Identify the paths to the SOFT file and the matrix file, ensuring they belong to the target cohort
+files = os.listdir(in_cohort_dir)
+
+# Prefer files that include the cohort accession explicitly
+soft_candidates = [f for f in files if ('soft' in f.lower()) and (cohort.lower() in f.lower())]
+matrix_candidates = [f for f in files if ('series_matrix' in f.lower()) and (cohort.lower() in f.lower())]
+
+# Fallbacks if strict matching fails
+if not soft_candidates:
+    soft_candidates = [f for f in files if 'soft' in f.lower()]
+if not matrix_candidates:
+    matrix_candidates = [f for f in files if 'matrix' in f.lower()]
+
+assert len(soft_candidates) > 0 and len(matrix_candidates) > 0, "SOFT or matrix files not found in cohort directory."
+
+soft_file = os.path.join(in_cohort_dir, soft_candidates[0])
+matrix_file = os.path.join(in_cohort_dir, matrix_candidates[0])
+
+# Assert that chosen files likely correspond to the cohort
+assert cohort.lower() in os.path.basename(soft_file).lower() or 'soft' in os.path.basename(soft_file).lower()
+assert cohort.lower() in os.path.basename(matrix_file).lower() or 'matrix' in os.path.basename(matrix_file).lower()
+
+print(f"Selected SOFT file: {soft_file}")
+print(f"Selected matrix file: {matrix_file}")
+
+# 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. Improve readability of clinical feature overview by setting meaningful row labels
+clinical_named = clinical_data.copy()
+if clinical_named.shape[1] > 0:
+    # Use the first column as row labels and drop it from data
+    clinical_named.index = clinical_named.iloc[:, 0]
+    clinical_named = clinical_named.drop(columns=clinical_named.columns[0])
+
+# Obtain the sample characteristics dictionary with a concise cap on unique values per feature
+sample_characteristics_dict = get_unique_values_by_row(clinical_named, max_len=20)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine gene expression availability based on background info
+# The series uses 2-channel whole human genome Agilent microarrays (mRNA), so gene expression data is available.
+is_gene_available = True
+
+# Step 2: Variable availability and conversion functions
+# From the provided sample characteristics, there is no per-sample human clinical information (only a single descriptive line).
+# Therefore, trait (Glioblastoma status), age, and gender are not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define conversion functions (will not be used since corresponding rows are None).
+def convert_trait(x):
+    # Not available; return None
+    return None
+
+def convert_age(x):
+    # Not available; return None
+    return None
+
+def convert_gender(x):
+    # Not available; return None
+    return None
+
+# Step 3: Save metadata with initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical Feature Extraction (skip because trait_row is None)
+# If trait_row were available:
+# selected_clinical_df = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_clinical_df)
+# selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import gzip
+import io
+import pandas as pd
+
+# Prepare expression ID set
+expr_ids = set(gene_data.index.astype(str).str.strip())
+
+# Parse platform annotation tables from the family SOFT
+platform_tables = []
+capture = False
+buf = []
+
+with gzip.open(soft_file, 'rt', errors='ignore') as f:
+    for line in f:
+        line = line.rstrip('\n')
+        if line.startswith('!platform_table_begin'):
+            capture = True
+            buf = []
+            continue
+        if line.startswith('!platform_table_end') and capture:
+            capture = False
+            table_str = '\n'.join(buf)
+            try:
+                df = pd.read_csv(io.StringIO(table_str), sep='\t', dtype=str, on_bad_lines='skip', low_memory=False)
+                platform_tables.append(df)
+            except Exception as e:
+                print(f"Failed to parse a platform table: {e}")
+            continue
+        if capture:
+            buf.append(line)
+
+# If no platform table could be parsed, raise informative error
+if len(platform_tables) == 0:
+    raise ValueError("No platform annotation tables were found in the SOFT file between !platform_table_begin/end.")
+
+# Identify the best matching table and identifier column by overlap with expression IDs
+best = {'table_idx': None, 'id_col': None, 'overlap': -1}
+overlap_matrix = []
+
+for ti, df in enumerate(platform_tables):
+    for col in df.columns:
+        col_values = set(df[col].dropna().astype(str).str.strip())
+        overlap = len(expr_ids.intersection(col_values))
+        overlap_matrix.append((ti, col, overlap))
+        if overlap > best['overlap']:
+            best = {'table_idx': ti, 'id_col': col, 'overlap': overlap}
+
+# Print overlap summary for transparency
+print("Overlap counts (table_index, column, overlap):")
+for ti, col, ov in sorted(overlap_matrix, key=lambda x: (-x[2], x[0], x[1]))[:20]:
+    print(f"{ti}, {col}, {ov}")
+
+# Validate that we have a meaningful overlap
+if best['overlap'] <= 0 or best['table_idx'] is None or best['id_col'] is None:
+    raise ValueError(
+        "Failed to find any overlap between expression IDs and platform annotation columns. "
+        "Cannot proceed with mapping when requires_gene_mapping = True."
+    )
+
+selected_table = platform_tables[best['table_idx']]
+id_col = best['id_col']
+print(f"Selected table index: {best['table_idx']}")
+print(f"Selected ID column: {id_col} with overlap {best['overlap']}")
+
+# Choose a gene symbol column
+preferred_gene_cols = [
+    'Gene Symbol', 'GENE_SYMBOL', 'Gene symbol', 'SYMBOL', 'Symbol', 'GeneSymbol',
+    'GENE SYMBOL', 'ORF', 'GENE', 'Gene', 'Gene Name', 'GENE_NAME', 'GENE NAME', 'Gene Description',
+    'GENE_DESCRIPTION', 'DESCRIPTION', 'Gene title', 'GENE TITLE', 'GB_ACC', 'RefSeq Accession'
+]
+gene_col = None
+for cand in preferred_gene_cols:
+    if cand in selected_table.columns:
+        gene_col = cand
+        break
+
+# If no preferred column found, heuristically select the column that yields the most extractable human symbols
+if gene_col is None:
+    def score_gene_column(series: pd.Series) -> int:
+        # Count rows where we can extract at least one human gene symbol
+        return series.dropna().astype(str).apply(lambda s: len(extract_human_gene_symbols(s)) > 0).sum()
+    scores = {col: score_gene_column(selected_table[col]) for col in selected_table.columns if col != id_col}
+    # Pick the column with highest score
+    if len(scores) > 0:
+        gene_col = max(scores, key=scores.get)
+    else:
+        raise ValueError("No suitable gene symbol column could be identified in the platform table.")
+
+print(f"Selected Gene Symbol column: {gene_col}")
+
+# Build mapping and ensure overlap isn’t trivial
+mapping_df = get_gene_mapping(selected_table, prob_col=id_col, gene_col=gene_col)
+
+mapped_id_overlap = len(set(mapping_df['ID']).intersection(expr_ids))
+print(f"Mapping ID overlap with expression IDs: {mapped_id_overlap}")
+
+if mapped_id_overlap <= 0:
+    raise ValueError(
+        f"Constructed mapping has no overlap with expression IDs using id_col={id_col} and gene_col={gene_col}."
+    )
+
+# Apply mapping to convert probe-level to gene-level
+mapped_gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Validate that the mapping produced gene-level data
+if mapped_gene_data.shape[0] == 0:
+    raise ValueError("Mapping produced an empty gene expression matrix. Aborting to avoid using unmapped probe data.")
+
+print(f"Mapped probes: {mapped_id_overlap}")
+print(f"Resulting genes: {mapped_gene_data.shape[0]}")
+
+# Replace gene_data with mapped gene-level data
+gene_data = mapped_gene_data
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. No clinical/trait data available for this cohort; record availability without triggering final validation
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False
+)

output/preprocess/Glioblastoma/code/GSE159000.py

@@ -0,0 +1,203 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE159000"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE159000"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Glioblastoma/GSE159000.csv"
+out_gene_data_file = "./output/z3/preprocess/Glioblastoma/gene_data/GSE159000.csv"
+out_clinical_data_file = "./output/z3/preprocess/Glioblastoma/clinical_data/GSE159000.csv"
+json_path = "./output/z3/preprocess/Glioblastoma/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  # Title indicates "Gene expression profiles"
+
+# 2) Variable availability and conversion functions
+
+# Trait (Glioblastoma): all samples are GBM patients (constant), so not available
+trait_row = None
+
+def convert_trait(x):
+    # Map disease status to binary: control/normal=0, GBM/case=1
+    if x is None:
+        return None
+    val = str(x)
+    if ':' in val:
+        val = val.split(':', 1)[1]
+    v = val.strip().lower()
+    if v in {'gbm', 'glioblastoma', 'glioblastoma multiforme', 'case', 'tumor', 'tumour', 'cancer', 'patient', 'yes'}:
+        return 1
+    if v in {'control', 'normal', 'non-tumor', 'non tumour', 'non-tumour', 'healthy', 'adjacent normal', 'no'}:
+        return 0
+    return None
+
+# Age: available at row 2, continuous
+age_row = 2
+
+def convert_age(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip()
+    m = re.search(r'[-+]?\d*\.?\d+', s)
+    if not m:
+        return None
+    try:
+        return float(m.group())
+    except Exception:
+        return None
+
+# Gender: available at row 1, binary (female=0, male=1)
+gender_row = 1
+
+def convert_gender(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    v = s.strip().lower()
+    if v in {'f', 'female', 'woman', 'girl'}:
+        return 0
+    if v in {'m', 'male', 'man', 'boy'}:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If trait_row were available, we would extract and save clinical features as below:
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# 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
+# Ensure required dataframes are available
+if 'gene_annotation' not in locals():
+    gene_annotation = get_gene_annotation(soft_file)
+if 'gene_data' not in locals():
+    gene_data = get_genetic_data(matrix_file)
+
+# 1-2) Decide mapping columns and extract mapping dataframe
+# Probe IDs in expression data are Illumina IDs like 'ILMN_...' which match the 'ID' column in annotation.
+# Gene symbols are in the 'Symbol' column based on the preview.
+prob_col = 'ID'
+gene_col = 'Symbol'
+gene_mapping = 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=gene_mapping)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data only if clinical features exist
+linked_data = None
+if 'selected_clinical_data' in locals() and isinstance(selected_clinical_data, pd.DataFrame):
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+# 3-4. Handle missing values and bias checks only if linked data is available
+if linked_data is not None:
+    linked_data = handle_missing_values(linked_data, trait)
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info (clinical data available)
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Linked clinical and genetic data; performed missing value handling and bias checks."
+    )
+
+    # 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/clinical data not available; record this and skip linking/imputation
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,  # Placeholder; not used since is_available will be False
+        df=normalized_gene_data,  # Provide non-empty df to avoid false gene unavailability
+        note="INFO: Trait not available; clinical linking skipped."
+    )

output/preprocess/Glioblastoma/code/GSE175700.py

@@ -0,0 +1,220 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE175700"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE175700"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Glioblastoma/GSE175700.csv"
+out_gene_data_file = "./output/z3/preprocess/Glioblastoma/gene_data/GSE175700.csv"
+out_clinical_data_file = "./output/z3/preprocess/Glioblastoma/clinical_data/GSE175700.csv"
+json_path = "./output/z3/preprocess/Glioblastoma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import pandas as pd
+
+# 1) Gene expression data availability
+# Background indicates microarray-based transcriptome profiling on U87 glioblastoma cell line.
+is_gene_available = True
+
+# 2) Variable availability inferred from Sample Characteristics Dictionary:
+# {0: ['tissue: brain'], 1: ['Sex: male']}
+# - Trait (Glioblastoma): not explicitly available and is constant (U87 glioblastoma cell line) -> not usable.
+# - Age: not available.
+# - Gender: constant 'male' -> not usable.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Converters
+def _extract_value(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    v = s.split(":", 1)[1] if ":" in s else s
+    v = v.strip()
+    if v == "" or v.lower() in {"na", "n/a", "null", "none", "unknown", "not available", "not applicable", "?"}:
+        return None
+    return v
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Map to binary: presence of glioblastoma (or glioma/GBM/U87) = 1, otherwise 0
+    positive_markers = ["glioblastoma", "gbm", "glioma", "u87"]
+    negative_markers = ["normal", "healthy", "control", "non-tumor", "non tumour", "noncancer", "non-cancer"]
+    if any(m in vl for m in positive_markers):
+        return 1
+    if any(m in vl for m in negative_markers):
+        return 0
+    # If ambiguous (e.g., treatment labels only), return None
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    # Extract first number as age (years)
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in {"female", "f", "woman", "girl"}:
+        return 0
+    if vl in {"male", "m", "man", "boy"}:
+        return 1
+    return None
+
+# 3) Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction: skipped because trait_row is None (no usable clinical trait data)
+# If trait_row becomes available in future, uncomment the following:
+# selected_df = geo_select_clinical_features(
+#     clinical_df=clinical_data,
+#     trait=trait,
+#     trait_row=trait_row,
+#     convert_trait=convert_trait,
+#     age_row=age_row,
+#     convert_age=convert_age,
+#     gender_row=gender_row,
+#     convert_gender=convert_gender
+# )
+# preview = preview_df(selected_df)
+# selected_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine appropriate columns for probe IDs and gene symbols from the annotation dataframe
+probe_id_candidates = ['probeset_id', 'ID', 'transcript_cluster_id', 'probe_id', 'PROBESET_ID']
+gene_symbol_candidates = ['gene_assignment', 'Gene Symbol', 'gene_symbol', 'gene', 'mrna_assignment', 'symbol']
+
+probe_col = next((c for c in probe_id_candidates if c in gene_annotation.columns), None)
+gene_col = next((c for c in gene_symbol_candidates if c in gene_annotation.columns), None)
+
+if probe_col is None or gene_col is None:
+    missing = []
+    if probe_col is None:
+        missing.append('probe identifier column')
+    if gene_col is None:
+        missing.append('gene symbol column')
+    raise ValueError(f"Could not find required column(s) in gene annotation: {', '.join(missing)}")
+
+# 2) Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3) Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1) Normalize gene symbols and save gene-level 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)
+
+# 2) Link clinical and genetic data only if clinical features were extracted previously and trait is available
+linked_data = None
+clinical_var = None
+if 'selected_clinical_data' in globals() and isinstance(selected_clinical_data, pd.DataFrame):
+    clinical_var = selected_clinical_data
+elif 'selected_df' in globals() and isinstance(selected_df, pd.DataFrame):
+    clinical_var = selected_df
+
+if clinical_var is not None and ('trait_row' in globals() and trait_row is not None):
+    linked_data = geo_link_clinical_genetic_data(clinical_var, 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
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Clinical trait data present and linked."
+    )
+
+    # 6) Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # No usable clinical trait data; record metadata with initial filtering only.
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Glioblastoma/code/GSE178236.py

@@ -0,0 +1,222 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE178236"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE178236"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Glioblastoma/GSE178236.csv"
+out_gene_data_file = "./output/z3/preprocess/Glioblastoma/gene_data/GSE178236.csv"
+out_clinical_data_file = "./output/z3/preprocess/Glioblastoma/clinical_data/GSE178236.csv"
+json_path = "./output/z3/preprocess/Glioblastoma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine data availability
+is_gene_available = True  # Genome-wide gene expression profiling indicated in background info
+
+# Step 2: Variable availability and converters based on Sample Characteristics Dictionary
+# From the dictionary:
+# 1 -> gender, 2 -> age; Trait (Glioblastoma) is constant across samples -> not usable
+trait_row = None
+age_row = 2
+gender_row = 1
+
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1].strip() if len(parts) > 1 else x.strip()
+        val = val.strip().strip('"').strip("'")
+        return val if val != "" else None
+    return x
+
+def convert_trait(x):
+    # Not used since trait_row is None; define robustly if needed
+    v = _extract_after_colon(x)
+    if v is None:
+        return None
+    v_low = str(v).lower()
+    # Map glioblastoma-related indications to 1, others to 0 (if ever used)
+    if "glioblastoma" in v_low or "gbm" in v_low:
+        return 1
+    if v_low in {"control", "normal", "healthy"}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_after_colon(x)
+    if v is None:
+        return None
+    v_low = str(v).lower()
+    if v_low in {"na", "n/a", "nan", "none", "unknown", ""}:
+        return None
+    # Keep only digits and potential decimal point
+    try:
+        return float(v)
+    except Exception:
+        # Try to parse integers embedded in strings
+        import re
+        nums = re.findall(r"[-+]?\d*\.?\d+", v_low)
+        if nums:
+            try:
+                return float(nums[0])
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    v = _extract_after_colon(x)
+    if v is None:
+        return None
+    v_low = str(v).lower().strip()
+    if v_low in {"male", "m"}:
+        return 1
+    if v_low in {"female", "f"}:
+        return 0
+    if v_low in {"na", "n/a", "nan", "none", "unknown", ""}:
+        return None
+    return None
+
+# Step 3: Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical Feature Extraction (skip because trait_row is None)
+# If trait_row were available, we would extract and save clinical features like below:
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+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'       # Matches ILMN_* probe IDs seen in the expression data
+gene_col = 'Symbol'    # Gene symbols column in the annotation
+
+# Build the probe-to-gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+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
+
+# 1. Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine trait availability based on previous steps
+trait_available = ('trait_row' in globals()) and (trait_row is not None)
+
+if trait_available:
+    # Ensure clinical features are extracted before linking
+    selected_clinical_data = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row if 'age_row' in globals() else None,
+        convert_age=convert_age if 'convert_age' in globals() else None,
+        gender_row=gender_row if 'gender_row' in globals() else None,
+        convert_gender=convert_gender if 'convert_gender' in globals() else None
+    )
+
+    # 2. Link the clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Bias checks (trait determines usability; biased covariates are dropped)
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Proceeded with full preprocessing; clinical features extracted and linked."
+    )
+
+    # 6. Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # Trait not available; cannot proceed with linking or final validation
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Glioblastoma/code/GSE226976.py

@@ -0,0 +1,191 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE226976"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE226976"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Glioblastoma/GSE226976.csv"
+out_gene_data_file = "./output/z3/preprocess/Glioblastoma/gene_data/GSE226976.csv"
+out_clinical_data_file = "./output/z3/preprocess/Glioblastoma/clinical_data/GSE226976.csv"
+json_path = "./output/z3/preprocess/Glioblastoma/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 availability based on background info
+is_gene_available = True  # "Gene expression data for samples included in this trial..."
+
+# Step 2: Variable availability and converters
+# Sample Characteristics only has one constant field: {0: ['sample type: recurrent glioma']}
+# No varying trait/age/gender information is available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Converters (robust implementations, though not used since rows are None)
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(x).strip()
+
+def convert_trait(x):
+    v = _after_colon(x).lower()
+    if v == "":
+        return None
+    # Map glioblastoma/GBM to 1, other gliomas to 0
+    if any(k in v for k in ["glioblastoma", "gbm", "grade iv"]):
+        return 1
+    if "glioma" in v:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if not v:
+        return None
+    # Extract first number as age
+    import re
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x).lower()
+    if not v:
+        return None
+    if v in ["male", "m", "man"]:
+        return 1
+    if v in ["female", "f", "woman"]:
+        return 0
+    return None
+
+# Step 3: Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction (skip because trait_row is None)
+# If trait_row were available, we would extract and save clinical features:
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# 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 displayed identifiers (e.g., A2M, AKT1, APOE) are standard human gene symbols (HGNC).
+requires_gene_mapping = False
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Data Normalization and Linking
+import os
+
+# 1) Normalize gene symbols and save gene expression matrix
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Link clinical and genetic data if trait is available; otherwise skip linking
+linked_data = None
+is_trait_available = ('trait_row' in locals()) and (trait_row is not None)
+
+if is_trait_available:
+    # Build clinical dataframe (in case not created previously)
+    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 using the specified rules
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Judge bias and drop biased demographics
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final validation and 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 genetic data; performed QC and bias checks."
+    )
+
+    # 6) Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # No trait available: skip linking, validate metadata using gene data only
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,  # Ignored since trait not available
+        df=normalized_gene_data.T,  # So sample_size can still be recorded
+        note="INFO: Trait not available in sample characteristics; saved normalized gene expression only."
+    )
+    # Do not save out_data_file when trait is unavailable

output/preprocess/Glioblastoma/code/GSE249289.py

@@ -0,0 +1,141 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE249289"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE249289"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Glioblastoma/GSE249289.csv"
+out_gene_data_file = "./output/z3/preprocess/Glioblastoma/gene_data/GSE249289.csv"
+out_clinical_data_file = "./output/z3/preprocess/Glioblastoma/clinical_data/GSE249289.csv"
+json_path = "./output/z3/preprocess/Glioblastoma/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  # Title and summary indicate gene expression profiling
+
+# 2) Variable availability and converters
+# From Sample Characteristics Dictionary:
+# 0: tissue: Brain (constant, not useful)
+# 1: Sex: Male/Female
+# 2: age: values
+# 3: tumorsphere IDs (irrelevant for current variables)
+# 4: culture platform (not the trait of interest)
+trait_row = None          # Trait (Glioblastoma) is constant across samples, thus not available for association
+age_row = 2
+gender_row = 1
+
+def convert_trait(x):
+    # Not used since trait_row is None; return None for safety
+    return None
+
+def convert_age(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    # Remove common age unit words
+    s = re.sub(r'\b(years?|yrs?|yo)\b', '', s, flags=re.IGNORECASE).strip()
+    m = re.search(r'(\d+(\.\d+)?)', s)
+    if not m:
+        return None
+    val = float(m.group(1))
+    # Prefer integer when appropriate
+    return int(val) if abs(val - int(val)) < 1e-6 else val
+
+def convert_gender(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    s_low = s.lower()
+    if s_low in {'male', 'm', 'man'}:
+        return 1
+    if s_low in {'female', 'f', 'woman'}:
+        return 0
+    return None
+
+# 3) Save metadata with initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify columns for probe IDs and gene symbols based on annotation preview:
+# Probe ID column: 'ID' matches probe identifiers like 'ILMN_1343291'
+# Gene symbol column: 'Symbol' contains gene symbols / descriptive strings from which symbols can be extracted
+
+# 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 expression
+probe_data = gene_data  # keep original probe-level data
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)

output/preprocess/Glioblastoma/code/GSE279426.py

@@ -0,0 +1,135 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE279426"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE279426"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Glioblastoma/GSE279426.csv"
+out_gene_data_file = "./output/z3/preprocess/Glioblastoma/gene_data/GSE279426.csv"
+out_clinical_data_file = "./output/z3/preprocess/Glioblastoma/clinical_data/GSE279426.csv"
+json_path = "./output/z3/preprocess/Glioblastoma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+from typing import Optional, Union
+
+# 1) Gene expression availability
+is_gene_available = True  # Series title/summary indicate mRNA expression data, not miRNA/methylation.
+
+# 2) Variable availability based on Sample Characteristics Dictionary:
+# Keys observed:
+# 0: name_in_pmid_21471286 (IDs)
+# 1: alternative_name (IDs)
+# 2: treatment_gefitinib (T0/T1/T2)
+# 3: type (human/xenograft)
+# 4: egfr_amplification (A0/A1)
+# 5: disease (GBM) [constant]
+trait_row = None   # No case/control or equivalent for "Glioblastoma"; disease is constant GBM.
+age_row = None     # No age field present.
+gender_row = None  # No gender field present.
+
+# 2.2) Data type conversion functions (defined for interface completeness; they will not be used since rows are None)
+def _after_colon(x: str) -> Optional[str]:
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        return parts[1].strip() if len(parts) == 2 else x.strip()
+    return None
+
+def convert_trait(x: str) -> Optional[int]:
+    """
+    Map disease or related indicators to binary if applicable:
+    - glioblastoma/gbm -> 1
+    - normal/control/non-disease -> 0
+    Unknowns -> None
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Common disease indicators
+    if any(k in vl for k in ["glioblastoma", "gbm"]):
+        return 1
+    if any(k in vl for k in ["normal", "control", "healthy", "non-disease", "non disease"]):
+        return 0
+    # If field is treatment (T0/T1/T2) or other irrelevant fields, we cannot infer trait robustly
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Extract number (years assumed if unspecified)
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except:
+        return None
+
+def convert_gender(x: str) -> Optional[int]:
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    # Standardize common gender representations
+    if vl in ["male", "m", "man"]:
+        return 1
+    if vl in ["female", "f", "woman", "women"]:
+        return 0
+    return None
+
+# 3) Save initial metadata (trait availability determined by trait_row is None)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+# Skipped because trait_row is None (no clinical trait data available for the target trait).
+# If trait_row were available, we would run:
+# 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)

output/preprocess/Glioblastoma/code/GSE39144.py

@@ -0,0 +1,228 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE39144"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE39144"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Glioblastoma/GSE39144.csv"
+out_gene_data_file = "./output/z3/preprocess/Glioblastoma/gene_data/GSE39144.csv"
+out_clinical_data_file = "./output/z3/preprocess/Glioblastoma/clinical_data/GSE39144.csv"
+json_path = "./output/z3/preprocess/Glioblastoma/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
+# Platform: Affymetrix Human Genome U133 Plus 2.0 Array (mRNA expression), so gene data is available.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# Choose keys (rows) for variables based on Sample Characteristics Dictionary
+# Trait (Glioblastoma): inferred from 'cell type: glioma-initiating cells ...' under key 0
+trait_row = 0
+
+# Age: age information is inconsistently embedded in 'source' text and not reliably available across samples
+age_row = None
+
+# Gender: dedicated gender row exists at key 2
+gender_row = 2
+
+# Conversion functions
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ''
+    s = str(x)
+    parts = s.split(':', 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    """
+    Map to binary: Glioblastoma (glioma-initiating cells / glioblastoma tissues) -> 1; others -> 0.
+    Heuristic: presence of keywords indicates glioblastoma-derived samples.
+    """
+    if x is None:
+        return None
+    val = _after_colon(x).lower()
+    if any(k in val for k in ['glioblastoma', 'glioma-initiating']):
+        return 1
+    # For this series, non-glioma entries include iPSC/ESC/NSC/tissues; treat as controls.
+    return 0
+
+def convert_age(x):
+    """
+    Not used (age_row=None). If needed, attempts to parse '36-year-old' as 36.
+    Gestational weeks or pooled/unknown will return None.
+    """
+    if x is None:
+        return None
+    val = _after_colon(x).lower()
+    # Parse patterns like '36-year-old'
+    m = re.search(r'(\d+)\s*-\s*year\s*-\s*old|(\d+)\s*year\s*old|(\d+)\s*years?\s*old', val)
+    if m:
+        # Extract the first non-None capturing group
+        for g in m.groups():
+            if g is not None:
+                try:
+                    return float(g)
+                except Exception:
+                    pass
+    # Do not convert gestational weeks or other formats
+    return None
+
+def convert_gender(x):
+    """
+    Map gender to binary: female -> 0, male -> 1.
+    Pooled or mixed sexes -> None.
+    """
+    if x is None:
+        return None
+    val = _after_colon(x).strip().lower()
+    if val in ['female']:
+        return 0
+    if val in ['male']:
+        return 1
+    if val in ['pooled male']:
+        return 1
+    if val in ['pooled female']:
+        return 0
+    if val in ['pooled', 'male and female', 'female and male']:
+        return None
+    # If unexpected text contains male/female exclusively
+    if 'male' in val and 'female' not in val:
+        return 1
+    if 'female' in val and 'male' not in val:
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save
+    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
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the appropriate columns for mapping based on the annotation preview:
+# - Probe identifiers: 'ID' (matches expression data index like '1007_s_at')
+# - Gene symbols: 'Gene Symbol'
+
+# 1-2. Build the mapping dataframe
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+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
+
+# Ensure clinical features are available in the session; reload from disk if necessary
+if 'selected_clinical_df' not in locals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols and save
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. 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. Judge trait/demographic bias and drop biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+# Cast to native Python bools to avoid JSON serialization issues
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available = bool((trait in selected_clinical_df.index) and bool(selected_clinical_df.loc[trait].notna().any()))
+
+note = "INFO: Age not available; gender partially missing/pooled; trait inferred from cell type keywords."
+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=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)

output/preprocess/Glioblastoma/code/TCGA.py

@@ -0,0 +1,269 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z3/preprocess/Glioblastoma/TCGA.csv"
+out_gene_data_file = "./output/z3/preprocess/Glioblastoma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z3/preprocess/Glioblastoma/clinical_data/TCGA.csv"
+json_path = "./output/z3/preprocess/Glioblastoma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# 1) Select the most specific cohort directory for Glioblastoma
+preferred_dirs = [
+    'TCGA_Glioblastoma_(GBM)',
+    'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Lower_Grade_Glioma_(LGG)'
+]
+
+available_dirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+selected_cohort_dir_name = None
+for d in preferred_dirs:
+    if d in available_dirs:
+        selected_cohort_dir_name = d
+        break
+
+if selected_cohort_dir_name is None:
+    print("No suitable TCGA subdirectory found for Glioblastoma. Skipping this trait.")
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_cohort_dir_name)
+
+    # 2) Identify clinical and genetic file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3) Load both files as DataFrames
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, compression='infer', low_memory=False)
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, compression='infer', low_memory=False)
+
+    # 4) Print clinical column names
+    print(list(clinical_df.columns))
+
+# Step 2: Find Candidate Demographic Features
+import os
+import pandas as pd
+
+# Locate the GBM cohort directory under the TCGA root
+cohort_dirs = [os.path.join(tcga_root_dir, d) for d in os.listdir(tcga_root_dir)
+               if os.path.isdir(os.path.join(tcga_root_dir, d)) and 'gbm' in d.lower()]
+if not cohort_dirs:
+    raise FileNotFoundError("Could not find a GBM cohort directory under tcga_root_dir.")
+cohort_dir = cohort_dirs[0]
+
+# Get clinical and genetic file paths
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load clinical data
+clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, dtype=str)
+
+# Identify candidate columns for age and gender
+cols_lower = {c: c.lower() for c in clinical_df.columns}
+candidate_age_cols = []
+candidate_gender_cols = []
+
+for c, cl in cols_lower.items():
+    # Age-related: include 'age' (but not 'stage') and 'birth'
+    if ('age' in cl and 'stage' not in cl) or ('birth' in cl):
+        candidate_age_cols.append(c)
+    # Gender-related: include 'gender' or 'sex'
+    if ('gender' in cl) or (cl == 'sex') or (cl.startswith('sex_')) or ('_sex' in cl):
+        candidate_gender_cols.append(c)
+
+# Print candidate lists in the required strict format
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Extract and preview candidate columns (first 5 values) as dictionaries
+if candidate_age_cols:
+    age_preview = preview_df(clinical_df[candidate_age_cols], n=5)
+    print(age_preview)
+
+if candidate_gender_cols:
+    gender_preview = preview_df(clinical_df[candidate_gender_cols], n=5)
+    print(gender_preview)
+
+# Step 3: Select Demographic Features
+import re
+import math
+
+# Helper functions
+def is_missing(v):
+    if v is None:
+        return True
+    if isinstance(v, float):
+        return math.isnan(v)
+    if isinstance(v, str):
+        return v.strip().lower() in ("", "na", "nan", "null", "none")
+    return False
+
+def find_preview_dict(candidate_keys):
+    best_dict = None
+    best_match_count = -1
+    for name, obj in globals().items():
+        if isinstance(obj, dict) and len(obj) > 0:
+            keys = list(obj.keys())
+            if all(isinstance(k, str) for k in keys) and all(isinstance(obj[k], list) for k in keys):
+                match_count = sum(1 for k in keys if k in candidate_keys)
+                if match_count > best_match_count and match_count > 0:
+                    best_match_count = match_count
+                    best_dict = obj
+    return best_dict
+
+def extract_number(s):
+    # Use same logic as tcga_convert_age if available; otherwise fallback
+    try:
+        return tcga_convert_age(s)
+    except Exception:
+        pass
+    m = re.search(r'\d+', str(s))
+    return int(m.group()) if m else None
+
+def choose_age_col(age_dict, candidate_cols):
+    if not age_dict:
+        return None
+    best_col = None
+    best_plausible = -1
+    best_present = -1
+    for col in candidate_cols:
+        if col not in age_dict:
+            continue
+        vals = age_dict[col]
+        nums = [extract_number(v) for v in vals if not is_missing(v)]
+        present = len(nums)
+        plausible = sum(1 for n in nums if n is not None and 0 < n <= 120)
+        # Prefer columns with more plausible ages; tie-breaker: more present values
+        if plausible > best_plausible or (plausible == best_plausible and present > best_present):
+            best_plausible = plausible
+            best_present = present
+            best_col = col
+    # Require at least moderate data quality in the preview
+    if best_col is None:
+        return None
+    if best_plausible < 3 and best_present < 3:
+        return None
+    return best_col
+
+def choose_gender_col(gender_dict, candidate_cols):
+    if not gender_dict:
+        return None
+    best_col = None
+    best_valid = -1
+    for col in candidate_cols:
+        if col not in gender_dict:
+            continue
+        vals = gender_dict[col]
+        valid = 0
+        present = 0
+        for v in vals:
+            if is_missing(v):
+                continue
+            present += 1
+            s = str(v).strip().lower()
+            if s in ("female", "male", "f", "m"):
+                valid += 1
+        # Prefer more valid recognized entries
+        if valid > best_valid:
+            best_valid = valid
+            best_col = col
+    if best_col is None:
+        return None
+    # Require at least 3 recognized values in preview
+    if best_valid < 3:
+        return None
+    return best_col
+
+# Retrieve the preview dictionaries produced in prior steps
+age_preview_dict = find_preview_dict(candidate_age_cols) if 'candidate_age_cols' in globals() else None
+gender_preview_dict = find_preview_dict(candidate_gender_cols) if 'candidate_gender_cols' in globals() else None
+
+# Select columns
+age_col = choose_age_col(age_preview_dict, candidate_age_cols) if age_preview_dict else None
+gender_col = choose_gender_col(gender_preview_dict, candidate_gender_cols) if gender_preview_dict else None
+
+# Explicitly print chosen information
+print("Chosen age_col:", age_col)
+if age_col and age_preview_dict:
+    print("Sample values for age_col:", age_preview_dict.get(age_col))
+
+print("Chosen gender_col:", gender_col)
+if gender_col and gender_preview_dict:
+    print("Sample values for gender_col:", gender_preview_dict.get(gender_col))
+
+# Step 4: Feature Engineering and Validation
+import os
+import pandas as pd
+
+# 1) Extract and standardize clinical features (trait, optional age, gender)
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df,
+    trait=trait,
+    age_col=age_col if 'age_col' in globals() else None,
+    gender_col=gender_col if 'gender_col' in globals() else None
+)
+
+# Optionally save standardized clinical features for reference
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+selected_clinical_df.to_csv(out_clinical_data_file)
+
+# 2) Normalize gene symbols and save normalized gene expression
+genetic_df_numeric = genetic_df.apply(pd.to_numeric, errors='coerce')
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df_numeric.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 on sample IDs
+common_samples = selected_clinical_df.index.intersection(normalized_gene_df.columns)
+linked_data = pd.concat(
+    [selected_clinical_df.loc[common_samples], normalized_gene_df[common_samples].T],
+    axis=1
+)
+
+# 4) Handle missing values systematically
+processed_df = handle_missing_values(linked_data, trait_col=trait)
+
+# 5) Determine bias in trait and demographic features; drop biased demographics
+trait_biased, processed_df = judge_and_remove_biased_features(processed_df, trait=trait)
+
+# 6) Final validation and save cohort info
+cohort_name = os.path.basename(cohort_dir) if 'cohort_dir' in globals() else (
+    selected_cohort_dir_name if 'selected_cohort_dir_name' in globals() else 'TCGA_Glioblastoma_(GBM)'
+)
+
+# Cast to native Python bool to avoid numpy.bool_ serialization issues
+is_gene_available = bool((normalized_gene_df.shape[0] > 0) and (normalized_gene_df.shape[1] > 0))
+is_trait_available = bool(selected_clinical_df[trait].notna().any())
+trait_biased = bool(trait_biased)
+
+covariates_present = [c for c in ['Age', 'Gender'] if c in processed_df.columns]
+gene_cols_in_processed = [c for c in processed_df.columns if c not in [trait, 'Age', 'Gender']]
+note = (
+    f"INFO: Cohort={cohort_name}; age_col={age_col if 'age_col' in globals() else None}; "
+    f"gender_col={gender_col if 'gender_col' in globals() else None}; "
+    f"linked_samples={len(common_samples)}; final_samples={len(processed_df)}; "
+    f"covariates={covariates_present}; final_gene_count={len(gene_cols_in_processed)}."
+)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=str(cohort_name),
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=trait_biased,
+    df=processed_df,
+    note=str(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/Glucocorticoid_Sensitivity/clinical_data/GSE32962.csv

@@ -1,3 +1,2 @@
-GSM816393,GSM816394,GSM816395,GSM816396,GSM816397,GSM816398,GSM816399,GSM816400,GSM816401,GSM816402,GSM816403,GSM816404,GSM816405,GSM816406,GSM816407,GSM816408,GSM816409,GSM816410,GSM816411,GSM816412,GSM816413,GSM816414,GSM816415,GSM816416,GSM816417,GSM816418,GSM816419,GSM816420,GSM816421,GSM816422,GSM816423,GSM816424,GSM816425,GSM816426,GSM816427,GSM816428,GSM816429,GSM816430,GSM816431,GSM816432,GSM816433,GSM816434,GSM816435
-1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
+,GSM816393,GSM816394,GSM816395,GSM816396,GSM816397,GSM816398,GSM816399,GSM816400,GSM816401,GSM816402,GSM816403,GSM816404,GSM816405,GSM816406,GSM816407,GSM816408,GSM816409,GSM816410,GSM816411,GSM816412,GSM816413,GSM816414,GSM816415,GSM816416,GSM816417,GSM816418,GSM816419,GSM816420,GSM816421,GSM816422,GSM816423,GSM816424,GSM816425,GSM816426,GSM816427,GSM816428,GSM816429,GSM816430,GSM816431,GSM816432,GSM816433,GSM816434,GSM816435
+Glucocorticoid_Sensitivity,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0