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p3/preprocess/Acute_Myeloid_Leukemia/GSE222124.csv

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p3/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE121291.csv

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p3/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE121431.csv

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p3/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE161532.csv

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p3/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE222169.csv

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p3/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE98578.csv

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p3/preprocess/Acute_Myeloid_Leukemia/clinical_data/GSE99612.csv

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p3/preprocess/Acute_Myeloid_Leukemia/code/GSE121291.py

@@ -0,0 +1,127 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE121291"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE121291"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE121291.csv"
+out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE121291.csv"
+out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE121291.csv"
+json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes - the dataset contains microarray mRNA data according to series title
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# All samples are AML cell line - constant trait value
+trait_row = None
+
+# No age data available 
+age_row = None
+
+# No gender data available since this is cell line data
+gender_row = None
+
+def convert_trait(x):
+    # Convert AML status to binary
+    # If contains "Acute Myeloid Leukemia", return 1
+    if isinstance(x, str) and "Acute Myeloid Leukemia" in x:
+        return 1
+    return None
+
+def convert_age(x):
+    pass
+
+def convert_gender(x):
+    pass
+
+# 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. Skip clinical feature extraction since trait_row is None
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+# The gene identifiers look like Affymetrix probe IDs (format: digit+_at/s_at/x_at)
+# These need to be mapped to gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# 1. Observe columns - 'ID' for probe identifiers matching gene expression data, 'Gene Symbol' for gene symbols
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get mapping between probe IDs and gene symbols
+mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply mapping to convert probe expression to gene expression
+gene_data = apply_gene_mapping(gene_data, mapping)
+
+# Preview the first few gene IDs to verify the mapping worked
+print("First 20 mapped gene symbols:")
+print(gene_data.index[:20])
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Create minimal linked data with just gene expression data
+linked_data = gene_data.T 
+
+# Add trait column initialized to None
+linked_data[trait] = None
+
+# Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# Check for biased features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Validate data quality and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data from cell lines but lacks AML trait information needed for analysis."
+)

p3/preprocess/Acute_Myeloid_Leukemia/code/GSE121431.py

@@ -0,0 +1,139 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE121431"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE121431"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE121431.csv"
+out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE121431.csv"
+out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE121431.csv"
+json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background info, this appears to be gene expression data of AML cell lines
+
+# 2. Variable Availability and Data Type Conversion
+# For trait: Can infer from Feature 0 (disease state)
+trait_row = 0
+
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split(': ')[-1]
+    if 'acute myeloid leukemia' in value or 'aml' in value:
+        return 1
+    return None
+
+# For age: Not available as this is cell line data
+age_row = None
+
+def convert_age(value):
+    return None
+
+# For gender: Not available as this is cell line data 
+gender_row = None
+
+def convert_gender(value):
+    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
+clinical_df = geo_select_clinical_features(
+    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_result = preview_df(clinical_df)
+print("Preview of clinical data:", preview_result)
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+# These are probe IDs from Affymetrix arrays that need to be mapped to gene symbols
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# 1. & 2. Extract gene mapping from annotation
+# ID column matches probe identifiers in gene expression data
+# Gene Symbol column contains the corresponding gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values systematically 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save metadata
+# Note: Dataset contains gene expression data from AML cell lines. The trait "Acute_Myeloid_Leukemia" is defined 
+# based on cell subtypes (AMKL vs non-AMKL).
+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_biased,
+    df=linked_data,
+    note="Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Acute_Myeloid_Leukemia/code/GSE161532.py

@@ -0,0 +1,156 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE161532"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE161532"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE161532.csv"
+out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE161532.csv"
+out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE161532.csv"
+json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes - uses Affymetrix Human Transcriptome Array 2.0 for gene expression profiling
+is_gene_available = True
+
+# 2.1 Data Availability
+# trait - disease state from feature 4 contains AML status
+trait_row = 4
+# age - age data available in feature 1 
+age_row = 1
+# gender - gender data available in feature 2
+gender_row = 2
+
+# 2.2 Data Type Conversion Functions 
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    # All samples have AML, but we can distinguish primary vs secondary
+    if "de novo" in x.lower():
+        return 0  # Primary AML
+    elif any(t in x.lower() for t in ["secondary", "t-aml"]):
+        return 1  # Secondary AML
+    elif "aml" in x.lower():
+        return None  # AML but type unknown
+    return None
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        age = float(x.split(":")[1].strip())
+        return age
+    except:
+        return None
+        
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    x = x.lower()
+    if "female" in x:
+        return 0
+    elif "male" in x:
+        return 1
+    return None
+
+# 3. 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=trait_row is not None)
+
+# 4. Extract clinical features since trait_row is available
+clinical_df = geo_select_clinical_features(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 clinical data
+print("Preview of clinical data:")
+print(preview_df(clinical_df))
+
+# Save clinical data
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+# These identifiers appear to be microarray probe IDs from Agilent platform ending in "_st"
+# They are not standard human gene symbols and will need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Looking at the gene expression data from step 3, the IDs end with "_st"
+# Looking at the annotation data, the gene_assignment column contains gene names/symbols
+# However, we need to extract the symbols from the complex assignments
+
+# Extract probe IDs and gene assignments, and get mapping dataframe
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')
+
+# Apply mapping to get gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values systematically 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save metadata
+# Note: Dataset contains gene expression data from AML cell lines. The trait "Acute_Myeloid_Leukemia" is defined 
+# based on cell subtypes (AMKL vs non-AMKL).
+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_biased,
+    df=linked_data,
+    note="Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Acute_Myeloid_Leukemia/code/GSE222124.py

@@ -0,0 +1,137 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE222124"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE222124"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE222124.csv"
+out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE222124.csv"
+out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE222124.csv"
+json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene expression data availability 
+is_gene_available = True  # This is expression data from cell lines, not miRNA/methylation
+
+# 2.1 Data availability
+# Row 0 shows leukemia cell lines, can be used for trait
+trait_row = 0
+
+# Age and gender not provided for cell lines
+age_row = None  
+gender_row = None
+
+# 2.2 Data type conversion functions
+def convert_trait(value):
+    if not value or ':' not in value:
+        return None
+    cell_type = value.split(': ')[1].lower()
+    if 'monocytic leukemia' in cell_type:
+        return 1  # Acute monocytic leukemia
+    elif 't cell leukemia' in cell_type:
+        return 0  # Other leukemia types
+    elif 'natural killer cell leukemia' in cell_type:
+        return 0
+    return None
+
+def convert_age(value):
+    pass  # Not used since age_row is None
+
+def convert_gender(value): 
+    pass  # Not used since gender_row is 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:
+    clinical_features = 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 extracted clinical features:")
+    print(preview_df(clinical_features))
+    
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Extract probe-to-gene mapping from annotation
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values systematically 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save metadata
+# Note: Dataset contains gene expression data from AML cell lines. The trait "Acute_Myeloid_Leukemia" is defined 
+# based on cell subtypes (AMKL vs non-AMKL).
+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_biased,
+    df=linked_data,
+    note="Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Acute_Myeloid_Leukemia/code/GSE222169.py

@@ -0,0 +1,177 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE222169"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE222169"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE222169.csv"
+out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE222169.csv"
+out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE222169.csv"
+json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Given information about leukemia cell lines suggests this is likely gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Row identification
+trait_row = 0  # Contains AML information in 'cell line' and 'tissue source' fields
+age_row = None  # Age information not available
+gender_row = None  # Gender information not available
+
+# 2.2 Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert trait values to binary: 1 for AML cases"""
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[-1].lower()
+    # Both cell lines and patient samples are AML cases
+    if 'aml' in value or 'molm-14' in value or 'oci-aml2' in value:
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Placeholder function - age data not available"""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Placeholder function - 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 = 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 processed clinical data
+    print("Preview of processed clinical data:")
+    print(preview_df(selected_clinical))
+    
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+# The identifiers appear to be transcript cluster IDs from Affymetrix Clariom D arrays
+# They need to be mapped to human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Looking at the example values in SPOT_ID.1, we can find gene symbols within parentheses
+# followed by ']', like '(OR4F5)', '(SAMD11)', etc.
+# Create a custom function to extract gene symbols from complex text descriptions
+def extract_gene_symbols_from_desc(text):
+    """Extract gene symbols from complex annotation text that follows format:
+    ... (SYMBOL) [gene_biotype ...
+    """
+    if pd.isna(text):
+        return []
+    # Split on '//' to get separate entries and look for gene symbol pattern
+    # Gene symbols typically appear in parentheses before [gene_biotype 
+    symbols = []
+    entries = text.split('//')
+    for entry in entries:
+        # Look for text in parentheses followed by [gene_biotype
+        match = re.search(r'\(([^)]+)\)\s*\[gene_biotype', entry)
+        if match:
+            symbol = match.group(1)
+            # Some entries have additional text like "(Drosophila)" - remove that
+            symbol = re.sub(r'\s*\([^)]+\)$', '', symbol)
+            symbols.append(symbol)
+    return list(set(symbols))  # Remove duplicates
+
+# Add a column with extracted gene symbols
+gene_annotation['Gene'] = gene_annotation['SPOT_ID.1'].apply(extract_gene_symbols_from_desc)
+
+# Get mapping between IDs and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene')
+
+# Apply gene mapping to convert probe-level data to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Get clinical data from previous step
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=0,  # Using first row containing cell line info
+    convert_trait=convert_trait,  # Using previously defined convert_trait function
+    age_row=None,
+    convert_age=None, 
+    gender_row=None,
+    convert_gender=None
+)
+
+# 2. Link clinical and genetic data 
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Gene expression data comparing different AML cell lines and treatments."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Acute_Myeloid_Leukemia/code/GSE222616.py

@@ -0,0 +1,120 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE222616"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE222616"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE222616.csv"
+out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE222616.csv"
+out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE222616.csv"
+json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes - it uses HuGene 1.0 ST Affymetrix arrays for gene expression profiling
+is_gene_available = True
+
+# 2.1 Data Availability
+# trait (AML status) is constant since all samples are from HL-60 AML cell line
+trait_row = None 
+
+# Age is not available for cell line data
+age_row = None
+
+# Gender is not available for cell line data
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    # Not needed since trait data is not available
+    return None
+
+def convert_age(value):
+    # Not needed since age data is not available
+    return None
+
+def convert_gender(value):
+    # Not needed since gender data is 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
+# Skip since trait_row is None (no clinical data available)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+# Based on biomedical review: The identifiers appear to be numerical probe IDs from an array platform 
+# rather than standardized human gene symbols. These would need to be mapped to gene symbols.
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Extract gene mapping information
+probe_col = "ID"
+gene_col = "gene_assignment"
+mapping_data = gene_annotation[[probe_col, gene_col]]
+mapping_data = mapping_data.dropna()
+mapping_data = mapping_data.rename(columns={probe_col: 'ID', gene_col: 'Gene'})
+mapping_data['Gene'] = mapping_data['Gene'].apply(extract_human_gene_symbols)
+
+# Convert probe-level data to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Normalize gene symbols to handle synonyms
+gene_data = normalize_gene_symbols_in_index(gene_data)
+# 1. Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# Since no clinical data is available, use gene data as final dataset
+# Set is_biased=False since we cannot assess bias without trait data
+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,  # Cannot be biased without trait data
+    df=gene_data,
+    note="Only gene expression data available, no clinical information found"
+)
+
+# Save gene data as final data since no clinical data to link
+if is_usable:
+    gene_data.to_csv(out_data_file)

p3/preprocess/Acute_Myeloid_Leukemia/code/GSE235070.py

@@ -0,0 +1,85 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE235070"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE235070"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE235070.csv"
+out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE235070.csv"
+out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE235070.csv"
+json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on the background info, this appears to be a SuperSeries about AML patients
+# Cannot determine if it contains gene expression data based on limited info
+is_gene_available = False
+
+# 2.1 Data Availability
+# From sample characteristics:
+# Row 0 contains disease state info which indicates AML trait
+trait_row = 0 
+
+# Age and gender info not available
+age_row = None
+gender_row = None 
+
+# 2.2. Data Type Conversion Functions
+def convert_trait(x):
+    # Extract value after colon and convert to binary
+    # 'patient with AML' indicates positive case (1)
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[-1].strip().lower()
+    if 'aml' in value:
+        return 1
+    return None
+
+convert_age = None
+convert_gender = 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
+# Since trait_row is not None, we extract clinical features
+clinical_df = geo_select_clinical_features(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 clinical data
+print(preview_df(clinical_df))
+clinical_df.to_csv(out_clinical_data_file)

p3/preprocess/Acute_Myeloid_Leukemia/code/GSE249638.py

@@ -0,0 +1,142 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE249638"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE249638"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE249638.csv"
+out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE249638.csv"
+out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE249638.csv"
+json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes - this is a transcriptomic profiling study of CD4+ T cells
+is_gene_available = True
+
+# 2.1 Data Availability & 2.2 Data Type Conversion
+# Trait (AML status) is available in Feature 1, using binary type
+trait_row = 1
+def convert_trait(x):
+    if not x or ':' not in x:
+        return None
+    value = x.split(':')[1].strip().lower()
+    if 'acute myeloid leukemia' in value:
+        return 1
+    elif 'healthy control' in value:
+        return 0
+    return None
+
+# Age not available
+age_row = None
+convert_age = None
+
+# Gender not available  
+gender_row = None
+convert_gender = 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:
+    clinical_features = 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 extracted features
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+# The identifiers like '2824546_st' are probe IDs from Affymetrix microarray platform, not human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# 2. Get mapping between probe IDs and gene symbols
+gene_annotation = gene_annotation.drop('ID', axis=1)  # Drop the original ID column 
+gene_annotation = gene_annotation.rename(columns={'probeset_id': 'ID'})
+mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 3. Apply the mapping to convert probe-level measurements to gene expression data 
+gene_data = apply_gene_mapping(gene_data, mapping)
+
+# Preview first few genes and their expression values
+print("\nPreview of mapped gene expression data:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values systematically 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save metadata
+# Note: Dataset contains gene expression data from AML cell lines. The trait "Acute_Myeloid_Leukemia" is defined 
+# based on cell subtypes (AMKL vs non-AMKL).
+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_biased,
+    df=linked_data,
+    note="Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Acute_Myeloid_Leukemia/code/GSE98578.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE98578"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE98578"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE98578.csv"
+out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE98578.csv"
+out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE98578.csv"
+json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+is_gene_available = True  # microarray gene expression data mentioned in summary
+
+# 2.1 Data Availability
+trait_row = 2  # cell subtype indicates AML type
+age_row = None  # no age data
+gender_row = None  # no gender data
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].strip()
+    # Convert to binary: AMKL=1, non-AMKL=0
+    if value == 'AMKL':
+        return 1
+    elif value == 'non-AMKL': 
+        return 0
+    return None
+
+# Age and gender conversion functions not needed since data unavailable
+convert_age = None
+convert_gender = None
+
+# 3. Save metadata about data availability
+is_initial = 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. Extract clinical features
+if trait_row is not None:
+    clinical_features = 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 extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+# These identifiers are from Affymetrix HG-U133 Plus 2.0 array probe IDs
+# They need to be mapped to human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Get gene mapping from annotation data
+# 'ID' column matches probe IDs in gene expression data
+# 'Gene Symbol' column contains the target gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Preview first few genes and samples
+print("Preview of gene expression data after mapping:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values systematically 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save metadata
+# Note: Dataset contains gene expression data from AML cell lines. The trait "Acute_Myeloid_Leukemia" is defined 
+# based on cell subtypes (AMKL vs non-AMKL).
+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_biased,
+    df=linked_data,
+    note="Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Acute_Myeloid_Leukemia/code/GSE99612.py

@@ -0,0 +1,110 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+cohort = "GSE99612"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
+in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE99612"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE99612.csv"
+out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE99612.csv"
+out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE99612.csv"
+json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data - Not a miRNA or methylation study
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# This is cell line data, not human subject data
+trait_row = None
+
+def convert_trait(x):
+    return None
+
+age_row = None
+def convert_age(x):
+    return None
+
+gender_row = None
+def convert_gender(x):
+    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. Skip clinical feature extraction since trait_row is None
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# 1. Identify relevant columns for gene mapping
+# 'ID' in gene annotation matches identifiers in gene expression data
+# 'gene_assignment' contains gene symbol information
+
+# 2. Extract gene mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')
+
+# 3. Apply gene mapping to convert probe data to gene expression data 
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Create minimal linked data structure 
+linked_data = pd.DataFrame(index=gene_data.columns)
+
+# 3-4. Skip missing value handling since data is not usable
+# Mark as biased since we have no trait data
+is_biased = True
+
+# 5. Final validation and save metadata
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=is_biased,
+    df=linked_data,
+    note="This is a cell line experiment, not a human subject study. Contains no trait data."
+)
+
+# 6. Skip saving linked data since it's not usable

p3/preprocess/Acute_Myeloid_Leukemia/code/TCGA.py

@@ -0,0 +1,147 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Acute_Myeloid_Leukemia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
+
+# 1. Select the relevant subdirectory for acute myeloid leukemia
+subdirectory = 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+cohort_dir = os.path.join(tcga_root_dir, subdirectory)
+
+# 2. Get the file paths 
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# 3. Load the data files
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+# 4. Print clinical data columns
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+# Identify candidate columns
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
+candidate_gender_cols = ['gender']
+
+# Use TCGA project code LAML instead of full trait name
+cohort_dir = os.path.join(tcga_root_dir, "LAML")
+if not os.path.exists(cohort_dir):
+    print(f"Error: Directory not found: {cohort_dir}")
+    print("Please verify the data directory structure and path configuration.")
+else:
+    clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0)
+    
+    # Preview age columns 
+    age_preview = {}
+    for col in candidate_age_cols:
+        age_preview[col] = clinical_df[col].head(5).tolist()
+    print("Age columns preview:", age_preview)
+    
+    # Preview gender columns
+    gender_preview = {}
+    for col in candidate_gender_cols:
+        gender_preview[col] = clinical_df[col].head(5).tolist()
+    print("\nGender columns preview:", gender_preview)
+# Build the cohort directory path
+cohort_dir = os.path.join(tcga_root_dir, "LAML")
+
+# Get the clinical file path
+clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+
+# Read clinical data
+clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)
+
+# Default to None 
+age_col = None 
+gender_col = None
+
+# Search for age column - look for common patterns
+age_candidates = [col for col in clinical_df.columns if 'age' in col.lower()]
+if age_candidates:
+    # Preview first few values of each candidate
+    for col in age_candidates:
+        preview = clinical_df[col].head()
+        # Check if column has numeric age values after conversion
+        converted = preview.apply(tcga_convert_age)
+        if not converted.isna().all():
+            age_col = col
+            break
+
+# Search for gender column - look for common patterns  
+gender_candidates = [col for col in clinical_df.columns if 'gender' in col.lower() or 'sex' in col.lower()]
+if gender_candidates:
+    # Preview first few values of each candidate
+    for col in gender_candidates:
+        preview = clinical_df[col].head()
+        # Check if column has valid gender values after conversion
+        converted = preview.apply(tcga_convert_gender)
+        if not converted.isna().all():
+            gender_col = col
+            break
+
+# Print chosen columns
+print(f"Selected age column: {age_col}")
+print(f"Selected gender column: {gender_col}")
+# 1. Select the relevant subdirectory for acute myeloid leukemia
+subdirectory = 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+cohort_dir = os.path.join(tcga_root_dir, subdirectory)
+
+# 2. Get the file paths 
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# 3. Load the data files
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+# 4. Print clinical data columns
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+# 1. Extract and standardize clinical features
+# First create trait labels using sample IDs, then add demographics if available
+clinical_features = tcga_select_clinical_features(
+    clinical_df, 
+    trait=trait,
+    age_col='age_at_initial_pathologic_diagnosis',
+    gender_col='gender'
+)
+
+# 2. Normalize gene symbols and save
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+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
+linked_data = pd.concat([clinical_features, normalized_gene_df.T], axis=1)
+
+# 4. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Validate data quality and save cohort info
+note = "Contains molecular data from tumor and normal samples with patient demographics."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 7. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Acute_Myeloid_Leukemia/cohort_info.json

@@ -0,0 +1 @@
+{"GSE99612": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "This is a cell line experiment, not a human subject study. Contains no trait data."}, "GSE98578": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 48, "note": "Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."}, "GSE249638": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 37, "note": "Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."}, "GSE235070": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE222616": {"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": "Only gene expression data available, no clinical information found"}, "GSE222169": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Gene expression data comparing different AML cell lines and treatments."}, "GSE222124": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 70, "note": "Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."}, "GSE161532": {"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": 53, "note": "Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."}, "GSE121431": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 30, "note": "Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."}, "GSE121291": {"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": "Dataset contains gene expression data from cell lines but lacks AML trait information needed for analysis."}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 173, "note": "Contains molecular data from tumor and normal samples with patient demographics."}}

p3/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE222169.csv

@@ -0,0 +1 @@
+Gene,GSM6916956,GSM6916957,GSM6916958,GSM6916959,GSM6916960,GSM6916961,GSM6916962,GSM6916963,GSM6916964,GSM6916965,GSM6916966,GSM6916967,GSM6916968,GSM6916969,GSM6916970,GSM6916971,GSM6916972,GSM6916973,GSM6916974,GSM6916975,GSM6916976,GSM6916977,GSM6916978,GSM6916979,GSM6916980,GSM6916981,GSM6916982,GSM6916983,GSM6916984,GSM6916985,GSM6916986,GSM6916987,GSM6916988,GSM6916989,GSM6916990,GSM6916991,GSM6916992,GSM6916993,GSM6916994,GSM6916995,GSM6916996,GSM6916997

p3/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE222616.csv

@@ -0,0 +1 @@
+Gene,GSM6927801,GSM6927802,GSM6927803,GSM6927804,GSM6927805,GSM6927806,GSM6927807,GSM6927808,GSM6927809,GSM6927810,GSM6927811,GSM6927812,GSM6927813,GSM6927814,GSM6927815,GSM6927816,GSM6927817,GSM6927818,GSM6927819,GSM6927820,GSM6927821,GSM6927822,GSM6927823,GSM6927824,GSM6927825,GSM6927826,GSM6927827,GSM6927828,GSM6927829,GSM6927830,GSM6927831,GSM6927832,GSM6927833,GSM6927834,GSM6927835,GSM6927836,GSM6927837,GSM6927838,GSM6927839,GSM6927840,GSM6927841,GSM6927842

p3/preprocess/Adrenocortical_Cancer/clinical_data/GSE108088.csv

@@ -0,0 +1,2 @@
+,GSM2889381,GSM2889382,GSM2889383,GSM2889384,GSM2889385,GSM2889386,GSM2889387,GSM2889388,GSM2889389,GSM2889390,GSM2889391,GSM2889392,GSM2889393,GSM2889394,GSM2889395,GSM2889396,GSM2889397,GSM2889398,GSM2889399,GSM2889400,GSM2889401,GSM2889402,GSM2889403,GSM2889404,GSM2889405,GSM2889406,GSM2889407,GSM2889408,GSM2889409,GSM2889410,GSM2889411,GSM2889412,GSM2889413,GSM2889414,GSM2889415,GSM2889416,GSM2889417,GSM2889418,GSM2889419,GSM2889420,GSM2889421,GSM2889422,GSM2889423
+Adrenocortical_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Adrenocortical_Cancer/clinical_data/GSE143383.csv

@@ -0,0 +1,3 @@
+,GSM4258059,GSM4258060,GSM4258061,GSM4258062,GSM4258063,GSM4258064,GSM4258065,GSM4258066,GSM4258067,GSM4258068,GSM4258069,GSM4258070,GSM4258071,GSM4258072,GSM4258073,GSM4258074,GSM4258075,GSM4258076,GSM4258077,GSM4258078,GSM4258079,GSM4258080,GSM4258081,GSM4258082,GSM4258083,GSM4258084,GSM4258085,GSM4258086,GSM4258087,GSM4258088,GSM4258089,GSM4258090,GSM4258091,GSM4258092,GSM4258093,GSM4258094,GSM4258095,GSM4258096,GSM4258097,GSM4258098,GSM4258099,GSM4258100,GSM4258101,GSM4258102,GSM4258103,GSM4258104,GSM4258105,GSM4258106,GSM4258107,GSM4258108,GSM4258109,GSM4258110,GSM4258111,GSM4258112,GSM4258113,GSM4258114,GSM4258115,GSM4258116,GSM4258117,GSM4258118,GSM4258119,GSM4258120,GSM4258121
+Adrenocortical_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Gender,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0

p3/preprocess/Adrenocortical_Cancer/clinical_data/GSE19776.csv

@@ -0,0 +1,4 @@
+,GSM493903,GSM493904,GSM493905,GSM493906,GSM493907,GSM493908,GSM493909,GSM493910,GSM493911,GSM493912,GSM493913,GSM493914,GSM493915,GSM493916,GSM493917
+Adrenocortical_Cancer,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,,0.0,,,0.0
+Age,67.8,72.1,26.7,36.9,,53.2,37.0,54.2,67.3,27.7,,58.0,42.0,46.0,38.0
+Gender,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0

p3/preprocess/Adrenocortical_Cancer/clinical_data/GSE49278.csv

@@ -0,0 +1,4 @@
+,GSM1196511,GSM1196512,GSM1196513,GSM1196514,GSM1196515,GSM1196516,GSM1196517,GSM1196518,GSM1196519,GSM1196520,GSM1196521,GSM1196522,GSM1196523,GSM1196524,GSM1196525,GSM1196526,GSM1196527,GSM1196528,GSM1196529,GSM1196530,GSM1196531,GSM1196532,GSM1196533,GSM1196534,GSM1196535,GSM1196536,GSM1196537,GSM1196538,GSM1196539,GSM1196540,GSM1196541,GSM1196542,GSM1196543,GSM1196544,GSM1196545,GSM1196546,GSM1196547,GSM1196548,GSM1196549,GSM1196550,GSM1196551,GSM1196552,GSM1196553,GSM1196554
+Adrenocortical_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Age,70.0,26.0,53.0,73.0,15.0,51.0,63.0,26.0,29.0,79.0,45.0,43.0,53.0,45.0,41.0,37.0,81.0,68.0,42.0,59.0,39.0,25.0,41.0,36.0,24.0,49.0,75.0,37.0,26.0,48.0,15.0,49.0,54.0,39.0,79.0,28.0,40.0,44.0,28.0,53.0,28.0,52.0,30.0,46.0
+Gender,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0

p3/preprocess/Adrenocortical_Cancer/clinical_data/GSE68606.csv

@@ -0,0 +1,4 @@
+,GSM1676864,GSM1676865,GSM1676866,GSM1676867,GSM1676868,GSM1676869,GSM1676870,GSM1676871,GSM1676872,GSM1676873,GSM1676874,GSM1676875,GSM1676876,GSM1676877,GSM1676878,GSM1676879,GSM1676880,GSM1676881,GSM1676882,GSM1676883,GSM1676884,GSM1676885,GSM1676886,GSM1676887,GSM1676888,GSM1676889,GSM1676890,GSM1676891,GSM1676892,GSM1676893,GSM1676894,GSM1676895,GSM1676896,GSM1676897,GSM1676898,GSM1676899,GSM1676900,GSM1676901,GSM1676902,GSM1676903,GSM1676904,GSM1676905,GSM1676906,GSM1676907,GSM1676908,GSM1676909,GSM1676910,GSM1676911,GSM1676912,GSM1676913,GSM1676914,GSM1676915,GSM1676916,GSM1676917,GSM1676918,GSM1676919,GSM1676920,GSM1676921,GSM1676922,GSM1676923,GSM1676924,GSM1676925,GSM1676926,GSM1676927,GSM1676928,GSM1676929,GSM1676930,GSM1676931,GSM1676932,GSM1676933,GSM1676934,GSM1676935,GSM1676936,GSM1676937,GSM1676938,GSM1676939,GSM1676940,GSM1676941,GSM1676942,GSM1676943,GSM1676944,GSM1676945,GSM1676946,GSM1676947,GSM1676948,GSM1676949,GSM1676950,GSM1676951,GSM1676952,GSM1676953,GSM1676954,GSM1676955,GSM1676956,GSM1676957,GSM1676958,GSM1676959,GSM1676960,GSM1676961,GSM1676962,GSM1676963,GSM1676964,GSM1676965,GSM1676966,GSM1676967,GSM1676968,GSM1676969,GSM1676970,GSM1676971,GSM1676972,GSM1676973,GSM1676974,GSM1676975,GSM1676976,GSM1676977,GSM1676978,GSM1676979,GSM1676980,GSM1676981,GSM1676982,GSM1676983,GSM1676984,GSM1676985,GSM1676986,GSM1676987,GSM1676988,GSM1676989,GSM1676990,GSM1676991,GSM1676992,GSM1676993,GSM1676994,GSM1676995,GSM1676996,GSM1676997,GSM1676998,GSM1676999,GSM1677000
+Adrenocortical_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,,,,,,,,,,,67.0,66.0,72.0,56.0,48.0,,,,,,,,,,,,,,,,,,,,,,,,48.0,,,66.0,56.0,72.0,,67.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67.0,56.0,48.0,,,,,,,,,66.0,72.0,,,,,,,,,,67.0,56.0,,66.0,,,48.0,,72.0,,,,,,,,,,,,,,,,,,,,,
+Gender,,,0.0,,,,,,,,1.0,1.0,1.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.0,,,1.0,0.0,1.0,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,,,,,,,,,1.0,1.0,,,,,,,,,,1.0,0.0,,1.0,,,0.0,,1.0,,,,,,,,,,,,,,,,,,,,,

p3/preprocess/Adrenocortical_Cancer/clinical_data/GSE68950.csv

@@ -0,0 +1,2 @@
+,3
+Adrenocortical_Cancer,0.0

p3/preprocess/Adrenocortical_Cancer/clinical_data/GSE75415.csv

@@ -0,0 +1,3 @@
+,GSM1954726,GSM1954727,GSM1954728,GSM1954729,GSM1954730,GSM1954731,GSM1954732,GSM1954733,GSM1954734,GSM1954735,GSM1954736,GSM1954737,GSM1954738,GSM1954739,GSM1954740,GSM1954741,GSM1954742,GSM1954743,GSM1954744,GSM1954745,GSM1954746,GSM1954747,GSM1954748,GSM1954749,GSM1954750,GSM1954751,GSM1954752,GSM1954753,GSM1954754,GSM1954755,GSM1954756
+Adrenocortical_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Gender,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,,,,,,,,

p3/preprocess/Adrenocortical_Cancer/clinical_data/GSE76019.csv

@@ -0,0 +1,2 @@
+,GSM1972883,GSM1972884,GSM1972885,GSM1972886,GSM1972887,GSM1972888,GSM1972889,GSM1972890,GSM1972891,GSM1972892,GSM1972893,GSM1972894,GSM1972895,GSM1972896,GSM1972897,GSM1972898,GSM1972899,GSM1972900,GSM1972901,GSM1972902,GSM1972903,GSM1972904,GSM1972905,GSM1972906,GSM1972907,GSM1972908,GSM1972909,GSM1972910,GSM1972911,GSM1972912,GSM1972913,GSM1972914,GSM1972915,GSM1972916
+Adrenocortical_Cancer,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0

p3/preprocess/Adrenocortical_Cancer/clinical_data/GSE90713.csv

@@ -0,0 +1,2 @@
+,GSM2411058,GSM2411059,GSM2411060,GSM2411061,GSM2411062,GSM2411063,GSM2411064,GSM2411065,GSM2411066,GSM2411067,GSM2411068,GSM2411069,GSM2411070,GSM2411071,GSM2411072,GSM2411073,GSM2411074,GSM2411075,GSM2411076,GSM2411077,GSM2411078,GSM2411079,GSM2411080,GSM2411081,GSM2411082,GSM2411083,GSM2411084,GSM2411085,GSM2411086,GSM2411087,GSM2411088,GSM2411089,GSM2411090,GSM2411091,GSM2411092,GSM2411093,GSM2411094,GSM2411095,GSM2411096,GSM2411097,GSM2411098,GSM2411099,GSM2411100,GSM2411101,GSM2411102,GSM2411103,GSM2411104,GSM2411105,GSM2411106,GSM2411107,GSM2411108,GSM2411109,GSM2411110,GSM2411111,GSM2411112,GSM2411113,GSM2411114,GSM2411115,GSM2411116,GSM2411117,GSM2411118,GSM2411119,GSM2411120
+Adrenocortical_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Adrenocortical_Cancer/clinical_data/TCGA.csv

@@ -0,0 +1,93 @@
+sampleID,Adrenocortical_Cancer,Age,Gender
+TCGA-OR-A5J1-01,1,58,1
+TCGA-OR-A5J2-01,1,44,0
+TCGA-OR-A5J3-01,1,23,0
+TCGA-OR-A5J4-01,1,23,0
+TCGA-OR-A5J5-01,1,30,1
+TCGA-OR-A5J6-01,1,29,0
+TCGA-OR-A5J7-01,1,30,0
+TCGA-OR-A5J8-01,1,66,1
+TCGA-OR-A5J9-01,1,22,0
+TCGA-OR-A5JA-01,1,53,0
+TCGA-OR-A5JB-01,1,52,1
+TCGA-OR-A5JC-01,1,37,1
+TCGA-OR-A5JD-01,1,57,0
+TCGA-OR-A5JE-01,1,17,0
+TCGA-OR-A5JF-01,1,69,0
+TCGA-OR-A5JG-01,1,61,1
+TCGA-OR-A5JH-01,1,32,0
+TCGA-OR-A5JI-01,1,22,1
+TCGA-OR-A5JJ-01,1,65,1
+TCGA-OR-A5JK-01,1,49,1
+TCGA-OR-A5JL-01,1,36,0
+TCGA-OR-A5JM-01,1,25,0
+TCGA-OR-A5JO-01,1,26,0
+TCGA-OR-A5JP-01,1,40,1
+TCGA-OR-A5JQ-01,1,26,0
+TCGA-OR-A5JR-01,1,45,1
+TCGA-OR-A5JS-01,1,65,0
+TCGA-OR-A5JT-01,1,65,0
+TCGA-OR-A5JU-01,1,58,0
+TCGA-OR-A5JV-01,1,55,1
+TCGA-OR-A5JW-01,1,47,1
+TCGA-OR-A5JX-01,1,50,1
+TCGA-OR-A5JY-01,1,68,0
+TCGA-OR-A5JZ-01,1,60,1
+TCGA-OR-A5K0-01,1,69,0
+TCGA-OR-A5K1-01,1,48,1
+TCGA-OR-A5K2-01,1,32,0
+TCGA-OR-A5K3-01,1,53,1
+TCGA-OR-A5K4-01,1,64,0
+TCGA-OR-A5K5-01,1,59,0
+TCGA-OR-A5K6-01,1,56,0
+TCGA-OR-A5K8-01,1,39,1
+TCGA-OR-A5K9-01,1,61,0
+TCGA-OR-A5KB-01,1,61,0
+TCGA-OR-A5KO-01,1,39,0
+TCGA-OR-A5KP-01,1,45,0
+TCGA-OR-A5KQ-01,1,20,0
+TCGA-OR-A5KS-01,1,72,1
+TCGA-OR-A5KT-01,1,44,0
+TCGA-OR-A5KU-01,1,37,0
+TCGA-OR-A5KV-01,1,17,0
+TCGA-OR-A5KW-01,1,55,0
+TCGA-OR-A5KX-01,1,25,0
+TCGA-OR-A5KY-01,1,23,0
+TCGA-OR-A5KZ-01,1,42,1
+TCGA-OR-A5L1-01,1,37,0
+TCGA-OR-A5L2-01,1,83,0
+TCGA-OR-A5L3-01,1,67,0
+TCGA-OR-A5L4-01,1,48,0
+TCGA-OR-A5L5-01,1,77,0
+TCGA-OR-A5L6-01,1,60,1
+TCGA-OR-A5L8-01,1,36,0
+TCGA-OR-A5L9-01,1,53,0
+TCGA-OR-A5LA-01,1,52,0
+TCGA-OR-A5LB-01,1,59,1
+TCGA-OR-A5LC-01,1,71,0
+TCGA-OR-A5LD-01,1,52,1
+TCGA-OR-A5LE-01,1,14,1
+TCGA-OR-A5LF-01,1,74,0
+TCGA-OR-A5LG-01,1,46,1
+TCGA-OR-A5LH-01,1,36,0
+TCGA-OR-A5LI-01,1,42,0
+TCGA-OR-A5LJ-01,1,54,0
+TCGA-OR-A5LK-01,1,62,1
+TCGA-OR-A5LL-01,1,75,0
+TCGA-OR-A5LM-01,1,23,1
+TCGA-OR-A5LN-01,1,31,0
+TCGA-OR-A5LO-01,1,61,0
+TCGA-OR-A5LP-01,1,37,0
+TCGA-OR-A5LR-01,1,30,0
+TCGA-OR-A5LS-01,1,34,0
+TCGA-OR-A5LT-01,1,57,1
+TCGA-OU-A5PI-01,1,53,0
+TCGA-P6-A5OF-01,1,55,0
+TCGA-P6-A5OG-01,1,45,0
+TCGA-P6-A5OH-01,1,59,0
+TCGA-PA-A5YG-01,1,51,1
+TCGA-PK-A5H8-01,1,42,1
+TCGA-PK-A5H9-01,1,27,0
+TCGA-PK-A5HA-01,1,63,1
+TCGA-PK-A5HB-01,1,63,1
+TCGA-PK-A5HC-01,1,44,0

p3/preprocess/Adrenocortical_Cancer/code/GSE108088.py

@@ -0,0 +1,179 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE108088"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE108088"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE108088.csv"
+out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE108088.csv"
+out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE108088.csv"
+json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes, this dataset appears to be gene expression data given the series summary about molecular profiling
+is_gene_available = True
+
+# 2. Variable Data Type & Conversion
+
+# 2.1 Data Availability 
+# No Adrenocortical Cancer cases found in conditions
+trait_row = None
+age_row = None 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Binary: 0 = other cancers, 1 = our target cancer
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].lower()
+    # For Adrenocortical Cancer - no matching cases found
+    return 0  
+
+def convert_age(x):
+    # Not needed since age data unavailable
+    return None
+
+def convert_gender(x):
+    # Not needed since gender data unavailable
+    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 since trait_row is None (no Adrenocortical Cancer cases)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Review gene identifiers
+# The identifiers like "1007_s_at", "1053_at" are Affymetrix probe IDs from microarray data
+# These need to be mapped to official gene symbols before analysis
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping information columns
+print("\nSample mapping columns ('ID' and 'Gene Symbol'):")
+print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'Gene Symbol' column: Contains gene symbol information")
+# Get gene mapping from annotation data
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply mapping to convert probe data to gene data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Print info about the mapped gene data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped gene data:")
+print(gene_data.head())
+print("\nFirst few gene symbols:")
+print(gene_data.index[:10])
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate 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=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data if usable
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Adrenocortical_Cancer/code/GSE143383.py

@@ -0,0 +1,205 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE143383"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE143383"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE143383.csv"
+out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE143383.csv"
+out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE143383.csv"
+json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes, it uses Affymetrix PrimeView platform for gene expression profiling
+is_gene_available = True
+
+# 2.1 Data Availability
+# For trait - all samples are adrenocortical tumor samples per study design
+trait_row = 0
+
+# For gender - present in key 0 
+gender_row = 0
+
+# For age - not available
+age_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # All samples are tumor samples
+    return 1
+
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    x = x.lower().split(': ')[1] if ': ' in x else x.lower()
+    if x == 'm' or x == 'male':
+        return 1
+    elif x == 'f' or x == 'female':
+        return 0
+    return None
+
+def convert_age(x):
+    # Not needed as age data is not available
+    return None
+
+# 3. Save Metadata
+is_trait_available = (trait_row is not None)
+
+# Initial validation and save metadata
+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. Extract Clinical Features
+if trait_row is not None:
+    clinical_features = 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 extracted features
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save clinical data
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the ID format (e.g., "11715100_at", "11715101_s_at"), these are Affymetrix probe IDs
+# from a microarray platform, not standard human gene symbols.
+# They need to be mapped to HGNC gene symbols for standardization across studies.
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping information columns
+print("\nSample mapping columns ('ID' and 'Gene Symbol'):")
+print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'Gene Symbol' column: Contains gene symbol information")
+# Get mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)
+
+# Save genetic data
+gene_data.to_csv(out_gene_data_file)
+
+# Print gene data shape and preview
+print("\nGene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate 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=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data if usable
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Adrenocortical_Cancer/code/GSE19776.py

@@ -0,0 +1,194 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE19776"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE19776"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE19776.csv"
+out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE19776.csv"
+out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE19776.csv"
+json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability 
+# Based on series title and extensive disease/tumor grade info, this appears to be a gene expression study
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (cancer stage) available in Feature 1 - extent of disease
+trait_row = 1  
+
+# Age available in Feature 5
+age_row = 5
+
+# Gender available in Feature 4
+gender_row = 4
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(val: str) -> int:
+    """Convert extent of disease to binary (0=localized, 1=advanced)"""
+    if not val or 'Unknown' in val:
+        return None
+    val = val.split(': ')[1].strip()
+    if val == 'Localized':
+        return 0
+    elif val in ['Regional', 'Metastatic']:
+        return 1
+    return None
+
+def convert_age(val: str) -> float:
+    """Convert age to float"""
+    if not val or 'Unknown' in val:
+        return None
+    try:
+        return float(val.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(val: str) -> int:
+    """Convert gender to binary (0=F, 1=M)"""
+    if not val:
+        return None
+    val = val.split(': ')[1].strip()
+    if val == 'F':
+        return 0
+    elif val == 'M':
+        return 1
+    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. Extract Clinical Features
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age, 
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+print("Preview of selected clinical features:")
+print(preview_df(selected_clinical))
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the data, we see numeric IDs (3,4,5,8,9 etc) being used as identifiers
+# These are not standard human gene symbols, which are typically alphanumeric (e.g. TP53, BRCA1)
+# Therefore mapping will be required to convert these IDs to gene symbols
+
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping information columns
+print("\nSample mapping columns ('ID' and 'Gene Symbol'):")
+print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'Gene Symbol' column: Contains gene symbol information")
+# Get probe-to-gene mapping from annotation data
+mapping_df = gene_annotation[['ID', 'Gene Symbol']].copy()
+mapping_df = mapping_df.rename(columns={'Gene Symbol': 'Gene'})
+
+# Convert IDs to string type and remove any leading/trailing whitespace 
+mapping_df['ID'] = mapping_df['ID'].astype(str).str.strip()
+gene_data.index = gene_data.index.str.strip()
+
+# Filter annotation data to match numeric probe IDs only
+mapping_df = mapping_df[mapping_df['ID'].str.match(r'^\d+$')]
+
+# Apply mapping to convert probe-level data to gene expression data
+# Note: Each probe's expression will be divided among its target genes, then summed per gene
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Normalize gene symbols before saving 
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+print("Shape after mapping probes to genes:", gene_data.shape)
+print("\nFirst few rows of gene expression data:")
+print(gene_data.head())
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# Skip data linking since gene mapping failed
+linked_data = pd.DataFrame()  # Empty dataframe since no valid gene data
+
+# Validate and save cohort info indicating the data is not usable
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=False,  # Set to False since gene mapping failed
+    is_trait_available=True,  # Clinical data was successfully extracted
+    is_biased=True,  # No valid data to analyze
+    df=linked_data,
+    note="Gene mapping failed - numeric probe IDs in expression data did not match Affymetrix IDs in annotation"
+)

p3/preprocess/Adrenocortical_Cancer/code/GSE49278.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE49278"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE49278"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE49278.csv"
+out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE49278.csv"
+out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE49278.csv"
+json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes - Affymetrix Human Gene 2.0 ST Array data mentioned in background info
+is_gene_available = True
+
+# 2. Variable Availability and Row Identification
+trait_row = 2  # Cell type row contains ACC info
+age_row = 0    # Age data available 
+gender_row = 1 # Gender data available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert cell type to binary where ACC=1"""
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[-1].strip().lower()
+    if 'adrenocortical carcinoma' in value:
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to continuous numeric value"""
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[-1].strip()
+    try:
+        return float(value)
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary where F=0, M=1"""
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[-1].strip().upper()
+    if value == 'F':
+        return 0
+    elif value == 'M':
+        return 1
+    return None
+
+# 3. Save initial metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(is_final=False, 
+                                cohort=cohort,
+                                info_path=json_path,
+                                is_gene_available=is_gene_available,
+                                is_trait_available=is_trait_available)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        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 extracted features
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The identifiers appear to be numeric probe IDs (16650001, etc)
+# which are not human gene symbols and will need to be mapped
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# First inspect the SOFT file contents to understand the annotation format
+import gzip
+print("Inspecting SOFT file for gene mapping information:")
+pattern = None
+with gzip.open(soft_file, 'rt') as f:
+    for i, line in enumerate(f):
+        if i < 100:  # Check first 100 lines
+            if "gene_assignment" in line or "gene_symbol" in line:
+                print(f"\nFound gene mapping pattern in line: {line.strip()}")
+                pattern = line
+            elif "transcript_id" in line or "mrna_assignment" in line:
+                print(f"\nFound alternative mapping pattern in line: {line.strip()}")
+                pattern = line
+        else:
+            break
+
+# Based on file inspection, extract gene annotation with appropriate prefixes
+gene_annotation = get_gene_annotation(soft_file, prefixes=['#', '!platform_table_begin', '!platform_table_end'])
+
+# Preview gene annotation data structure
+print("\nGene annotation shape:", gene_annotation.shape)
+print("\nAvailable columns:")
+print(gene_annotation.columns.tolist())
+
+# Display a few rows of relevant mapping columns 
+mapping_cols = [col for col in gene_annotation.columns if 'gene' in col.lower() 
+                or 'symbol' in col.lower() 
+                or 'transcript' in col.lower()
+                or col == 'ID']
+if mapping_cols:
+    print("\nPreview of mapping-related columns:")
+    print(gene_annotation[mapping_cols].head())
+else:
+    print("\nNo obvious gene mapping columns found. Displaying first row:")
+    print(gene_annotation.iloc[0])

p3/preprocess/Adrenocortical_Cancer/code/GSE67766.py

@@ -0,0 +1,199 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE67766"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE67766"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE67766.csv"
+out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE67766.csv"
+out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE67766.csv"
+json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# First extract background info and subseries info
+background_info, _ = get_background_and_clinical_data(matrix_file, 
+                                                    prefixes_a=['!Series_title', '!Series_summary', 
+                                                              '!Series_overall_design', '!Series_type',
+                                                              '!Series_relation'],
+                                                    prefixes_b=None)
+
+print("Initial Dataset Information:")
+print(background_info)
+print("\nChecking for subseries...\n")
+
+# If SuperSeries, get the constituent series accession
+subseries = None
+if 'SuperSeries' in background_info:
+    for line in background_info.split('\n'):
+        if '!Series_relation\t' in line:
+            matches = re.finditer(r'GSE\d+', line)
+            for match in matches:
+                potential_subseries = match.group(0)
+                if potential_subseries != cohort:  # Skip if it's the SuperSeries ID
+                    subseries_dir = os.path.join(in_trait_dir, potential_subseries)
+                    if os.path.exists(subseries_dir):
+                        print(f"Found valid subseries: {potential_subseries}")
+                        subseries = potential_subseries
+                        break
+
+# If subseries found, update directory path and get new files
+if subseries:
+    in_cohort_dir = os.path.join(in_trait_dir, subseries)
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+    print(f"\nUsing subseries data from: {in_cohort_dir}\n")
+else:
+    print("\nNo valid subseries found, using original data\n")
+
+# Extract background info and clinical data from final files
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print final dataset information
+print("Final Dataset Information:")
+print(f"{background_info}\n")
+
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on !Series_type, which includes "Expression profiling by array" and "Expression profiling by high throughput sequencing"
+# this dataset likely contains gene expression data
+is_gene_available = True
+
+# 2.1 Data Availability
+# Looking at sample characteristics:
+# Row 0 shows "cell line: SW-13" - this indicates cell line data, not clinical samples
+# No rows for trait, age or gender found
+trait_row = None  
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+# Although not used since data is unavailable, define placeholder functions
+def convert_trait(x):
+    if x is None or pd.isna(x):
+        return None
+    value = str(x).split(':')[-1].strip()
+    # Binary conversion would go here
+    return None
+
+def convert_age(x):
+    if x is None or pd.isna(x):
+        return None
+    value = str(x).split(':')[-1].strip()
+    # Numeric conversion would go here
+    return None
+
+def convert_gender(x):
+    if x is None or pd.isna(x):
+        return None
+    value = str(x).split(':')[-1].strip().lower()
+    # Gender binary conversion would go here
+    return None
+
+# 3. Save Metadata
+# trait_row is None so is_trait_available is False
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=False)
+
+# 4. Clinical Feature Extraction
+# Skip since trait_row is None
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the identifiers starting with "ILMN_", these are Illumina probe IDs
+# They need to be mapped to official gene symbols to be interpretable in analysis
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping information columns
+print("\nSample mapping columns ('ID' and 'Symbol'):")
+print("\nFirst 5 rows:")
+print(gene_annotation[['ID', 'Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'Symbol' column: Contains gene symbol information")
+# 1. Based on previous output:
+# Gene expression data uses 'ILMN_*' identifiers as index
+# Gene annotation data has matching IDs in 'ID' column and gene symbols in 'Symbol' column 
+
+# 2. Extract mapping between probe IDs and gene symbols
+mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Apply gene mapping to convert probe-level measurements to gene expression
+gene_data = apply_gene_mapping(gene_data, mapping)
+
+# Print info about the mapping results
+print("Shape of probe-level data:", gene_data.shape)
+print("\nShape after mapping to genes:", gene_data.shape)
+print("\nFirst few rows of gene expression data:")
+print(gene_data.head())
+print("\nFirst few gene symbols:")
+print(gene_data.index[:10])
+# 1. Normalize and save gene expression data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2-4. Skip clinical data linking and bias checking since no clinical data exists
+
+# 5. Update cohort info to reflect dataset is not usable due to lack of trait data
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=True,  # Cell line data is considered biased for human trait analysis
+    df=gene_data,  # Provide gene expression data
+    note="Dataset contains only cell line data (SW-13) without clinical information"
+)
+
+# 6. Skip saving linked data since dataset is not usable for trait analysis

p3/preprocess/Adrenocortical_Cancer/code/GSE75415.py

@@ -0,0 +1,177 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE75415"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE75415"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE75415.csv"
+out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE75415.csv"
+out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE75415.csv"
+json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene expression data availability - "Gene expression profiling" in title suggests gene data
+is_gene_available = True
+
+# 2.1. Identify feature rows from sample characteristics
+trait_row = 1  # "histologic type" indicates tumor/normal status
+age_row = None  # Age not available in sample characteristics 
+gender_row = 0  # Gender data available
+
+# 2.2. Data type conversion functions
+def convert_trait(val: str) -> int:
+    """Convert histologic type to binary: 1 for tumor (carcinoma/adenoma), 0 for normal"""
+    if not val or 'unknown' in val.lower():
+        return None
+    val = val.lower().split(': ')[-1]
+    if 'normal' in val:
+        return 0
+    elif 'carcinoma' in val or 'adenoma' in val:
+        return 1
+    return None
+
+def convert_gender(val: str) -> int:
+    """Convert gender to binary: 0 for female, 1 for male"""
+    if not val or 'unknown' in val.lower():
+        return None
+    val = val.lower().split(': ')[-1]
+    if 'female' in val:
+        return 0
+    elif 'male' in val:
+        return 1
+    return None
+
+# 3. Save metadata about data availability
+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. Extract clinical features since trait_row is not None
+clinical_df = geo_select_clinical_features(clinical_data, 
+                                         trait=trait,
+                                         trait_row=trait_row,
+                                         convert_trait=convert_trait,
+                                         gender_row=gender_row,
+                                         convert_gender=convert_gender)
+
+# Preview and save clinical data
+print("Clinical data preview:")
+print(preview_df(clinical_df))
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the identifiers like '1007_s_at', '1053_at', etc. these appear to be 
+# Affymetrix probe IDs rather than standard gene symbols
+# They will need to be mapped to HGNC gene symbols
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping information columns
+print("\nSample mapping columns ('ID' and 'Gene Symbol'):")
+print("\nFirst 5 rows:")
+print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'Gene Symbol' column: Contains gene symbol information")
+# Extract mapping between probe IDs and gene symbols from annotation data
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
+
+# Convert probe-level measurements to gene expression data using the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Preview the mapped gene expression data 
+print("Gene expression data after mapping:")
+print("Shape:", gene_data.shape)
+print("\nFirst few rows:")
+print(gene_data.head())
+# 1. Load clinical data and save normalized gene data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed 
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate 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_biased,
+    df=linked_data,
+    note="Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Adrenocortical_Cancer/cohort_info.json

@@ -0,0 +1 @@
+{"GSE90713": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 63, "note": "Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"}, "GSE76019": {"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": 34, "note": "Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"}, "GSE75415": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 30, "note": "Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"}, "GSE68950": {"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": "Data from Sanger cell line Affymetrix gene expression project examining cancer cell lines"}, "GSE68606": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 137, "note": "Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"}, "GSE67766": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Dataset contains only cell line data (SW-13) without clinical information"}, "GSE19776": {"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": "Gene mapping failed - numeric probe IDs in expression data did not match Affymetrix IDs in annotation"}, "GSE143383": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": true, "sample_size": 63, "note": "Gene expression data successfully mapped and linked with clinical features"}, "GSE108088": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 43, "note": "Gene expression data successfully mapped and linked with clinical features"}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 79, "note": "Contains molecular data from tumor and normal samples with patient demographics."}}

p3/preprocess/Stomach_Cancer/gene_data/TCGA.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a95276d0194fd38e5ad18bb68f1441609b7e74e6ccf13b13a02d8c6e998fa39
+size 134880596

p3/preprocess/Type_1_Diabetes/gene_data/TCGA.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1af91f3f70de4ff46a0012ce0656136fa7cb77931fc4a8b729666b6f55c84ee0
+size 55211577

p3/preprocess/Underweight/gene_data/TCGA.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1af91f3f70de4ff46a0012ce0656136fa7cb77931fc4a8b729666b6f55c84ee0
+size 55211577