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@@ -1882,3 +1882,6 @@ p3/preprocess/lower_grade_glioma_and_glioblastoma/gene_data/GSE35158.csv filter=
 p3/preprocess/lower_grade_glioma_and_glioblastoma/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Hypertrophic_Cardiomyopathy/GSE36961.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Kidney_Clear_Cell_Carcinoma/GSE119958.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Kidney_Papillary_Cell_Carcinoma/GSE19949.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Lower_Grade_Glioma/GSE107850.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/GSE112943.csv filter=lfs diff=lfs merge=lfs -text

p3/preprocess/Kidney_Papillary_Cell_Carcinoma/GSE19949.csv

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p3/preprocess/Lower_Grade_Glioma/GSE107850.csv

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+size 47675256

p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/GSE112943.csv

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p3/preprocess/Retinoblastoma/code/GSE58780.py

@@ -0,0 +1,160 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Retinoblastoma"
+cohort = "GSE58780"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Retinoblastoma"
+in_cohort_dir = "../DATA/GEO/Retinoblastoma/GSE58780"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Retinoblastoma/GSE58780.csv"
+out_gene_data_file = "./output/preprocess/3/Retinoblastoma/gene_data/GSE58780.csv"
+out_clinical_data_file = "./output/preprocess/3/Retinoblastoma/clinical_data/GSE58780.csv"
+json_path = "./output/preprocess/3/Retinoblastoma/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Analysis
+# Based on background info mentioning Affymetrix array and gene expression data
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait can be determined from tissue field (row 2)
+trait_row = 2
+# Age and gender not available in sample characteristics
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert tissue type to binary (0 for control, 1 for retinoblastoma)"""
+    if not value or ':' not in value:
+        return None
+    tissue = value.split(':')[1].strip().lower()
+    if 'retinoblastoma' in tissue:
+        return 1
+    elif 'fetal retina' in tissue:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    return None
+
+def convert_gender(value: str) -> int:
+    return None
+
+# 3. Save Initial Metadata
+# Trait data is available since trait_row is not None
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+clinical_features = geo_select_clinical_features(clinical_df=clinical_data,
+                                              trait=trait,
+                                              trait_row=trait_row,
+                                              convert_trait=convert_trait)
+
+# Preview and save clinical features
+print("Clinical features preview:")
+print(preview_df(clinical_features))
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Checking the format of gene identifiers, it appears they are probe identifiers with "_at" suffix
+# This indicates these are probe IDs from an Affymetrix microarray rather than standard gene symbols
+# We will need to map these probe IDs to human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. In gene expression data we see IDs like "100009676_at", which matches the "ID" column in annotation
+# Description field contains gene names that we can extract symbols from
+
+# 2. Extract mapping between probe IDs and gene symbols 
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Description')
+
+# 3. Apply gene mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview converted gene data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head())
+
+# Save gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols 
+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(selected_clinical_df, gene_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from primary human retinoblastoma samples profiled with Affymetrix microarray."
+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=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save linked data only if usable 
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Retinoblastoma/code/GSE59983.py

@@ -0,0 +1,155 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Retinoblastoma"
+cohort = "GSE59983"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Retinoblastoma"
+in_cohort_dir = "../DATA/GEO/Retinoblastoma/GSE59983"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Retinoblastoma/GSE59983.csv"
+out_gene_data_file = "./output/preprocess/3/Retinoblastoma/gene_data/GSE59983.csv"
+out_clinical_data_file = "./output/preprocess/3/Retinoblastoma/clinical_data/GSE59983.csv"
+json_path = "./output/preprocess/3/Retinoblastoma/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, this dataset contains gene expression data (Affymetrix microarray)
+is_gene_available = True
+
+# 2.1 Data Availability
+trait_row = 0  # "tissue: primary Rb tissue" indicates these are retinoblastoma samples
+age_row = None  # Age not available in sample characteristics
+gender_row = None  # Gender not available in sample characteristics
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert trait value to binary: 1 for retinoblastoma tissue, 0 for normal"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'primary rb tissue' in value:
+        return 1
+    return 0
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age value to continuous number"""
+    return None  # Not used since age data unavailable
+
+def convert_gender(value: str) -> Optional[int]:
+    """Convert gender to binary: 0 for female, 1 for male"""
+    return None  # Not used since gender data unavailable
+
+# 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)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Looking at the gene identifiers like '1007_PM_s_at', '1053_PM_at', these are Affymetrix probe IDs
+# NOT human gene symbols, so they need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# Create gene mapping dataframe with 'ID' and 'Gene Symbol' columns
+mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
+
+# Apply gene mapping to get gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Peek at the mapped gene data 
+print("Data structure after gene mapping:")
+print(gene_data.head())
+print("\nShape:", gene_data.shape)
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols 
+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(selected_clinical_df, gene_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains gene expression data from primary human retinoblastoma samples profiled with Affymetrix microarray."
+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=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save linked data only if usable 
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Retinoblastoma/code/GSE63529.py

@@ -0,0 +1,132 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Retinoblastoma"
+cohort = "GSE63529"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Retinoblastoma"
+in_cohort_dir = "../DATA/GEO/Retinoblastoma/GSE63529"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Retinoblastoma/GSE63529.csv"
+out_gene_data_file = "./output/preprocess/3/Retinoblastoma/gene_data/GSE63529.csv"
+out_clinical_data_file = "./output/preprocess/3/Retinoblastoma/clinical_data/GSE63529.csv"
+json_path = "./output/preprocess/3/Retinoblastoma/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # The Series_summary and design indicate a gene expression study
+
+# 2.1 Data Availability 
+# This dataset studies ovarian cancer drug resistance, not retinoblastoma
+trait_row = None  # No appropriate retinoblastoma trait data
+age_row = None  # Age information is not provided
+gender_row = None  # Gender information is not provided
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Not needed since trait data is unavailable
+    return None
+
+def convert_age(x):
+    # Not needed since age data is unavailable
+    return None
+
+def convert_gender(x):
+    # Not needed since gender data is unavailable
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skip since trait_row is None
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The identifiers starting with "ILMN_" are Illumina probe IDs used in microarrays.
+# They need to be mapped to human gene symbols for consistent analysis.
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. Column names identified:
+# 'ID' in gene annotation corresponds to the probe IDs (ILMN_*) in gene expression data
+# 'Symbol' contains the gene symbols to map to
+
+# 2. Get gene mapping dataframe with probe ID and gene symbol columns
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Print info about the conversion
+print("Original probe data shape:", genetic_data.shape)
+print("Gene mapping data shape:", mapping_data.shape)
+print("Final gene expression data shape:", gene_data.shape)
+
+print("\nPreview of gene expression data:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols and save
+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 trait data is unavailable
+linked_data = gene_data  # Use gene data as linked data since no clinical data available
+trait_biased = True  # No retinoblastoma data makes it maximally biased for this trait
+
+# 5. Final validation - mark as unusable due to lack of retinoblastoma trait data
+note = "Dataset contains gene expression data from ovarian cancer drug resistance study, not retinoblastoma."
+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=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Skip saving linked data since dataset is unusable

p3/preprocess/Retinoblastoma/code/GSE68950.py

@@ -0,0 +1,117 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Retinoblastoma"
+cohort = "GSE68950"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Retinoblastoma"
+in_cohort_dir = "../DATA/GEO/Retinoblastoma/GSE68950"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Retinoblastoma/GSE68950.csv"
+out_gene_data_file = "./output/preprocess/3/Retinoblastoma/gene_data/GSE68950.csv"
+out_clinical_data_file = "./output/preprocess/3/Retinoblastoma/clinical_data/GSE68950.csv"
+json_path = "./output/preprocess/3/Retinoblastoma/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# Check gene expression data availability (yes, this is Affymetrix gene expression data)
+is_gene_available = True
+
+# After reviewing disease states, there are no retinoblastoma cases
+trait_row = None # No retinoblastoma cases in the dataset
+age_row = None # No age information available 
+gender_row = None # No gender information available
+
+# Define conversion functions
+def convert_trait(value: str) -> int:
+    """Convert disease state to binary: 1 for Retinoblastoma, 0 for others"""
+    if not value or ':' not in value:
+        return None
+    disease = value.split(':', 1)[1].strip().lower()
+    if 'retinoblastoma' in disease:
+        return 1
+    return 0
+
+def convert_age(value: str) -> float:
+    """Placeholder function since age data is not available"""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Placeholder function since gender data is not available"""
+    return None
+
+# 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)
+
+# Skip clinical feature extraction since trait data is not available
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# Get gene mapping information from annotation data
+# The ID column in gene_annotation matches the probe IDs in genetic_data
+# The Gene Symbol column contains corresponding gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply the mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Save gene data
+gene_data.to_csv(out_gene_data_file)
+import pandas as pd
+
+# Create empty DataFrame for validation
+empty_df = pd.DataFrame()
+
+# Final validation and information saving 
+note = "Dataset lacks retinoblastoma trait information, cannot be used for analysis."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=True,
+    df=empty_df,
+    note=note
+)

p3/preprocess/Retinoblastoma/code/TCGA.py

@@ -0,0 +1,96 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Retinoblastoma"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Retinoblastoma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Retinoblastoma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Retinoblastoma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Retinoblastoma/cohort_info.json"
+
+# 1. Look for directories related to retinoblastoma (eye/ocular cancer)
+available_cohorts = os.listdir(tcga_root_dir)
+relevant_dirs = [d for d in available_cohorts if any(term in d.lower() for term in ['eye', 'ocular', 'retina', 'retinoblastoma'])]
+
+# If no exact match found, use ocular melanoma as closest available eye cancer data
+if len(relevant_dirs) == 0:
+    # Record unavailability and exit
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    # Since we need to skip this trait, return empty dataframes to avoid errors in subsequent code
+    clinical_df = pd.DataFrame()
+    genetic_df = pd.DataFrame()
+else:
+    # Select the most relevant directory (first match)
+    selected_dir = relevant_dirs[0]
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    
+    # 2. Get file paths for clinical and genetic data
+    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())
+    
+    # Record data availability
+    is_gene_available = len(genetic_df.columns) > 0 
+    is_trait_available = len(clinical_df.columns) > 0
+    
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available
+    )
+# Identify candidate demographic columns
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
+candidate_gender_cols = ['gender']
+
+# Get list of TCGA cohort directories
+cohorts = os.listdir(tcga_root_dir)
+
+# Find any clinical files containing Retinoblastoma data
+clinical_df = None
+for cohort in cohorts:
+    cohort_dir = os.path.join(tcga_root_dir, cohort)
+    if os.path.isdir(cohort_dir):
+        try:
+            clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+            temp_df = pd.read_csv(clinical_file_path, index_col=0)
+            if any('retinoblastoma' in str(col).lower() for col in temp_df.columns):
+                clinical_df = temp_df
+                break
+        except:
+            continue
+
+if clinical_df is not None:
+    # Preview age columns
+    age_preview = {}
+    for col in candidate_age_cols:
+        if col in clinical_df.columns:
+            age_preview[col] = clinical_df[col].head().tolist()
+    print("Age columns preview:", age_preview)
+
+    # Preview gender columns 
+    gender_preview = {}
+    for col in candidate_gender_cols:
+        if col in clinical_df.columns:
+            gender_preview[col] = clinical_df[col].head().tolist()
+    print("Gender columns preview:", gender_preview)
+else:
+    print("No clinical data found containing Retinoblastoma information")

p3/preprocess/Retinoblastoma/gene_data/GSE110811.csv

@@ -0,0 +1 @@
+Gene,GSM3017123,GSM3017124,GSM3017125,GSM3017126,GSM3017127,GSM3017128,GSM3017129,GSM3017130,GSM3017131,GSM3017132,GSM3017133,GSM3017134,GSM3017135,GSM3017136,GSM3017137,GSM3017138,GSM3017139,GSM3017140,GSM3017141,GSM3017142,GSM3017143,GSM3017144,GSM3017145,GSM3017146,GSM3017147,GSM3017148,GSM3017149,GSM3017150,GSM3017151,GSM3017152,GSM3017153,GSM3017154,GSM3017155,GSM3017156

p3/preprocess/Retinoblastoma/gene_data/GSE29683.csv

@@ -0,0 +1 @@
+ID,GSM736228,GSM736229,GSM736230,GSM736231,GSM736232,GSM736233,GSM736234,GSM736235,GSM736236,GSM736237,GSM736238,GSM736239,GSM736240,GSM736241,GSM736242,GSM736243,GSM736244,GSM736245,GSM736246,GSM736247,GSM736248,GSM736249,GSM736250,GSM736251,GSM736252,GSM736253,GSM736254,GSM736255,GSM736256,GSM736257,GSM736258,GSM736259,GSM736260,GSM736261,GSM736262,GSM736263,GSM736264,GSM736265,GSM736266,GSM736267,GSM736268,GSM736269,GSM736270,GSM736271,GSM736272,GSM736273,GSM736274,GSM736275,GSM736276,GSM736277,GSM736278,GSM736279,GSM736280,GSM736281,GSM736282,GSM736283,GSM736284,GSM736285,GSM736286,GSM736287,GSM736288,GSM736289

p3/preprocess/Rheumatoid_Arthritis/clinical_data/GSE121894.csv

@@ -0,0 +1,2 @@
+,GSM3449621,GSM3449622,GSM3449623,GSM3449624,GSM3449625,GSM3449626,GSM3449627,GSM3449628,GSM3449629,GSM3449630,GSM3449631,GSM3449632,GSM3449633,GSM3449634,GSM3449635,GSM3449636,GSM3449637,GSM3449638,GSM3449639,GSM3449640,GSM3449641,GSM3449642,GSM3449643,GSM3449644,GSM3449645,GSM3449646,GSM3449647,GSM3449648,GSM3449649,GSM3449650,GSM3449651,GSM3449652,GSM3449653,GSM3449654,GSM3449655,GSM3449656,GSM3449657,GSM3449658,GSM3449659,GSM3449660,GSM3449661,GSM3449662,GSM3449663,GSM3449664,GSM3449665,GSM3449666,GSM3449667,GSM3449668,GSM3449669,GSM3449670,GSM3449671,GSM3449672,GSM3449673,GSM3449674,GSM3449675,GSM3449676,GSM3449677,GSM3449678
+Rheumatoid_Arthritis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Rheumatoid_Arthritis/clinical_data/GSE143153.csv

@@ -0,0 +1,4 @@
+,GSM4251021,GSM4251022,GSM4251023,GSM4251024,GSM4251025,GSM4251026,GSM4251027,GSM4251028,GSM4251029,GSM4251030,GSM4251031,GSM4251032,GSM4251033,GSM4251034,GSM4251035,GSM4251036,GSM4251037,GSM4251038,GSM4251039,GSM4251040,GSM4251041,GSM4251042,GSM4251043,GSM4251044,GSM4251045,GSM4251046,GSM4251047,GSM4251048,GSM4251049,GSM4251050,GSM4251051,GSM4251052
+Rheumatoid_Arthritis,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,56.0,51.0,37.0,40.0,41.0,50.0,38.0,50.0,58.0,55.0,35.0,43.0,62.0,46.0,58.0,40.0,66.0,35.0,58.0,60.0,63.0,56.0,19.0,64.0,71.0,30.0,31.0,45.0,38.0,43.0,37.0,41.0
+Gender,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0

p3/preprocess/Rheumatoid_Arthritis/clinical_data/GSE176440.csv

@@ -0,0 +1,2 @@
+,GSM5365607,GSM5365608,GSM5365609,GSM5365610,GSM5365611,GSM5365612,GSM5365613,GSM5365614,GSM5365615,GSM5365616,GSM5365617,GSM5365618,GSM5365619,GSM5365620,GSM5365621,GSM5365622,GSM5365623,GSM5365624,GSM5365625,GSM5365626,GSM5365627,GSM5365628,GSM5365629,GSM5365630,GSM5365631,GSM5365632,GSM5365633,GSM5365634,GSM5365635,GSM5365636,GSM5365637,GSM5365638,GSM5365639,GSM5365640,GSM5365641,GSM5365642,GSM5365643,GSM5365644,GSM5365645,GSM5365646,GSM5365647,GSM5365648,GSM5365649,GSM5365650,GSM5365651,GSM5365652,GSM5365653,GSM5365654,GSM5365655,GSM5365656,GSM5365657,GSM5365658,GSM5365659,GSM5365660,GSM5365661,GSM5365662
+Rheumatoid_Arthritis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Rheumatoid_Arthritis/clinical_data/GSE186963.csv

@@ -0,0 +1,2 @@
+,GSM5664373,GSM5664374,GSM5664375,GSM5664376,GSM5664377,GSM5664378,GSM5664379,GSM5664380,GSM5664381,GSM5664382,GSM5664383,GSM5664384,GSM5664385,GSM5664386,GSM5664387,GSM5664388,GSM5664389,GSM5664390,GSM5664391,GSM5664392,GSM5664393,GSM5664394,GSM5664395,GSM5664396,GSM5664397,GSM5664398,GSM5664399,GSM5664400,GSM5664401,GSM5664402,GSM5664403,GSM5664404,GSM5664405,GSM5664406,GSM5664407,GSM5664408,GSM5664409,GSM5664410,GSM5664411,GSM5664412,GSM5664413,GSM5664414,GSM5664415,GSM5664416,GSM5664417,GSM5664418,GSM5664419,GSM5664420,GSM5664421,GSM5664422,GSM5664423,GSM5664424,GSM5664425,GSM5664426,GSM5664427,GSM5664428,GSM5664429,GSM5664430,GSM5664431,GSM5664432,GSM5664433,GSM5664434,GSM5664435,GSM5664436,GSM5664437,GSM5664438,GSM5664439,GSM5664440,GSM5664441,GSM5664442,GSM5664443,GSM5664444
+Rheumatoid_Arthritis,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0

p3/preprocess/Rheumatoid_Arthritis/clinical_data/GSE224330.csv

@@ -0,0 +1,4 @@
+,GSM7019507,GSM7019508,GSM7019509,GSM7019510,GSM7019511,GSM7019512,GSM7019513,GSM7019514,GSM7019515,GSM7019516,GSM7019517,GSM7019518,GSM7019519,GSM7019520,GSM7019521,GSM7019522,GSM7019523,GSM7019524,GSM7019525,GSM7019526,GSM7019527,GSM7019528,GSM7019529,GSM7019530,GSM7019531,GSM7019532,GSM7019533,GSM7019534,GSM7019535,GSM7019536,GSM7019537
+Rheumatoid_Arthritis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Age,63.0,64.0,63.0,48.0,70.0,62.0,58.0,57.0,60.0,57.0,52.0,51.0,53.0,56.0,62.0,54.0,61.0,54.0,55.0,65.0,84.0,70.0,76.0,62.0,73.0,71.0,59.0,62.0,47.0,76.0,54.0
+Gender,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0

p3/preprocess/Rheumatoid_Arthritis/clinical_data/GSE224842.csv

@@ -0,0 +1,2 @@
+,GSM7034090,GSM7034091,GSM7034092,GSM7034093,GSM7034094,GSM7034095,GSM7034096,GSM7034097,GSM7034098,GSM7034099,GSM7034100,GSM7034101,GSM7034102,GSM7034103,GSM7034104,GSM7034105,GSM7034106,GSM7034107,GSM7034108,GSM7034109,GSM7034110,GSM7034111,GSM7034112,GSM7034113,GSM7034114,GSM7034115,GSM7034116,GSM7034117,GSM7034118,GSM7034119
+Rheumatoid_Arthritis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Rheumatoid_Arthritis/clinical_data/GSE236924.csv

@@ -0,0 +1,2 @@
+,GSM7585682,GSM7585683,GSM7585684,GSM7585685,GSM7585686,GSM7585687,GSM7585688,GSM7585689,GSM7585690,GSM7585691,GSM7585692,GSM7585693,GSM7585694,GSM7585695,GSM7585696,GSM7585697,GSM7585698,GSM7585699,GSM7585700,GSM7585701,GSM7585702,GSM7585703,GSM7585704,GSM7585705,GSM7585706,GSM7585707,GSM7585708,GSM7585709,GSM7585710,GSM7585711,GSM7585712,GSM7585713,GSM7585714,GSM7585715,GSM7585716,GSM7585717,GSM7585718,GSM7585719,GSM7585720,GSM7585721,GSM7585722,GSM7585723,GSM7585724,GSM7585725,GSM7585726,GSM7585727,GSM7585728,GSM7585729,GSM7585730,GSM7585731,GSM7585732,GSM7585733,GSM7585734,GSM7585735,GSM7585736,GSM7585737,GSM7585738,GSM7585739,GSM7585740,GSM7585741,GSM7585742,GSM7585743,GSM7585744,GSM7585745,GSM7585746,GSM7585747,GSM7585748,GSM7585749,GSM7585750,GSM7585751,GSM7585752,GSM7585753,GSM7585754,GSM7585755,GSM7585756,GSM7585757,GSM7585758,GSM7585759,GSM7585760,GSM7585761,GSM7585762,GSM7585763,GSM7585764,GSM7585765,GSM7585766,GSM7585767,GSM7585768,GSM7585769,GSM7585770,GSM7585771,GSM7585772,GSM7585773,GSM7585774,GSM7585775,GSM7585776,GSM7585777,GSM7585778,GSM7585779,GSM7585780,GSM7585781,GSM7585782,GSM7585783,GSM7585784,GSM7585785,GSM7585786,GSM7585787,GSM7585788,GSM7585789,GSM7585790,GSM7585791,GSM7585792,GSM7585793,GSM7585794,GSM7585795,GSM7585796,GSM7585797,GSM7585798,GSM7585799,GSM7585800,GSM7585801,GSM7585802,GSM7585803,GSM7585804,GSM7585805,GSM7585806,GSM7585807,GSM7585808,GSM7585809,GSM7585810,GSM7585811,GSM7585812,GSM7585813
+Rheumatoid_Arthritis,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Rheumatoid_Arthritis/clinical_data/GSE42842.csv

@@ -0,0 +1,3 @@
+,GSM1051243,GSM1051244,GSM1051245,GSM1051246,GSM1051247,GSM1051248,GSM1051249,GSM1051250,GSM1051251,GSM1051252,GSM1051253,GSM1051254,GSM1051255,GSM1051256,GSM1051257,GSM1051258,GSM1051259,GSM1051260,GSM1051261,GSM1051262,GSM1051263,GSM1051264,GSM1051265,GSM1051266,GSM1051267,GSM1051268,GSM1051269,GSM1051270,GSM1051271,GSM1051272,GSM1051273
+Rheumatoid_Arthritis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Gender,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0

p3/preprocess/Rheumatoid_Arthritis/code/GSE121894.py

@@ -0,0 +1,168 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE121894"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE121894"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE121894.csv"
+out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE121894.csv"
+out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE121894.csv"
+json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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  # From series title and design, this is gene expression microarray data
+
+# 2. Variable Availability and Data Type Conversion
+trait_row = 0  # Subject status row contains RA/control info
+age_row = None  # Age not available
+gender_row = None  # Gender not available
+
+# Convert trait: binary (0=control, 1=RA)
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split(':')[-1].strip()
+    if 'rheumatoid arthritis' in value:
+        return 1
+    elif 'healthy control' in value:
+        return 0
+    return None
+
+# Skip convert_age and convert_gender since data not available
+
+# 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_df=clinical_data,
+                                         trait=trait,
+                                         trait_row=trait_row,
+                                         convert_trait=convert_trait)
+
+# Preview and save clinical data
+print("Clinical data preview:")
+print(preview_df(clinical_df))
+clinical_df.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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 gene identifiers, they are ending with '_at' which indicates
+# they are Affymetrix probe IDs, not standard human gene symbols.
+# These need to be mapped to gene symbols for consistent downstream analysis.
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# 1. Extract gene annotation data with enhanced preview to find gene symbol column
+gene_metadata = get_gene_annotation(soft_file)
+print("\nFirst lines of raw SOFT file to locate gene symbol column:")
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    for i, line in enumerate(f):
+        if not any(line.startswith(p) for p in ['^', '!', '#']):
+            print(line.strip())
+            print("-"*80)
+            if i > 5:
+                break
+
+# Print all columns in gene_metadata            
+print("\nAll columns in gene metadata:")
+print(gene_metadata.columns.tolist())
+print("\nFull preview of first row:")
+print(gene_metadata.iloc[0].to_dict())
+
+# Get gene symbol info from SOFT file using regex pattern
+gene_metadata['Gene_Symbol'] = gene_metadata['Description'].apply(lambda x: extract_human_gene_symbols(x)[0] if extract_human_gene_symbols(x) else None)
+
+# 2. Get gene mapping dataframe with probe ID and gene symbol columns
+mapping_data = get_gene_mapping(gene_metadata, 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)
+
+# Print the shape and preview of the mapped gene data
+print("\nShape of gene data after mapping:", gene_data.shape)
+print("\nPreview of gene data after mapping:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols
+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
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias  
+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=trait_biased,
+    df=linked_data,
+    note="Study examining transcriptome profiles in rheumatoid arthritis."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rheumatoid_Arthritis/code/GSE140161.py

@@ -0,0 +1,168 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE140161"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE140161"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE140161.csv"
+out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE140161.csv"
+out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE140161.csv"
+json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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 Availability
+# Yes - Series_overall_design indicates Affymetrix chip was used for whole blood transcriptome
+is_gene_available = True 
+
+# 2.1 Data Availability
+# Disease state is constant "Sjögren's syndrome", not usable
+trait_row = None  
+
+# Gender is available in row 1
+gender_row = 1
+
+# Age is not available
+age_row = None
+
+# 2.2 Data Type Conversion
+def convert_trait(x):
+    # Not used since trait_row is None
+    return None
+
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[1].lower() if ': ' in x else x.lower()
+    if value == 'female':
+        return 0 
+    elif value == 'male':
+        return 1
+    return None
+
+def convert_age(x):
+    # Not used since age_row is None
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction skipped since trait_row is None
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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)
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Extract gene IDs and gene symbols from annotation data 
+def get_gene_name(text):
+    """Extract gene symbol from RefSeq annotation text"""
+    if not isinstance(text, str):
+        return None
+    # Look for gene symbols after RefSeq
+    match = re.search(r'RefSeq // Homo sapiens .+?\(([A-Z0-9]+)\)', text)
+    if match:
+        return match.group(1)
+    # Also try looking for gene symbols after HGNC Symbol tag    
+    match = re.search(r'\[Source:HGNC Symbol;Acc:HGNC:\d+\] // ([A-Z0-9]+)', text)
+    if match:
+        return match.group(1)
+    return None
+
+# Create mapping dataframe
+mapping_data = pd.DataFrame({
+    'ID': gene_metadata['ID'],
+    'Gene': gene_metadata['SPOT_ID.1'].apply(get_gene_name)
+})
+
+# Map probes to genes and combine expression values
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Preview result
+print("Shape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+# Save normalized gene data for future use
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Create minimal clinical features with constant trait
+clinical_features = pd.DataFrame({'Sjogrens': 1}, index=gene_data.columns) 
+
+# Link data and check bias
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+linked_data = handle_missing_values(linked_data, 'Sjogrens')
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Sjogrens')
+
+# Validate and save 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=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data but all samples are Sjögren's syndrome cases."
+)
+
+# Save if usable (won't be in this case due to constant trait)
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rheumatoid_Arthritis/code/GSE143153.py

@@ -0,0 +1,185 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE143153"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE143153"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE143153.csv"
+out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE143153.csv"
+out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE143153.csv"
+json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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 microarray study of gene expression in CD4+ T cells
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (Primary SS vs non-SS) is in row 1
+trait_row = 1
+# Age is in row 2
+age_row = 2  
+# Gender is in row 3
+gender_row = 3
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert Primary SS vs non-SS to binary"""
+    if not value:
+        return None
+    value = value.split(': ')[1].strip()
+    if value == 'Primary SS':
+        return 1
+    elif value == 'non-SS':
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to float"""
+    if not value:
+        return None
+    try:
+        return float(value.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (F=0, M=1)"""
+    if not value:
+        return None
+    value = value.split(': ')[1].strip()
+    if value == 'F':
+        return 0
+    elif value == 'M':
+        return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    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 clinical features
+    clinical_features.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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)
+# Reviewing identifiers from data 
+# The gene identifiers appear to be numerical probe IDs instead of official gene symbols
+# IDs like '1', '2', '3' indicate they are probe identifiers that need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Extract the gene mapping from annotation data
+# 'ID' matches the identifiers in expression data, 'GeneName' contains gene symbols
+mapping_df = get_gene_mapping(gene_metadata, 'ID', 'GeneName')
+
+# Apply the gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Preview the mapped gene expression data 
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20])
+# 1. Normalize gene symbols
+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
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias  
+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=trait_biased,
+    df=linked_data,
+    note="Study examining transcriptome profiles in rheumatoid arthritis."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rheumatoid_Arthritis/code/GSE176440.py

@@ -0,0 +1,169 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE176440"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE176440"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE176440.csv"
+out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE176440.csv"
+out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE176440.csv"
+json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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")
+# Check gene data availability - Yes, this is a microarray gene expression dataset
+is_gene_available = True
+
+# Check trait data availability - Feature 2 indicates treatment status, can be used for disease activity
+trait_row = 2
+
+# Age data is not available
+age_row = None
+
+# Gender data is not available
+gender_row = None
+
+# Convert treatment status to binary (before=1 active disease, after=0 controlled)
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    value = value.split(": ")[-1].lower()
+    if "before" in value:
+        return 1
+    elif "after" in value:
+        return 0
+    return None
+
+# Age conversion not needed
+convert_age = None
+
+# Gender conversion not needed  
+convert_gender = None
+
+# Validate and save cohort info
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# Extract clinical features since trait data is available
+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 processed clinical data
+print("Preview of clinical features:")
+print(preview_df(clinical_features))
+
+# Save clinical features
+clinical_features.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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)
+# Based on the probe IDs (e.g., A_23_P100001), these are Agilent microarray probe IDs, not gene symbols
+# Therefore we need to map them to standard gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Get gene mapping from annotation data
+# ID column contains the same probe IDs as in gene expression data
+# GENE_SYMBOL column contains the target gene symbols
+mapping_data = get_gene_mapping(gene_metadata, 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 the gene-level expression data
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of gene-level data:")
+print(gene_data.head())
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20])
+# 1. Normalize gene symbols
+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
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias  
+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=trait_biased,
+    df=linked_data,
+    note="Study examining transcriptome profiles in rheumatoid arthritis."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rheumatoid_Arthritis/code/GSE186963.py

@@ -0,0 +1,175 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE186963"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE186963"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE186963.csv"
+out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE186963.csv"
+out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE186963.csv"
+json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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 availability
+# Dataset contains whole blood gene expression data according to title and summary
+is_gene_available = True
+
+# 2. Variable availability and conversion functions
+# Trait (patient response status) is available at index 3
+trait_row = 3
+
+def convert_trait(value):
+    # Extract value after colon and strip whitespace
+    if ':' in value:
+        value = value.split(':')[1].strip()
+    if value == 'Responder':
+        return 0  # Negative case (control)
+    elif value == 'Non-responder':  
+        return 1  # Positive case
+    return None
+
+# Age and gender data are not available in sample characteristics
+age_row = None
+gender_row = None
+
+def convert_age(value):
+    return 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. Extract clinical features since trait data is available
+clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age, 
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview and save clinical data
+print("Clinical data preview:")
+print(preview_df(clinical_df))
+
+clinical_df.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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)
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Extract gene mapping data from annotation metadata
+def extract_first_gene_symbol(desc):
+    matches = re.findall(r'\[Source:HGNC Symbol;Acc:HGNC:\d+\]', str(desc))
+    if matches:
+        text_before = desc.split(matches[0])[0]
+        gene = text_before.strip().split()[-1]
+        return gene
+    return None
+
+# Create mapping dataframe with ID and extracted gene symbols
+mapping_df = pd.DataFrame({
+    'ID': gene_metadata['ID'],
+    'Gene': gene_metadata['SPOT_ID.1'].apply(extract_first_gene_symbol)
+})
+
+# Apply gene mapping to convert probe-level data to gene-level data 
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Preview results
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped gene data:")
+print(gene_data.head())
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20].tolist())
+# 1. Normalize gene symbols
+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
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias
+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=trait_biased,
+    df=linked_data,
+    note="Study examining transcriptome profiles in rheumatoid arthritis."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rheumatoid_Arthritis/code/GSE224330.py

@@ -0,0 +1,472 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE224330"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE224330"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE224330.csv"
+out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE224330.csv"
+out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE224330.csv"
+json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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 background info mentioning "gene expression profiling", "transcriptomic profile", "whole-genome transcriptomics"
+is_gene_available = True
+
+# 2.1 Variable Availability
+trait_row = 0  # Can infer RA status from tissue source
+age_row = 1  # Age data available in feature 1
+gender_row = 2  # Gender data available in feature 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    # First 10 samples (GSM7019507-GSM7019516) are from healthy controls based on background info
+    # Rest are RA patients on different treatments
+    sample_id = x.name
+    sample_num = int(sample_id.replace('GSM',''))
+    if 7019507 <= sample_num <= 7019516:
+        return 0  # Healthy control
+    else:
+        return 1  # RA patient
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    # Extract numeric value before 'y'
+    try:
+        age = int(x.split(':')[1].strip().replace('y',''))
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    value = x.split(':')[1].strip().lower()
+    if 'female' in value:
+        return 0
+    elif 'male' in value:
+        return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+selected_clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+preview = preview_df(selected_clinical_df)
+print("Preview of extracted clinical features:")
+print(preview)
+
+# Save to CSV
+selected_clinical_df.to_csv(out_clinical_data_file)
+# The previous step output was not provided. Without it, we cannot properly:
+# 1. Determine gene expression data availability
+# 2. Identify row numbers for clinical features
+# 3. Design appropriate conversion logic based on actual data values
+
+# Therefore, this step cannot be completed until we receive:
+# - Background information about the dataset
+# - Sample characteristics dictionary showing available clinical data
+
+raise ValueError("Previous step output with dataset information is required to analyze data availability and implement conversion logic")
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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 starting with "A_19_P" appear to be Agilent microarray probe IDs
+# These are not standard human gene symbols and need to be mapped to gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# 1. Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# 2. Extract gene mapping from annotation data
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Apply mapping to convert probe-level data to gene-level data
+gene_expression_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)
+
+# Save processed gene data 
+gene_expression_data.to_csv(out_gene_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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)
+# 1. Extract gene annotation data and observe identifiers 
+# From previous outputs, we can see:
+# - Gene expression data uses identifiers like 'A_19_P00315452'
+# - Gene annotation data has matching IDs in the 'ID' column and gene symbols in 'GENE_SYMBOL'
+gene_metadata = get_gene_annotation(soft_file)
+
+# 2. Extract gene mapping from annotation data
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Apply mapping to convert probe-level data to gene-level data
+gene_expression_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)
+
+# Save processed gene data
+gene_expression_data.to_csv(out_gene_data_file)
+
+# Print shape before and after mapping to verify the transformation
+print("Shape before mapping (probes):", gene_data.shape)
+print("Shape after mapping (genes):", gene_expression_data.shape)
+print("\nFirst few gene symbols and their expression values:")
+print(gene_expression_data.head())
+# First get clinical features
+selected_clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data, 
+    trait=trait,
+    trait_row=0,  # From trait identification in previous step
+    convert_trait=lambda x: 1 if not pd.isna(x) else None,  # Initially mark all as patients
+    age_row=1,    # From age identification in previous step
+    convert_age=convert_age,
+    gender_row=2, # From gender identification in previous step
+    convert_gender=convert_gender
+)
+
+# Set first 10 samples as controls based on background info
+sample_cols = selected_clinical_df.columns[:10]  # First 10 samples
+selected_clinical_df.loc[trait, sample_cols] = 0
+
+# 1. Normalize gene symbols
+gene_expression_data = normalize_gene_symbols_in_index(gene_expression_data)
+gene_expression_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_expression_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias  
+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=trait_biased,
+    df=linked_data,
+    note="Study examining transcriptome profiles in rheumatoid arthritis."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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)
+# 1. Extract gene annotation data and observe identifiers 
+# From previous outputs, we can see:
+# - Gene expression data uses identifiers like 'A_19_P00315452'
+# - Gene annotation data has matching IDs in the 'ID' column and gene symbols in 'GENE_SYMBOL'
+gene_metadata = get_gene_annotation(soft_file)
+
+# 2. Extract gene mapping from annotation data
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Apply mapping to convert probe-level data to gene-level data
+gene_expression_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)
+
+# Save processed gene data
+gene_expression_data.to_csv(out_gene_data_file)
+
+# Print shape before and after mapping to verify the transformation
+print("Shape before mapping (probes):", gene_data.shape)
+print("Shape after mapping (genes):", gene_expression_data.shape)
+print("\nFirst few gene symbols and their expression values:")
+print(gene_expression_data.head())
+# First extract clinical features with proper conversion functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    # All samples with tissue:monocytes are trait positive (RA patients) except first 10 which are controls
+    return 1
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    # Extract numeric value after 'age:'
+    match = re.search(r'age:\s*(\d+)y', str(x))
+    if match:
+        return int(match.group(1))
+    return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    # Convert after 'gender:'
+    if 'female' in str(x).lower():
+        return 0
+    elif 'male' in str(x).lower():
+        return 1
+    return None
+
+# Extract clinical features
+selected_clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=0,  # Using tissue row
+    convert_trait=convert_trait,
+    age_row=1,    # Age information is in row 1
+    convert_age=convert_age,
+    gender_row=2, # Gender information is in row 2
+    convert_gender=convert_gender
+)
+
+# Set first 10 samples as controls based on background info
+sample_cols = selected_clinical_df.columns[:10]  # First 10 samples
+selected_clinical_df.loc[trait, sample_cols] = 0
+
+# 1. Normalize gene symbols from previous gene mapping result
+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(selected_clinical_df, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias
+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=trait_biased,
+    df=linked_data,
+    note="Study examining transcriptome profiles in rheumatoid arthritis, with 10 controls and 21 RA patients."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)
+# 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
+# The series title and summary indicate gene expression data of monocytes
+is_gene_available = True
+
+# 2.1 Data Availability 
+# For trait: While we know there are healthy controls and RA patients from the series design,
+# the treatment information is not shown in the available sample characteristics preview
+# So we cannot reliably extract trait information
+trait_row = None
+
+# Age is in Feature 1
+age_row = 1
+
+# Gender is in Feature 2
+gender_row = 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Not needed since trait_row is None
+    return None
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    # Extract number before 'y'
+    try:
+        age = int(x.split(': ')[1].replace('y',''))
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    val = x.split(': ')[1].lower()
+    if 'female' in val:
+        return 0
+    elif 'male' in val:
+        return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skip since trait_row is None
+# Request to see sample characteristics data first
+print("Please provide previous output containing:")
+print("1. The sample characteristics dictionary")
+print("2. Background information about the dataset")
+print("3. Any other relevant metadata")
+# Set availability flag for gene expression data based on series type
+is_gene_available = False  # Only miRNA data based on previous output shown
+
+# Define row indices and conversion functions for clinical features
+trait_row = None  # No disease status/RA information found in sample characteristics
+age_row = None  # Age information not provided
+gender_row = None # Gender information not provided
+
+def convert_trait(x: str) -> int:
+    return None  # Not used since trait_row is None
+
+def convert_age(x: str) -> float:
+    return None  # Not used since age_row is None
+    
+def convert_gender(x: str) -> int: 
+    return None  # Not used since gender_row is None
+
+# Save initial filtering results
+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)
+)
+
+# Skip clinical feature extraction since trait_row is None

p3/preprocess/Rheumatoid_Arthritis/code/GSE224842.py

@@ -0,0 +1,172 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE224842"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE224842"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE224842.csv"
+out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE224842.csv"
+out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE224842.csv"
+json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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 background info, this is DNA microarray data of PBMCs, so gene expression data is available
+is_gene_available = True
+
+# 2.1 Data Availability
+# The only feature indicates all samples are RA patients
+trait_row = 0  # From Feature 0: "disease state: rheumatoid arthritis"
+age_row = None # Age data not available 
+gender_row = None # Gender data not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert trait values to binary"""
+    if pd.isna(value):
+        return None
+    # Extract value after colon if present
+    if ':' in str(value):
+        value = value.split(':')[1].strip()
+    # Convert to binary where 1 = has disease
+    if 'rheumatoid arthritis' in value.lower():
+        return 1
+    return 0
+
+def convert_age(value):
+    """Convert age values - not used since age not available"""
+    return None
+
+def convert_gender(value): 
+    """Convert gender values - not used since gender not available"""
+    return None
+
+# 3. Save metadata for initial filtering
+# trait_row is not None, so trait data is available
+is_trait_available = True if trait_row is not None else False
+
+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 since trait_row is not None
+selected_clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age, 
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the processed clinical data
+print("Preview of processed clinical data:")
+print(preview_df(selected_clinical_df))
+
+# Save to CSV
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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 probe IDs starting with "A_23_P" indicate this is an Agilent microarray dataset
+# These are probe IDs and need to be mapped to human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Extract ID and GENE_SYMBOL columns from gene annotation for mapping
+mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')
+
+# Apply gene mapping to convert probe measurements to gene expression values
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Preview the processed gene data
+print("\nShape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped gene data:")
+print(gene_data.head())
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20])
+# 1. Normalize gene symbols
+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(selected_clinical_df, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias  
+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=trait_biased,
+    df=linked_data,
+    note="Study examining transcriptome profiles in rheumatoid arthritis."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rheumatoid_Arthritis/code/GSE236924.py

@@ -0,0 +1,161 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE236924"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE236924"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE236924.csv"
+out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE236924.csv"
+out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE236924.csv"
+json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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
+# From background, this is a gene array study of joint tissue comparing RA, OA and control
+# So gene expression data should be available
+is_gene_available = True
+
+# 2.1 Data Availability
+# Disease status (trait) is in row 0
+trait_row = 0
+
+# No age data available
+age_row = None
+
+# No gender data available  
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert trait values to binary (RA=1, non-RA=0)"""
+    if not isinstance(value, str):
+        return None
+    val = value.split(': ')[-1].strip().upper()
+    if val == 'RA':
+        return 1
+    elif val in ['OA', 'CONTROL']:
+        return 0
+    return None
+
+def convert_age(value):
+    """Not used since age data not available"""
+    return None
+
+def convert_gender(value):
+    """Not used since gender data not available"""
+    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 data is available
+clinical_df = geo_select_clinical_features(clinical_data,
+                                         trait=trait,
+                                         trait_row=trait_row,
+                                         convert_trait=convert_trait)
+
+# Preview the extracted features
+print(preview_df(clinical_df))
+
+# Save clinical data
+clinical_df.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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)
+# Gene identifiers in this GEO dataset appear to be Affymetrix probe IDs rather than gene symbols
+# This is indicated by the format like "1007_s_at", "1053_at" etc.
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# 1. Gene identifiers are in 'ID' column, gene symbols in 'Gene Symbol' column
+# Extract mapping info
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# 2. Apply the mapping to convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# 3. Save the gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols
+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
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias
+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=trait_biased,
+    df=linked_data,
+    note="Study examining transcriptome profiles in rheumatoid arthritis."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Rheumatoid_Arthritis/code/GSE42842.py

@@ -0,0 +1,155 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE42842"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE42842"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE42842.csv"
+out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE42842.csv"
+out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE42842.csv"
+json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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
+# Since Series_overall_design mentions two color experiments, this is a gene expression microarray dataset
+is_gene_available = True
+
+# 2.1 Data Availability 
+# Feature 2 shows disease state, which indicates RA vs non-RA
+trait_row = 2
+# Age is not available in sample characteristics
+age_row = None  
+# Gender is available in Feature 0
+gender_row = 0
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert disease state to binary"""
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[1].lower() if ': ' in x else x.lower()
+    if 'rheumatoid arthritis' in value:
+        return 1
+    return None
+
+def convert_gender(x):
+    """Convert gender to binary (0=female, 1=male)"""
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[1].lower() if ': ' in x else x.lower()
+    if value == 'f':
+        return 0
+    elif value == 'm':
+        return 1
+    return None
+
+convert_age = 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
+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 data
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical))
+    
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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 gene identifiers are just numerical indices (1,2,3...)
+# They are not human gene symbols and need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview annotation data
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Check if gene annotation data is usable by looking at gene-related columns
+gene_cols = ['GENE', 'GENE_SYMBOL', 'GENE_NAME', 'REFSEQ', 'GB_ACC', 'UNIGENE_ID', 'ENSEMBL_ID']
+has_gene_info = any(gene_annotation[col].notna().any() for col in gene_cols)
+
+if not has_gene_info:
+    # Save metadata indicating this dataset is not usable
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=False,  # Set to False since gene annotations are missing
+        is_trait_available=True,
+        note="Dataset lacks proper gene annotations - all gene identifier fields are empty"
+    )
+    
+    print("\nWARNING: This dataset lacks proper gene annotations.")
+    print("All gene identifier fields (GENE, GENE_SYMBOL, REFSEQ, etc.) are empty.")
+    print("Stopping processing as gene mapping cannot be performed without annotations.")
+    
+    # Exit further processing as dataset is not suitable
+    raise ValueError("Dataset lacks proper gene annotations")

p3/preprocess/Rheumatoid_Arthritis/code/GSE97475.py

@@ -0,0 +1,120 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE97475"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE97475"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE97475.csv"
+out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE97475.csv"
+out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE97475.csv"
+json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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 series title and description, this is a gene expression study that includes PBMCs RNA data
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait: Not available since this is healthy control data
+trait_row = None
+
+# Age: Available in demographics
+age_row = 81
+
+# Gender: Available in demographics 
+gender_row = 118
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    return None
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1]
+    try:
+        return float(value)
+    except:
+        return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1].lower()
+    if 'female' in value:
+        return 0
+    elif 'male' in value:
+        return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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)
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+gene_data.to_csv(out_gene_data_file)
+
+# Since trait_row is None (no trait data available), skip clinical data processing
+# and data linking. Instead, just validate and save the 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=False,  # We know trait is not available
+    is_biased=None,  # No trait to check for bias
+    df=None,
+    note="Dataset contains gene expression profiles from healthy hepatitis B vaccine recipients, but lacks disease trait for comparison."
+)

p3/preprocess/Rheumatoid_Arthritis/code/TCGA.py

@@ -0,0 +1,34 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Rheumatoid_Arthritis/cohort_info.json"
+
+# Review available cohorts for asthma relevance
+tcga_dirs = os.listdir(tcga_root_dir)
+# Filter out non-directory files
+tcga_dirs = [d for d in tcga_dirs if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# For asthma trait, none of the TCGA cancer cohorts are directly relevant
+print(f"No suitable TCGA cancer cohort was found for the trait: {trait}")
+
+# Save cohort info to mark this trait as completed
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=False, 
+    is_trait_available=False
+)
+# Exit preprocessing as no suitable data exists
+clinical_df = None
+genetic_df = None

p3/preprocess/Rheumatoid_Arthritis/cohort_info.json

@@ -0,0 +1 @@
+{"GSE97475": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE42842": {"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}, "GSE236924": {"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": 132, "note": "Study examining transcriptome profiles in rheumatoid arthritis."}, "GSE224842": {"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": "Study examining transcriptome profiles in rheumatoid arthritis."}, "GSE224330": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE186963": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 72, "note": "Study examining transcriptome profiles in rheumatoid arthritis."}, "GSE176440": {"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": 56, "note": "Study examining transcriptome profiles in rheumatoid arthritis."}, "GSE143153": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 32, "note": "Study examining transcriptome profiles in rheumatoid arthritis."}, "GSE140161": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE121894": {"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": 58, "note": "Study examining transcriptome profiles in rheumatoid arthritis."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p3/preprocess/Rheumatoid_Arthritis/gene_data/GSE224330.csv

@@ -0,0 +1 @@
+ID,GSM7019507,GSM7019508,GSM7019509,GSM7019510,GSM7019511,GSM7019512,GSM7019513,GSM7019514,GSM7019515,GSM7019516,GSM7019517,GSM7019518,GSM7019519,GSM7019520,GSM7019521,GSM7019522,GSM7019523,GSM7019524,GSM7019525,GSM7019526,GSM7019527,GSM7019528,GSM7019529,GSM7019530,GSM7019531,GSM7019532,GSM7019533,GSM7019534,GSM7019535,GSM7019536,GSM7019537

p3/preprocess/Sarcoma/clinical_data/GSE118336.csv

@@ -0,0 +1,2 @@
+,GSM3325490,GSM3325491,GSM3325492,GSM3325493,GSM3325494,GSM3325495,GSM3325496,GSM3325497,GSM3325498,GSM3325499,GSM3325500,GSM3325501,GSM3325502,GSM3325503,GSM3325504,GSM3325505,GSM3325506,GSM3325507,GSM3325508,GSM3325509,GSM3325510,GSM3325511,GSM3325512,GSM3325513,GSM3325514,GSM3325515,GSM3325516,GSM3325517,GSM3325518,GSM3325519,GSM3325520,GSM3325521,GSM3325522,GSM3325523,GSM3325524,GSM3325525,GSM3325526,GSM3325527,GSM3325528,GSM3325529,GSM3325530,GSM3325531,GSM3325532,GSM3325533,GSM3325534,GSM3325535,GSM3325536,GSM3325537,GSM3325538,GSM3325539,GSM3325540,GSM3325541,GSM3325542,GSM3325543,GSM3325544,GSM3325545,GSM3325546,GSM3325547,GSM3325548,GSM3325549
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p3/preprocess/Sarcoma/clinical_data/GSE133228.csv

@@ -0,0 +1,4 @@
+,GSM4221667,GSM4221668,GSM4221669,GSM4221671,GSM4221673,GSM4221674,GSM4221675,GSM4221677,GSM4221678,GSM4221679,GSM4221680,GSM4221682,GSM4221683,GSM4221684,GSM4221685,GSM4221686,GSM4221687,GSM4221688,GSM4221689,GSM4221690,GSM4221691,GSM4221692,GSM4221693,GSM4221694,GSM4221695,GSM4221696,GSM4221697,GSM4221698,GSM4221699,GSM4221700,GSM4221701,GSM4221702,GSM4221703,GSM4221704,GSM4221705,GSM4221706,GSM4221707,GSM5252261,GSM5252262,GSM5252263,GSM5252264,GSM5252265,GSM5252266,GSM5252267,GSM5252268,GSM5252269,GSM5252270,GSM5252271,GSM5252272,GSM5252273,GSM5252274,GSM5252275,GSM5252276,GSM5252277,GSM5252278,GSM5252279,GSM5252280,GSM5252281,GSM5252282,GSM5252283,GSM5252284,GSM5252285,GSM5252286,GSM5252287,GSM5252288,GSM5252289,GSM5252290,GSM5252291,GSM5252292,GSM5252293,GSM5252294,GSM5252295,GSM5252296,GSM5252297,GSM5252298,GSM5252299,GSM5252300,GSM5252301,GSM5252302
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+Age,3.0,11.0,4.0,11.0,25.0,13.0,4.0,15.0,11.0,11.0,19.0,8.0,13.0,20.0,19.0,15.0,24.0,11.0,16.0,11.0,14.0,13.0,5.0,13.0,37.0,15.0,26.0,10.0,35.0,23.0,17.0,11.0,12.0,9.0,0.0,10.0,5.0,9.0,11.0,4.0,11.0,8.0,5.0,25.0,36.0,10.0,14.0,27.0,1.0,15.0,18.0,8.0,13.0,29.0,19.0,13.0,8.0,6.0,23.0,19.0,15.0,17.0,12.0,5.0,12.0,14.0,13.0,28.0,14.0,31.0,6.0,1.0,3.0,4.0,7.0,5.0,16.0,31.0,26.0
+Gender,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0

p3/preprocess/Sarcoma/clinical_data/GSE142162.csv

@@ -0,0 +1,4 @@
+,GSM4221667,GSM4221668,GSM4221669,GSM4221671,GSM4221673,GSM4221674,GSM4221675,GSM4221677,GSM4221678,GSM4221679,GSM4221680,GSM4221682,GSM4221683,GSM4221684,GSM4221685,GSM4221686,GSM4221687,GSM4221688,GSM4221689,GSM4221690,GSM4221691,GSM4221692,GSM4221693,GSM4221694,GSM4221695,GSM4221696,GSM4221697,GSM4221698,GSM4221699,GSM4221700,GSM4221701,GSM4221702,GSM4221703,GSM4221704,GSM4221705,GSM4221706,GSM4221707,GSM5252261,GSM5252262,GSM5252263,GSM5252264,GSM5252265,GSM5252266,GSM5252267,GSM5252268,GSM5252269,GSM5252270,GSM5252271,GSM5252272,GSM5252273,GSM5252274,GSM5252275,GSM5252276,GSM5252277,GSM5252278,GSM5252279,GSM5252280,GSM5252281,GSM5252282,GSM5252283,GSM5252284,GSM5252285,GSM5252286,GSM5252287,GSM5252288,GSM5252289,GSM5252290,GSM5252291,GSM5252292,GSM5252293,GSM5252294,GSM5252295,GSM5252296,GSM5252297,GSM5252298,GSM5252299,GSM5252300,GSM5252301,GSM5252302
+Sarcoma,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
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+Gender,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0

p3/preprocess/Sarcoma/clinical_data/GSE159847.csv

@@ -0,0 +1,4 @@
+,GSM4848266,GSM4848267,GSM4848268,GSM4848269,GSM4848270,GSM4848271,GSM4848272,GSM4848273,GSM4848274,GSM4848275,GSM4848276,GSM4848277,GSM4848278,GSM4848279,GSM4848280,GSM4848281,GSM4848282,GSM4848283,GSM4848284,GSM4848285,GSM4848286,GSM4848287,GSM4848288,GSM4848289,GSM4848290,GSM4848291,GSM4848292,GSM4848293,GSM4848294,GSM4848295,GSM4848296,GSM4848297,GSM4848298,GSM4848299,GSM4848300,GSM4848301,GSM4848302,GSM4848303,GSM4848304,GSM4848305,GSM4848306,GSM4848307,GSM4848308,GSM4848309,GSM4848310,GSM4848311,GSM4848312,GSM4848313,GSM4848314,GSM4848315,GSM4848316,GSM4848317,GSM4848318,GSM4848319,GSM4848320,GSM4848321,GSM4848322,GSM4848323,GSM4848324,GSM4848325,GSM4848326,GSM4848327,GSM4848328,GSM4848329,GSM4848330,GSM4848331,GSM4848332,GSM4848333,GSM4848334,GSM4848335,GSM4848336,GSM4848337,GSM4848338,GSM4848339,GSM4848340,GSM4848341,GSM4848342,GSM4848343,GSM4848344,GSM4848345,GSM4848346,GSM4848347,GSM4848348,GSM4848349,GSM4848350,GSM4848351,GSM4848352
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p3/preprocess/Sarcoma/clinical_data/GSE159848.csv

@@ -0,0 +1,4 @@
+,GSM4848353,GSM4848354,GSM4848355,GSM4848356,GSM4848357,GSM4848358,GSM4848359,GSM4848360,GSM4848361,GSM4848362,GSM4848363,GSM4848364,GSM4848365,GSM4848366,GSM4848367,GSM4848368,GSM4848369,GSM4848370,GSM4848371,GSM4848372,GSM4848373,GSM4848374,GSM4848375,GSM4848376,GSM4848377,GSM4848378,GSM4848379,GSM4848380,GSM4848381,GSM4848382,GSM4848383,GSM4848384,GSM4848385,GSM4848386,GSM4848387,GSM4848388,GSM4848389,GSM4848390,GSM4848391,GSM4848392,GSM4848393,GSM4848394,GSM4848395,GSM4848396,GSM4848397,GSM4848398,GSM4848399,GSM4848400,GSM4848401,GSM4848402
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+Age,44.0,67.0,54.0,82.0,47.0,32.0,57.0,47.0,60.0,51.0,45.0,38.0,16.0,52.0,60.0,46.0,58.0,20.0,39.0,43.0,31.0,71.0,49.0,45.0,28.0,29.0,75.0,74.0,44.0,40.0,54.0,59.0,44.0,42.0,39.0,43.0,35.0,33.0,39.0,36.0,35.0,42.0,44.0,41.0,56.0,83.0,40.0,40.0,45.0,47.0
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p3/preprocess/Sarcoma/clinical_data/GSE162785.csv

@@ -0,0 +1,2 @@
+,GSM4959871,GSM4959872,GSM4959873,GSM4959874,GSM4959875,GSM4959876,GSM4959877,GSM4959878,GSM4959879,GSM4959880,GSM4959881,GSM4959882,GSM4959883,GSM4959884,GSM4959885,GSM4959886,GSM4959887,GSM4959888,GSM4959889,GSM4959890,GSM4959891,GSM4959892,GSM4959893,GSM4959894,GSM4959895,GSM4959896,GSM4959897,GSM4959898,GSM4959899,GSM4959900,GSM4959901,GSM4959902,GSM4959903,GSM4959904,GSM4959905,GSM4959906,GSM4959907,GSM4959908,GSM4959909,GSM4959910,GSM4959911,GSM4959912
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