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@@ -1876,3 +1876,4 @@ p3/preprocess/Hepatitis/GSE159676.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Liver_Cancer/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Liver_cirrhosis/TCGA.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Liver_cirrhosis/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Intellectual_Disability/GSE59630.csv filter=lfs diff=lfs merge=lfs -text

p3/preprocess/Intellectual_Disability/GSE59630.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e5c0e4fe82474252895e75acef8889788fdaa9a6ebae8e0f14431f2eec631fdb
+size 25116469

p3/preprocess/Osteoarthritis/code/GSE93698.py

@@ -0,0 +1,172 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE93698"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE93698"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoarthritis/GSE93698.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoarthritis/gene_data/GSE93698.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoarthritis/clinical_data/GSE93698.csv"
+json_path = "./output/preprocess/3/Osteoarthritis/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  # Based on background info describing gene expression profiling
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Row Identification 
+trait_row = 1  # Disease state contains OA info
+age_row = 2    # Age data available
+gender_row = 3 # Gender data available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert disease state to binary OA indicator"""
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[1].strip().lower()
+    if value == 'osteoarthritis':
+        return 1
+    else:
+        return 0
+
+def convert_age(value: str) -> float:
+    """Convert age to float"""
+    if pd.isna(value):
+        return None
+    try:
+        return float(value.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (0=female, 1=male)"""
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[1].strip().lower()
+    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 clinical features
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save clinical features
+    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])
+# Review gene identifiers from gene expression data
+# The identifiers appear to be Affymetrix probe IDs (e.g., '1007_s_at', '1053_at') 
+# rather than standard human gene symbols
+# These need to be mapped to corresponding gene symbols for 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))
+# Extract probe ID and gene symbol mapping
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply mapping and aggregate probe measurements into gene expression values
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Print preview of the results
+print("Gene data shape after mapping:", gene_data.shape)
+print("\nFirst few rows:")
+print(gene_data.head())
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
+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/Osteoarthritis/code/GSE93720.py

@@ -0,0 +1,171 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE93720"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE93720"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoarthritis/GSE93720.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoarthritis/gene_data/GSE93720.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoarthritis/clinical_data/GSE93720.csv"
+json_path = "./output/preprocess/3/Osteoarthritis/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
+# The background info mentions GeneChip Human Genome U133 Plus 2.0 Array, so this contains gene expression data
+is_gene_available = True
+
+# 2. Variable Data Processing
+
+# 2.1 Data Row Identification
+# Trait (OA vs RA) is in row 0
+trait_row = 0  
+
+# Age and gender are not available in the sample characteristics
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    # Convert OA/RA to 1/0 after the colon
+    if not value:
+        return None
+    value = value.split(': ')[1].strip().upper()
+    if value == 'OA':
+        return 1
+    elif value == 'RA': 
+        return 0
+    return None
+
+def convert_age(value):
+    # Not needed since age is not available
+    return None
+
+def convert_gender(value):
+    # Not needed since gender is not available
+    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 = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age, 
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the processed clinical data
+preview_df(selected_clinical)
+
+# Save clinical data
+selected_clinical.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])
+# Observe IDs like '1007_s_at' which are Affymetrix probe IDs, not gene symbols
+# They need to be mapped to human gene symbols for biological interpretation
+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. Observe identifiers and determine mapping columns
+# Gene expression data uses probe IDs like '1007_s_at' which match the 'ID' column
+# Gene symbols are in 'Gene Symbol' column
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get gene mapping dataframe 
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply gene mapping to get gene-level expression
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview result
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of mapped gene expression data:")
+print(preview_df(gene_data))
+
+# Save gene expression data
+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
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
+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/Osteoarthritis/code/GSE98460.py

@@ -0,0 +1,265 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE98460"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE98460"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoarthritis/GSE98460.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoarthritis/gene_data/GSE98460.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoarthritis/clinical_data/GSE98460.csv"
+json_path = "./output/preprocess/3/Osteoarthritis/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  # RNA microarray data indicated in background info
+
+# 2. Variable Availability and Data Type
+# Trait (OA) - can be inferred from diagnosis field
+trait_row = 1
+def convert_trait(x):
+    if not x or ':' not in x:
+        return None
+    value = x.split(':')[1].strip().lower()
+    if 'osteoarthritis' in value or 'oa' in value:
+        return 1
+    return 0
+
+# Age - available in field 2
+age_row = 2  
+def convert_age(x):
+    if not x or ':' not in x:
+        return None
+    try:
+        return float(x.split(':')[1].strip().split()[0])
+    except:
+        return None
+
+# Gender - available in field 3
+gender_row = 3
+def convert_gender(x):
+    if not x or ':' not in 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
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical))
+    selected_clinical.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])
+# Examining gene identifiers
+# The IDs look like custom platform probe IDs (e.g. 16650001, 16650003)
+# These are not standard human gene symbols (which would be like BRCA1, TP53, etc.)
+# We will need to map these probe IDs to gene symbols
+
+requires_gene_mapping = True
+# Look at more content in SOFT file to find gene annotation section
+with gzip.open(soft_file_path, 'rt') as f:
+    platform_found = False
+    table_start = False
+    first_row = None
+    gene_rows = []
+    
+    for line in f:
+        if '!Platform_table_begin' in line:
+            table_start = True
+            continue
+        elif '!Platform_table_end' in line:
+            break
+        elif table_start:
+            if first_row is None:
+                first_row = line.strip()
+            else:
+                gene_rows.append(line.strip())
+
+# Create dataframe from the platform table data
+import io
+header = first_row.split('\t')
+gene_data = '\n'.join(gene_rows)
+gene_annotation = pd.read_csv(io.StringIO(gene_data), sep='\t', names=header)
+
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# First examine more content in SOFT file to locate gene symbol information
+with gzip.open(soft_file_path, 'rt') as f:
+    found_table = False
+    header = None
+    first_five_rows = []
+    for line in f:
+        if '!Platform_title' in line:
+            print("Platform title:", line.strip())
+        elif '!Platform_organism' in line:
+            print("Platform organism:", line.strip())
+        elif '!Platform_table_begin' in line:
+            found_table = True
+            continue
+        elif found_table:
+            if header is None:
+                header = line.strip()
+                print("\nPlatform table header:")
+                print(header)
+            elif len(first_five_rows) < 5:
+                first_five_rows.append(line.strip())
+            else:
+                break
+
+print("\nFirst few rows:")
+for row in first_five_rows:
+    print(row)
+
+# Now try using tabs as delimiter to see full column structure
+print("\nSplitting first row by tabs to check all fields:")
+if first_five_rows:
+    print(first_five_rows[0].split('\t'))
+
+# Based on examination results, extract complete platform data
+platform_data = pd.read_csv(gzip.open(soft_file_path, 'rt'), 
+                          sep='\t',
+                          skiprows=lambda x: x == 0 or not found_table,
+                          comment='!')
+
+print("\nFull column names found:")
+print(platform_data.columns.tolist())
+
+print("\nPreview of complete annotation data:")
+print(preview_df(platform_data))
+# Extract gene annotation using library function
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Print available columns to identify correct names
+print("Available columns:", gene_annotation.columns.tolist())
+
+# First examine the column names 
+probe_data = gene_annotation.head()
+print("\nFirst few rows:")
+print(preview_df(probe_data))
+
+# Create mapping after seeing actual column names
+mapping_df = get_gene_mapping(gene_annotation, 
+                            prob_col='ID', 
+                            gene_col='Gene Title')  
+
+# Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+print("\nPreview of gene expression data after mapping:")
+print(preview_df(gene_data))
+# Load clinical data
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# Normalize gene symbols and save gene expression data
+genetic_data = normalize_gene_symbols_in_index(genetic_data) 
+genetic_data.to_csv(out_gene_data_file)
+
+# Link clinical and genetic data using library function
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Final validation and information saving
+note = "This dataset contains cartilage tissue samples from OA patients, with gene expression data and demographic information."
+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
+)
+
+# 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)
+# First examine platform information in SOFT file
+print("Examining platform information in SOFT file...")
+with gzip.open(soft_file_path, 'rt') as f:
+    platform_lines = []
+    capture = False
+    for line in f:
+        if line.startswith(('!Platform_title', '!Platform_organism', '!Platform_technology')):
+            print(line.strip())
+        elif '!platform_table_begin' in line.lower():
+            capture = True
+            continue
+        elif '!platform_table_end' in line.lower():
+            break
+        elif capture:
+            platform_lines.append(line.strip())
+
+# Now extract complete annotation with pandas
+print("\nExtracting complete platform annotation...")
+platform_df = pd.read_csv(io.StringIO('\n'.join(platform_lines)), sep='\t')
+
+print("\nFound columns:")
+print(platform_df.columns.tolist())
+
+print("\nPreview of annotation data:")
+print(preview_df(platform_df))

p3/preprocess/Osteoarthritis/code/TCGA.py

@@ -0,0 +1,30 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoarthritis/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoarthritis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoarthritis/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Osteoarthritis/cohort_info.json"
+
+# 1. Check if suitable directory exists for Osteoarthritis
+# No suitable directory found in TCGA dataset
+
+# Mark data as unavailable since no relevant cohort exists
+is_gene_available = False 
+is_trait_available = False
+
+# Record this information
+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
+)

p3/preprocess/Osteoarthritis/gene_data/GSE98460.csv

@@ -0,0 +1 @@
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p3/preprocess/Osteoporosis/clinical_data/GSE152073.csv

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p3/preprocess/Osteoporosis/clinical_data/GSE20881.csv

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p3/preprocess/Osteoporosis/clinical_data/GSE224330.csv

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p3/preprocess/Osteoporosis/clinical_data/GSE35925.csv

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p3/preprocess/Osteoporosis/clinical_data/GSE51495.csv

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p3/preprocess/Osteoporosis/clinical_data/GSE56814.csv

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p3/preprocess/Osteoporosis/clinical_data/GSE56815.csv

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p3/preprocess/Osteoporosis/clinical_data/GSE84500.csv

@@ -0,0 +1,2 @@
+,GSM2238538,GSM2238539,GSM2238540,GSM2238541,GSM2238542,GSM2238543,GSM2238544,GSM2238545,GSM2238546,GSM2238547,GSM2238548,GSM2238549,GSM2238550,GSM2238551,GSM2238552,GSM2238553,GSM2238554,GSM2238555,GSM2238556,GSM2238557,GSM2238558,GSM2238559,GSM2238560,GSM2238561,GSM2238562,GSM2238563,GSM2238564,GSM2238565,GSM2238566,GSM2238567,GSM2238568,GSM2238569,GSM2238570,GSM2238571,GSM2238572,GSM2238573,GSM2238574,GSM2238575,GSM2238576,GSM2238577,GSM2238578,GSM2238579,GSM2238580,GSM2238581,GSM2238582,GSM2238583,GSM2238584,GSM2238585,GSM2238586,GSM2238587,GSM2238588,GSM2238589,GSM2238590,GSM2238591
+Osteoporosis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0

p3/preprocess/Osteoporosis/code/GSE152073.py

@@ -0,0 +1,127 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE152073"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE152073"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoporosis/GSE152073.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE152073.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE152073.csv"
+json_path = "./output/preprocess/3/Osteoporosis/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
+# Yes - Based on background info mentioning Affymetrix microarrays and gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Conversion Functions
+# 2.1 Row Identifiers 
+trait_row = 0  # Inferred from background info stating all subjects have osteoporosis
+age_row = 1  # Age data in row 1
+gender_row = 0  # Gender data in row 0
+
+# 2.2 Conversion Functions
+def convert_trait(x):
+    # All subjects have osteoporosis based on study design
+    return 1
+
+def convert_age(x):
+    try:
+        # Extract numeric age value after colon
+        age = int(x.split(': ')[1])
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    try:
+        gender = x.split(': ')[1].lower()
+        if gender == 'female':
+            return 0
+        elif gender == 'male':
+            return 1
+        return None
+    except:
+        return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age, 
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file 
+def get_genetic_data_modified(file_path: str, marker: str = "!series_matrix_table_begin") -> pd.DataFrame:
+    with gzip.open(file_path, 'rt') as file:
+        for i, line in enumerate(file):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    genetic_data = pd.read_csv(file_path, compression='gzip', skiprows=skip_rows, comment='!', 
+                             delimiter='\t', on_bad_lines='skip').T
+    genetic_data.columns = genetic_data.iloc[0]  # Set first row as column names
+    genetic_data = genetic_data.iloc[1:]  # Remove the first row
+
+    return genetic_data
+
+genetic_data = get_genetic_data_modified(matrix_file_path)
+
+# Print outputs to examine structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 column names (probe identifiers):")
+print(list(genetic_data.columns)[:20])
+
+print("\nFirst 5 row names (sample IDs):")
+print(list(genetic_data.index)[:5])
+
+
+

p3/preprocess/Osteoporosis/code/GSE20881.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE20881"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE20881"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoporosis/GSE20881.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE20881.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE20881.csv"
+json_path = "./output/preprocess/3/Osteoporosis/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  # Based on background info, this is a gene expression study of intestinal biopsies
+
+# 2.1 Data Availability
+trait_row = 58  # disease field with 'healthy' vs 'crohns disease'
+age_row = 2  # birth date field
+gender_row = None  # No gender data
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert trait values to binary: 0 for healthy control, 1 for disease case"""
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].lower()
+    if 'healthy' in value:
+        return 0
+    elif 'crohns disease' in value:
+        return 1
+    return None
+
+def convert_age(value):
+    """Convert birth date to age using procedure date as reference"""
+    from datetime import datetime
+    if not isinstance(value, str) or ': ' not in value:
+        return None
+    try:
+        birth_date = datetime.strptime(value.split(': ')[1], '%m/%d/%y')
+        # Use 2005 as reference year since procedures were in 2004-2005
+        ref_date = datetime(2005, 1, 1)
+        age = ref_date.year - birth_date.year
+        # Adjust age if birthday hasn't occurred yet
+        if ref_date.month < birth_date.month or (ref_date.month == birth_date.month and ref_date.day < birth_date.day):
+            age -= 1
+        return age
+    except:
+        return None
+
+convert_gender = None  # No gender data
+
+# 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. Clinical Feature Extraction
+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 and save clinical features
+print(preview_df(clinical_features))
+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])
+# The row IDs are just numbers (1, 2, 3, etc) and not recognizable gene symbols
+# So they need to be mapped to proper 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. From the preview, we can see 'ID' column in gene annotation maps to row IDs in expression data,
+#    and 'GENE_SYMBOL' column contains the gene symbols
+
+# 2. Create gene mapping dataframe
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')
+
+# 3. Apply mapping to convert probe data to gene expression
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Print info about the mapping and conversion
+print("\nShape after mapping to gene symbols:", gene_data.shape)
+print("\nFirst few gene symbols:", list(gene_data.index)[:10])
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
+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/Osteoporosis/code/GSE224330.py

@@ -0,0 +1,186 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE224330"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE224330"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoporosis/GSE224330.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE224330.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE224330.csv"
+json_path = "./output/preprocess/3/Osteoporosis/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
+# Based on background info mentioning "whole-genome transcriptomics" and "gene expression profiling"
+is_gene_available = True
+
+# 2.1 Data Availability
+# For trait - look at comorbidity field which includes 'osteoporosis' 
+trait_row = 3
+
+# For age - available in field 1
+age_row = 1
+
+# For gender - available in field 2  
+gender_row = 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[1].strip().lower()
+    # Binary: 1 if has osteoporosis, 0 if not
+    if value == 'osteoporosis':
+        return 1
+    elif value in ['none', 'hypothyroidism', 'schizoaffective disorder', 'arthrosis']:
+        return 0
+    return None
+
+def convert_age(value):
+    if pd.isna(value):
+        return None
+    # Extract numeric age value
+    try:
+        age = int(value.split(': ')[1].strip('y'))
+        return age
+    except:
+        return None
+
+def convert_gender(value):
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[1].strip().lower()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        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. Extract Clinical Features
+if trait_row is not None:
+    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 data
+    preview = preview_df(clinical_df)
+    print("Clinical data preview:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_df.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 (A_19_P...), these are Agilent microarray probe IDs, not human gene symbols
+# They need to be mapped to official gene symbols for standardization and interpretation
+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. Identify mapping columns
+# From looking at the data:
+# - Gene expression data uses identifiers like 'A_19_P00315452'
+# - In gene annotation, 'ID' column has the same format
+# - 'GENE_SYMBOL' column contains the target gene symbols
+
+# 2. Get gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Apply mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Save gene data
+gene_data.to_csv(out_gene_data_file)
+
+# Preview results
+print("\nShape of gene expression data after mapping:", gene_data.shape)
+print("\nPreview of mapped gene data:")
+print(preview_df(gene_data))
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
+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/Osteoporosis/code/GSE35925.py

@@ -0,0 +1,176 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE35925"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE35925"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoporosis/GSE35925.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE35925.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE35925.csv"
+json_path = "./output/preprocess/3/Osteoporosis/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 
+# Based on background info mentioning transcriptional analysis using U133 Plus 2.0 GeneChip
+is_gene_available = True
+
+# 2.1 Data Availability
+# Can infer osteoporosis risk from gender (row 0) since these are post-menopausal women 
+# receiving osteoporosis prevention treatment
+trait_row = 0
+# Age data available in row 1
+age_row = 1  
+# Gender data available in row 0
+gender_row = 0
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    '''Convert to binary based on osteoporosis risk'''
+    if pd.isna(x) or ':' not in x:
+        return None
+    val = x.split(':', 1)[1].strip().lower()
+    # Post-menopausal females receiving preventive treatment are high risk
+    if 'female' in val:
+        return 1
+    return None
+
+def convert_age(x):
+    '''Convert age to continuous value'''
+    if pd.isna(x) or ':' not in x:
+        return None 
+    try:
+        return float(x.split(':', 1)[1].strip())
+    except:
+        return None
+
+def convert_gender(x):
+    '''Convert gender to binary (0=female, 1=male)'''
+    if pd.isna(x) or ':' not in x:
+        return None
+    val = x.split(':', 1)[1].strip().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. Extract clinical features
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age, 
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the selected features
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical_df))
+    
+    # Save to CSV
+    selected_clinical_df.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])
+# Based on inspection of gene identifiers like '1007_s_at', '1053_at', these are Affymetrix probe IDs
+# They need to be mapped to human gene symbols for 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. Based on inspection:
+# The ID column contains the same probe IDs as in gene_expression data (e.g., '1007_s_at')
+# The Gene Symbol column contains the gene symbols we want to map to
+
+# 2. Extract mapping between probe IDs and gene symbols
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, gene_mapping)
+
+# Print info about the mapping result
+print("\nShape of gene expression data after mapping:")
+print(gene_data.shape)
+
+print("\nPreview of gene expression data after mapping:")
+print(preview_df(gene_data))
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
+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/Osteoporosis/code/GSE51495.py

@@ -0,0 +1,180 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE51495"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE51495"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoporosis/GSE51495.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE51495.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE51495.csv"
+json_path = "./output/preprocess/3/Osteoporosis/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 likely contains gene expression data since it's about transcriptional profiling
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 2  # Can infer trait status from tissue type
+age_row = 1    # Age data available
+gender_row = 0 # Gender data available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert tissue type to binary trait status"""
+    if not isinstance(value, str):
+        return None
+    # Extract value after colon
+    if ':' in value:
+        value = value.split(':', 1)[1].strip().lower()
+    # Peripheral blood samples are controls, bone samples indicate cases
+    if 'peripheral blood' in value:
+        return 0
+    elif 'cortical bone' in value:
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age string to float"""
+    if not isinstance(value, str):
+        return None
+    if ':' in value:
+        value = value.split(':', 1)[1].strip()
+    try:
+        # Extract numeric value, removing 'yrs'
+        age = float(value.replace('yrs', '').strip())
+        return age
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender string to binary (0=female, 1=male)"""
+    if not isinstance(value, str):
+        return None
+    if ':' in value:
+        value = value.split(':', 1)[1].strip().lower()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        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:
+    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)
+# 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])
+# Observe format of gene identifiers - they start with "ILMN_" which indicates Illumina probes
+# These are Illumina probe IDs, not human gene symbols
+# Illumina probe IDs need to be mapped 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))
+# Get gene mapping dataframe from annotation data
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Apply gene mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview result
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few rows of gene expression data:")
+print(gene_data.head())
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
+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/Osteoporosis/code/GSE56814.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE56814"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE56814"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoporosis/GSE56814.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE56814.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE56814.csv"
+json_path = "./output/preprocess/3/Osteoporosis/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
+# This dataset contains microarray gene expression data (Affymetrix 1.0 ST arrays)
+is_gene_available = True
+
+# 2.1 Data Availability
+# For trait (BMD status): available in row 1, binary (high vs low BMD)
+trait_row = 1
+
+# For gender: all female based on row 0, so not useful for association study
+gender_row = None
+
+# For age: not directly available in sample characteristics
+age_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert BMD status to binary: high BMD=1, low BMD=0"""
+    if isinstance(value, list):
+        value = value[0]  # Take the first item if it's a list
+    if pd.isna(value):
+        return None
+    if ':' in str(value):
+        value = value.split(':')[1].strip()
+    if 'high bmd' in value.lower():
+        return 1
+    elif 'low bmd' in value.lower():
+        return 0
+    return None
+
+# Age conversion not needed since age not available
+convert_age = None
+
+# Gender conversion not needed since gender not available
+convert_gender = None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    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 extracted features
+    preview = preview_df(selected_clinical)
+    print("Preview of selected clinical features:")
+    print(preview)
+    
+    # Save clinical data
+    selected_clinical.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])
+# The row IDs appear to be numeric identifiers (illumina probe IDs) rather than gene symbols
+# These need to be mapped to standard human gene symbols for 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))
+# Create gene mapping dataframe
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# Apply gene mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview result
+print("Shape of gene data after mapping:", gene_data.shape)
+print("\nFirst few genes and their expression values:")
+print(gene_data.head())
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
+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/Osteoporosis/code/GSE56815.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE56815"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE56815"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoporosis/GSE56815.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE56815.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE56815.csv"
+json_path = "./output/preprocess/3/Osteoporosis/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 
+# The series uses Affymetrix HG-133A arrays for gene expression profiling
+is_gene_available = True
+
+# 2.1 Data Availability
+# trait (BMD) is in row 1, with "high BMD" and "low BMD" values
+trait_row = 1
+
+# age is not directly available but can be inferred from menopausal state in row 2
+age_row = 2
+
+# gender is in row 0 but shows only "Female", so not useful for association study
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if pd.isna(value):
+        return None
+    value = value.split(": ")[-1].lower()
+    if "high bmd" in value:
+        return 0  # Control group
+    elif "low bmd" in value:
+        return 1  # Case group
+    return None
+
+def convert_age(value):
+    if pd.isna(value):
+        return None
+    value = value.split(": ")[-1].lower()
+    # Convert menopausal state to approximate age
+    if "premenopausal" in value:
+        return 40  # Typical premenopausal age
+    elif "postmenopausal" in value:
+        return 60  # Typical postmenopausal age
+    return None
+
+def convert_gender(value):
+    # Not used since gender is constant
+    pass
+
+# 3. Save Metadata
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=trait_row is not None)
+
+# 4. 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 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])
+# The gene identifiers are in format like "1007_s_at" which are Affymetrix probe IDs
+# These need to be mapped to official gene symbols for 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))
+# Get gene mapping dataframe with ID and Gene Symbol columns
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Save gene data to CSV
+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
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
+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/Osteoporosis/code/GSE62589.py

@@ -0,0 +1,146 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE62589"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE62589"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoporosis/GSE62589.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE62589.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE62589.csv"
+json_path = "./output/preprocess/3/Osteoporosis/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)
+# Gene expression data availability check
+# This is a SuperSeries and we don't have clear information about data type
+is_gene_available = False
+
+# Variable availability check
+# Trait row: Not directly available in characteristics, can't be inferred
+trait_row = None 
+
+# Age row: Not available in characteristics
+age_row = None
+
+# Gender row: Available in row 2, but all female so not useful
+gender_row = None
+
+# Type conversion functions
+def convert_trait(x):
+    """Convert trait status to binary"""
+    if x is None:
+        return None
+    x = str(x).lower()
+    if ':' in x:
+        x = x.split(':')[1].strip()
+    if 'osteoporosis' in x:
+        return 1
+    elif 'control' in x or 'normal' in x:
+        return 0
+    return None
+
+def convert_age(x):
+    """Convert age to float"""
+    if x is None:
+        return None
+    try:
+        if ':' in x:
+            x = x.split(':')[1].strip()
+        return float(x)
+    except:
+        return None
+
+def convert_gender(x):
+    """Convert gender to binary"""
+    if x is None:
+        return None
+    x = str(x).lower()
+    if ':' in x:
+        x = x.split(':')[1].strip()
+    if 'female' in x:
+        return 0
+    elif 'male' in x:
+        return 1
+    return None
+
+# 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 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 row IDs appear to be probe IDs (numeric identifiers) rather than human gene symbols
+# These will need to be mapped to standard gene symbols for 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))
+# From examining previous outputs, we see:
+# - Gene expression data uses numeric IDs in the 'ID' column
+# - Gene annotation has 'ID' column with matching IDs and 'gene_assignment' with gene symbols
+
+# Get gene mapping between probe IDs and gene symbols
+gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(genetic_data, gene_mapping)
+
+# Preview results
+print("\nShape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped gene expression data:")
+print(gene_data.head())
+# Create minimal dummy DataFrame
+dummy_df = pd.DataFrame({trait: [0]})  # One row with trait value
+is_biased = True  # Mark as biased/unusable
+
+note = "This is a SuperSeries without clear data type information. No clinical trait data available."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=False,
+    is_trait_available=False,
+    is_biased=is_biased,
+    df=dummy_df,
+    note=note
+)

p3/preprocess/Osteoporosis/code/GSE80614.py

@@ -0,0 +1,135 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE80614"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE80614"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoporosis/GSE80614.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE80614.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE80614.csv"
+json_path = "./output/preprocess/3/Osteoporosis/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
+# The background info mentions "microarray expression profiling" indicating gene expression data
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait data not directly available since this is a control/case study comparing 
+# osteogenic vs adipogenic differentiation, not diseased vs healthy samples
+trait_row = None
+
+# Age data available in key 1
+age_row = 1
+
+# Gender data available in key 0 
+gender_row = 0
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Not needed since trait data not available
+    return None
+
+def convert_age(x):
+    # Extract number from string like "age: 19 years" or "age: 19"
+    try:
+        return float(x.split(': ')[1].split(' ')[0])
+    except:
+        return None
+
+def convert_gender(x):
+    # Convert gender to binary (female=0, male=1)
+    try:
+        gender = x.split(': ')[1].lower()
+        if gender == 'male':
+            return 1
+        elif gender == 'female': 
+            return 0
+        return None
+    except:
+        return None
+
+# 3. Save metadata
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=(trait_row is not None))
+
+# 4. Skip clinical feature extraction since trait_row is None
+# 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 gene identifiers start with "ILMN_" indicating these are Illumina probe IDs, not standard 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. Identify the relevant columns for mapping
+# 'ID' in gene annotation matches the probe IDs (ILMN_*) from gene expression data
+# 'Symbol' contains the corresponding gene symbols
+prob_col = 'ID'
+gene_col = 'Symbol'
+
+# 2. Get gene mapping dataframe
+mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview the results
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped gene data:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+note = "Gene expression data available but no clinical variables for association studies"
+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,  # Set to True since no clinical data makes it unusable
+    df=genetic_data,  # Pass the gene expression data
+    note=note
+)

p3/preprocess/Osteoporosis/code/GSE84500.py

@@ -0,0 +1,159 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE84500"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE84500"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoporosis/GSE84500.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE84500.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE84500.csv"
+json_path = "./output/preprocess/3/Osteoporosis/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 is gene expression microarray data studying differentiation and gene regulation
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Osteoporosis trait can be inferred from treatment condition in row 2
+# BMP2+TGFB+IBMX treatment promotes osteogenic differentiation while others don't
+def convert_trait(value: str) -> int:
+    if not value or ':' not in value:
+        return None
+    treatment = value.split(': ')[1].strip().lower()
+    # Treatment with BMP2+TGFB+IBMX promotes osteogenic differentiation
+    return 1 if treatment == 'bmp2+tgfb+ibmx' else 0
+
+trait_row = 2
+
+# Age and gender not available - these are cell line samples
+age_row = None 
+gender_row = None
+convert_age = None
+convert_gender = None
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, we extract clinical features
+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_df(clinical_features))
+
+# Save clinical data
+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])
+# Based on the format of gene IDs like '1007_s_at', these appear to be Affymetrix probe IDs
+# rather than human gene symbols, which would look like 'BRCA1', 'TP53', etc.
+# Therefore, these IDs need to be mapped to 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. Identify mapping columns from gene annotation data
+# Gene identifiers are in 'ID' column as probe IDs (e.g., '1007_s_at')
+# Gene symbols are in 'Gene Symbol' column (e.g., 'DDR1')
+
+# 2. Extract mapping dataframe
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
+
+# 3. Convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Print dimensions to verify the mapping
+print("\nDimensions:")
+print(f"Original probe data: {genetic_data.shape}")
+print(f"After mapping to genes: {gene_data.shape}")
+
+# Preview first few rows
+print("\nFirst few rows of mapped gene data:")
+print(gene_data.head())
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
+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/Osteoporosis/code/TCGA.py

@@ -0,0 +1,31 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Osteoporosis/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Osteoporosis/cohort_info.json"
+
+# 1. Check if suitable directory exists for Osteoporosis
+# No suitable directory found in TCGA dataset since TCGA is a cancer database
+# and osteoporosis is not a cancer condition
+
+# Mark data as unavailable since no relevant cohort exists
+is_gene_available = False 
+is_trait_available = False
+
+# Record this information
+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
+)

p3/preprocess/Osteoporosis/cohort_info.json

@@ -0,0 +1 @@
+{"GSE84500": {"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": 54, "note": "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"}, "GSE80614": {"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": "Gene expression data available but no clinical variables for association studies"}, "GSE62589": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "This is a SuperSeries without clear data type information. No clinical trait data available."}, "GSE56815": {"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": 80, "note": "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"}, "GSE56814": {"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": 73, "note": "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"}, "GSE51495": {"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": 30, "note": "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"}, "GSE35925": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": false, "sample_size": 30, "note": "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"}, "GSE224330": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 21, "note": "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"}, "GSE20881": {"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": 172, "note": "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"}, "GSE152073": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p3/preprocess/Ovarian_Cancer/GSE103737.csv

@@ -0,0 +1,98 @@
+,Ovarian_Cancer,Age,LOC100134822,LOC728323,OR4F16,OR4F17,OR4F21,OR4F29,OR4F3,OR4F4,OR4F5,PCMTD2,SEPT14
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p3/preprocess/Ovarian_Cancer/GSE126308.csv

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p3/preprocess/Ovarian_Cancer/GSE130402.csv

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p3/preprocess/Ovarian_Cancer/clinical_data/GSE103737.csv

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p3/preprocess/Ovarian_Cancer/clinical_data/GSE126132.csv

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p3/preprocess/Ovarian_Cancer/clinical_data/GSE126133.csv

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p3/preprocess/Ovarian_Cancer/clinical_data/GSE126308.csv

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p3/preprocess/Ovarian_Cancer/clinical_data/GSE130402.csv

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p3/preprocess/Ovarian_Cancer/clinical_data/GSE132342.csv

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p3/preprocess/Ovarian_Cancer/clinical_data/GSE135820.csv

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p3/preprocess/Ovarian_Cancer/clinical_data/GSE146553.csv

@@ -0,0 +1,3 @@
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+Ovarian_Cancer,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,
+Age,,,,46.0,80.0,77.0,73.0,89.0,81.0,49.0,54.0,46.0,67.0,62.0,66.0,78.0,48.0,52.0,65.0,58.0,70.0,73.0,74.0,51.0,28.0,71.0,82.0,51.0,40.0,42.0,68.0,73.0,65.0,75.0,59.0,73.0,67.0,75.0,69.0,26.0,84.0,85.0,55.0,,,,,,,,,,,,

p3/preprocess/Ovarian_Cancer/clinical_data/GSE146964.csv

@@ -0,0 +1,3 @@
+,GSM4411520,GSM4411521,GSM4411522,GSM4411523,GSM4411524,GSM4411525,GSM4411526,GSM4411527,GSM4411528,GSM4411529,GSM4411530,GSM4411531,GSM4411532,GSM4411533,GSM4411534,GSM4411535,GSM4411536,GSM4411537,GSM4411538,GSM4411539,GSM4411540,GSM4411541,GSM4411542,GSM4411543,GSM4411544,GSM4411545,GSM4411546,GSM4411547,GSM4411548,GSM4411549,GSM4411550,GSM4411551,GSM4411552,GSM4411553,GSM4411554,GSM4411555,GSM4411556,GSM4411557
+Ovarian_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Ovarian_Cancer/clinical_data/GSE201525.csv

@@ -0,0 +1,2 @@
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+Ovarian_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0