p3/preprocess/Kidney_Chromophobe/code/GSE40914.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Chromophobe"
cohort = "GSE40914"

# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Chromophobe"
in_cohort_dir = "../DATA/GEO/Kidney_Chromophobe/GSE40914"

# Output paths
out_data_file = "./output/preprocess/3/Kidney_Chromophobe/GSE40914.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Chromophobe/gene_data/GSE40914.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Chromophobe/clinical_data/GSE40914.csv"
json_path = "./output/preprocess/3/Kidney_Chromophobe/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)

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on background info, this is a microarray dataset studying gene expression
# including lncRNAs in RCC, so gene data should be available
is_gene_available = True

# 2. Data Availability and Type Conversion
# Sample characteristics show limited data - disease, tissue, and sample type
# 2.1 Data Availability 
# trait_row - Can infer case/control from "tissue" field (row 1)
trait_row = 1
# age_row - Not available
age_row = None  
# gender_row - Not available
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert tissue field to binary trait"""
    if not isinstance(value, str):
        return None
    # Extract value after colon
    if ':' in value:
        value = value.split(':', 1)[1].strip()
    # Map tissue type to binary - tumor (1) vs non-tumor (0)
    if 'tumor' in value.lower():
        return 1
    return 0

# No age/gender conversion functions needed since data not available
convert_age = None
convert_gender = None

# 3. Save Metadata
# Initial validation based on gene and trait availability
is_validated = 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, extract clinical features
if trait_row is not None:
    clinical_features = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save clinical features
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())

# Look at general data statistics 
print("\nData shape:", gene_metadata.shape)

# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# 1. In gene expression data, row IDs are simple numbers like "1", "2", etc.
# From annotation preview, 'ID' contains the same identifiers
# 'GENE_SYMBOL' contains the target gene symbols we want to map to
prob_col = 'ID'
gene_col = 'GENE_SYMBOL'

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)

# Convert expression values to numeric type, handling 'Null' values
genetic_data = genetic_data.replace(['Null', 'null', 'NULL'], pd.NA)
numeric_cols = genetic_data.columns
genetic_data[numeric_cols] = genetic_data[numeric_cols].astype(float)

# 3. Convert probe measurements to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Print dimensions of output
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few gene symbols:", gene_data.index[:5].tolist())
# 1. In gene expression data, row IDs are simple numbers like "1", "2", etc.
# From annotation preview, 'ID' contains the same identifiers
# 'GENE_SYMBOL' contains the target gene symbols we want to map to
prob_col = 'ID'
gene_col = 'GENE_SYMBOL'

# 2. Get gene mapping dataframe 
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)

# Convert expression values to numeric type
numeric_cols = genetic_data.columns
genetic_data[numeric_cols] = genetic_data[numeric_cols].apply(pd.to_numeric, errors='coerce')

# 3. Convert probe measurements to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Print dimensions of output
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few gene symbols:", gene_data.index[:5].tolist())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Get clinical features 
clinical_features = geo_select_clinical_features(
    clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    age_row=age_row,
    convert_age=convert_age,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# 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)

# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
    is_biased = True
    linked_data = None
else:
    # 4. Judge whether features are biased and remove biased demographic features
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples from various tissue types including kidney, lung, stomach and other organs, with both tumor and normal tissues."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note=note
)

# 6. Save the linked data only if it's usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)