p3/preprocess/Irritable_bowel_syndrome_(IBS)/code/GSE36701.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Irritable_bowel_syndrome_(IBS)"
cohort = "GSE36701"

# Input paths
in_trait_dir = "../DATA/GEO/Irritable_bowel_syndrome_(IBS)"
in_cohort_dir = "../DATA/GEO/Irritable_bowel_syndrome_(IBS)/GSE36701"

# Output paths
out_data_file = "./output/preprocess/3/Irritable_bowel_syndrome_(IBS)/GSE36701.csv"
out_gene_data_file = "./output/preprocess/3/Irritable_bowel_syndrome_(IBS)/gene_data/GSE36701.csv"
out_clinical_data_file = "./output/preprocess/3/Irritable_bowel_syndrome_(IBS)/clinical_data/GSE36701.csv"
json_path = "./output/preprocess/3/Irritable_bowel_syndrome_(IBS)/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
is_gene_available = True  # Based on background info mentioning "gene expression analysis"

# 2.1 Data row identification
trait_row = 3  # 'disease' field contains IBS status
gender_row = 1  # 'gender' field
age_row = None  # Age data not available

# 2.2 Data type conversion functions
def convert_trait(value: str) -> int:
    """Convert IBS status to binary: 1 for IBS/post-infection, 0 for healthy"""
    if not value or ':' not in value:
        return None
    val = value.split(':')[1].strip()
    if val == 'HV':  # Healthy volunteer
        return 0
    elif val in ['IBS-D', 'IBS-C', 'PIBD', 'PIBS']:  # IBS cases
        return 1
    elif val == 'PINIBS':  # Post-infection no IBS = healthy
        return 0
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary: 0 for female, 1 for male"""
    if not value or ':' not in value:
        return None
    val = value.split(':')[1].strip()
    if val == 'F':
        return 0
    elif val == 'M':
        return 1
    return None

# 3. Save metadata
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=trait_row is not None
)

# 4. Extract clinical features
selected_clinical_df = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# Preview and save clinical data
print("Preview of selected clinical features:")
print(preview_df(selected_clinical_df))

selected_clinical_df.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())
# These IDs appear to be Affymetrix probe IDs from the HG-U133 microarray platform
# They are not standard human gene symbols and will need to be mapped
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))
# Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')

# Apply gene mapping to convert probe measurements to gene expression
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview output
print("Original shape:", genetic_data.shape)
print("Mapped shape:", gene_data.shape)
print("\nFirst 10 gene symbols:")
print(gene_data.index[:10].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,
    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 contains IBS case-control gene expression data from rectal mucosal biopsies."
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)