p3/preprocess/Glucocorticoid_Sensitivity/code/GSE57795.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Glucocorticoid_Sensitivity"
cohort = "GSE57795"

# Input paths
in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE57795"

# Output paths
out_data_file = "./output/preprocess/3/Glucocorticoid_Sensitivity/GSE57795.csv"
out_gene_data_file = "./output/preprocess/3/Glucocorticoid_Sensitivity/gene_data/GSE57795.csv"
out_clinical_data_file = "./output/preprocess/3/Glucocorticoid_Sensitivity/clinical_data/GSE57795.csv"
json_path = "./output/preprocess/3/Glucocorticoid_Sensitivity/cohort_info.json"

# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# Yes, it contains gene expression data using Illumina HumanWG-6 BeadChips
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 5  # Dexamethasone sensitivity data is in row 5
age_row = None  # No patient age data available
gender_row = None  # No gender data available 

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.lower()
    if 'sensitive' in value:
        return 1  # Good responders coded as 1
    elif 'resistant' in value:
        return 0  # Poor responders coded as 0
    return None

def convert_age(value):
    return None  # Not needed since age data not available

def convert_gender(value):
    return None  # Not needed since gender data not available

# 3. Save Metadata
# Initial filtering - trait data is available (trait_row is not None)
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)

# 4. Clinical Feature Extraction
# Since trait_row is not None, we proceed with clinical feature extraction
clinical_features = geo_select_clinical_features(clinical_data, 
                                               trait=trait,
                                               trait_row=trait_row,
                                               convert_trait=convert_trait,
                                               age_row=age_row,
                                               convert_age=convert_age,
                                               gender_row=gender_row,
                                               convert_gender=convert_gender)

# Preview the extracted features
preview_result = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview_result)

# Save clinical features
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These are Illumina probe IDs (starting with "ILMN_"), not human gene symbols
# They need to be mapped to gene symbols for interpretable analysis
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# From examining the output, we can see:
# - Gene expression data uses IDs like 'ILMN_1343291'
# - In gene annotation, 'ID' column contains the same ILMN identifiers
# - 'Symbol' column contains the gene symbols we want to map to

# Get mapping between probe IDs and gene symbols
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') 

# Apply mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, gene_mapping)

# Preview converted gene expression data
print("\nFirst few genes and their expression values:")
print(gene_data.head())
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# Read the processed clinical data file 
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Link clinical and genetic data using the normalized gene data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

# Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)

# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = "Gene expression data from glucocorticoid sensitivity study."
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
)

# Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")