p3/preprocess/Post-Traumatic_Stress_Disorder/code/GSE63878.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Post-Traumatic_Stress_Disorder"
cohort = "GSE63878"

# Input paths
in_trait_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder"
in_cohort_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder/GSE63878"

# Output paths
out_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/GSE63878.csv"
out_gene_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/gene_data/GSE63878.csv"
out_clinical_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/clinical_data/GSE63878.csv"
json_path = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/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, it's microarray data for gene expression, so:
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Identify rows containing each variable
trait_row = 1  # The trait info is in row 1 under 'condition'
age_row = None  # Age not available
gender_row = None  # Gender not available 

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert PTSD status to binary (0: control, 1: case)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'control' in value:
        return 0
    elif 'case' in value:
        return 1
    return None

convert_age = None  # Not needed since age data unavailable
convert_gender = None  # Not needed since gender data unavailable

# 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_df = geo_select_clinical_features(clinical_data,
                                             trait=trait,
                                             trait_row=trait_row,
                                             convert_trait=convert_trait)
    
    # Preview the processed clinical data
    print("Preview of processed clinical data:")
    print(preview_df(clinical_df))
    
    # Save clinical data
    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 examining the gene identifiers (numeric probe IDs like 7896740), 
# these appear to be Illumina probe IDs rather than human gene symbols,
# so they will 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. Extract the mapping info
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# Preview the gene data
print("\nPreview of gene data after mapping:")
print(preview_df(gene_data))

# Save gene 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 = "Dataset contains subcutaneous adipose tissue gene expression data from PCOS patients and controls. The gender feature is biased (all female) and was removed."
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)