p3/preprocess/Alopecia/code/GSE18876.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Alopecia"
cohort = "GSE18876"

# Input paths
in_trait_dir = "../DATA/GEO/Alopecia"
in_cohort_dir = "../DATA/GEO/Alopecia/GSE18876"

# Output paths
out_data_file = "./output/preprocess/3/Alopecia/GSE18876.csv"
out_gene_data_file = "./output/preprocess/3/Alopecia/gene_data/GSE18876.csv"
out_clinical_data_file = "./output/preprocess/3/Alopecia/clinical_data/GSE18876.csv"
json_path = "./output/preprocess/3/Alopecia/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# Gene expression data availability
# Yes - the series title and summary mention transcriptional profiling using exon arrays
is_gene_available = True

# Variable rows and conversion functions
trait_row = None  # Cannot reliably determine alopecia status from characteristics
age_row = 0  # Age is in feature 0
gender_row = None  # Not needed since all samples are male based on background info

def convert_age(value):
    if not value or ':' not in value:
        return None
    try:
        age = int(value.split(':')[1].strip())
        return age
    except:
        return None

# Note: trait and gender conversion functions not needed since data not available
convert_trait = None  
convert_gender = None

# Save metadata for initial filtering
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)

# Skip clinical feature extraction since trait data is not available
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
requires_gene_mapping = True
# Extract gene annotation from SOFT file and get meaningful data 
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))

print("\nNumber of non-null values in each column:")
print(gene_annotation.count())

# Print example rows showing the mapping columns
print("\nSample mapping columns ('ID' and gene_assignment):")
print(gene_annotation[['ID', 'gene_assignment']].head().to_string())

print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers") 
print("'gene_assignment' column: Gene information")
# Get mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

# Apply gene mapping to convert probe IDs to gene symbols
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Normalize gene symbols 
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print shape and preview to verify mapping
print("Shape after mapping:", gene_data.shape)
print("\nPreview of mapped gene expression data:")
print(gene_data.head())
# Save normalized gene data
gene_data.to_csv(out_gene_data_file)

# Since trait data is not available, mark dataset as unusable
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,  
    df=gene_data,
    note="Dataset lacks trait information required for analysis"
)