p3/preprocess/Psoriasis/code/GSE226244.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriasis"
cohort = "GSE226244"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE226244"

# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE226244.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE226244.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE226244.csv"
json_path = "./output/preprocess/3/Psoriasis/cohort_info.json"

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

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# 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")
# 1. Gene Expression Data Availability
# Based on the background info mentioning "microarray analysis", this dataset contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# For trait (psoriasis status):
# Feature 0 contains disease state info
trait_row = 0

def convert_trait(x):
    if not isinstance(x, str):
        return None
    val = x.split(': ')[-1].strip()
    if val == 'Psoriasis':
        return 1
    elif val == 'Control':
        return 0
    return None

# Age and gender not available in sample characteristics
age_row = None
gender_row = None

def convert_age(x):
    return None

def convert_gender(x): 
    return None

# 3. Save metadata
# Initial filtering based on gene and trait availability
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
clinical_df = 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
print("Preview of clinical features:")
print(preview_df(clinical_df))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 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)
# Based on the format of gene IDs (e.g., '1007_s_at', '1053_at', '117_at'), these appear to be 
# Affymetrix probe IDs rather than standard human gene symbols.
# They will need to be mapped to gene symbols for downstream analysis.
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Extract gene mapping data
# 'ID' column in the gene annotation contains probe IDs matching gene expression data
# 'Gene Symbol' column contains gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')

# Map probe-level data to gene expressions
gene_data = apply_gene_mapping(gene_data, mapping_df)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2-6. Handle clinical data, linking and saving
if clinical_df is not None and not clinical_df.empty:
    # Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

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

    # Check for bias
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # Validate and save info
    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
    )

    # Save if usable
    if is_usable:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)

else:
    # Record that clinical data is not available
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=None,
        df=gene_data,
        note="Contains gene expression data but lacks valid clinical information needed for trait association studies."
    )