p3/preprocess/Stroke/code/GSE125771.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Stroke"
cohort = "GSE125771"

# Input paths
in_trait_dir = "../DATA/GEO/Stroke"
in_cohort_dir = "../DATA/GEO/Stroke/GSE125771"

# Output paths
out_data_file = "./output/preprocess/3/Stroke/GSE125771.csv"
out_gene_data_file = "./output/preprocess/3/Stroke/gene_data/GSE125771.csv"
out_clinical_data_file = "./output/preprocess/3/Stroke/clinical_data/GSE125771.csv"
json_path = "./output/preprocess/3/Stroke/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")
# 1. Gene Expression Data Availability
# Based on Series_summary and methods description, this is a gene expression microarray dataset
is_gene_available = True

# 2.1 Data Availability
# Trait: Controls not found - all samples are carotid atherosclerotic plaques from patients with >50% stenosis
trait_row = None  

# Age: Feature 3 contains age data
age_row = 3

# Gender: Feature 2 contains sex data
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Not used since trait_row is None
    return None

def convert_age(x):
    # Extract numeric age value after colon
    try:
        age = float(x.split(': ')[1])
        return age
    except:
        return None

def convert_gender(x):
    # Convert sex to binary (Female=0, Male=1)
    try:
        sex = x.split(': ')[1].strip()
        if sex == 'Female':
            return 0
        elif sex == 'Male':
            return 1
        return None
    except:
        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=False  # trait_row is None
)

# 4. Skip clinical feature extraction since trait_row is None
# 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)
# The identifiers (e.g. TC01000001.hg.1) appear to be probe IDs from a microarray platform
# rather than standard human gene symbols. They need to be mapped to gene symbols.
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview annotation data to identify columns for mapping
print("Gene Annotation Preview (first 5 rows):")
print(preview_df(gene_metadata))

# From the output we can see:
# - 'ID' column contains probe IDs that match our expression data
# - 'gene_assignment' column has gene symbol information 
# We'll use these columns for mapping probe IDs to gene symbols

# Keep columns needed for mapping
mapping_cols = ['ID', 'gene_assignment'] 
gene_metadata = gene_metadata[mapping_cols]

# Print shape of annotation data
print(f"\nShape of gene annotation data: {gene_metadata.shape}")
# Extract ID and gene symbol mapping
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')

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

# Print shape and preview data to verify mapping worked
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data) 
gene_data.to_csv(out_gene_data_file)

# Create a minimal DataFrame with gene data and set trait as missing
linked_data = pd.DataFrame(index=gene_data.columns)
linked_data[trait] = None

# Add gene expression data
linked_data = pd.concat([linked_data.T, gene_data]).T

# Set bias flag for validation
trait_biased = True  # Missing trait data means it cannot be used for trait analysis

# Validate and record dataset info
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=trait_biased,
    df=linked_data,
    note="Gene expression data available but no stroke phenotype information found in dataset."
)