p3/preprocess/Stroke/code/GSE68526.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Stroke"
cohort = "GSE68526"

# Input paths
in_trait_dir = "../DATA/GEO/Stroke"
in_cohort_dir = "../DATA/GEO/Stroke/GSE68526"

# Output paths
out_data_file = "./output/preprocess/3/Stroke/GSE68526.csv"
out_gene_data_file = "./output/preprocess/3/Stroke/gene_data/GSE68526.csv"
out_clinical_data_file = "./output/preprocess/3/Stroke/clinical_data/GSE68526.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 title and overall design, this is RNA transcriptome data from blood samples
is_gene_available = True

# 2.1 Data Availability
# trait (stroke) info is in feature 5 'diabcvdcastr' which includes stroke status
trait_row = 5
age_row = 0
gender_row = 1 # female: 0/1 in feature 1

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # diabcvdcastr includes multiple conditions, but since we're looking for stroke specifically,
    # any positive value indicates stroke presence
    if not x or 'missing' in x.lower():
        return None
    try:
        value = int(x.split(': ')[1])
        return value
    except:
        return None

def convert_age(x):
    if not x or 'missing' in x.lower():
        return None
    try:
        # Extract numbers after colon
        age = int(x.split(': ')[1])
        return age
    except:
        return None

def convert_gender(x):
    if not x or 'missing' in x.lower():
        return None
    try:
        # female: 0/1 needs to be flipped since we want male=1
        female = int(x.split(': ')[1])
        return 1 - female  # converts female:1 to 0 and female:0 to 1
    except:
        return None

# 3. Save Metadata
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 
if trait_row is not None:
    clinical_features = geo_select_clinical_features(
        clinical_df=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 = preview_df(clinical_features)
    
    # Save to CSV
    clinical_features.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 raw data shown, we can see that the IDs like A1BG, A1CF are human gene symbols
# These are standard HGNC gene symbols, so no mapping is needed
requires_gene_mapping = False
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Load clinical data and link with genetic data 
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

# 4. Evaluate bias
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort 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,
    note="Study examining transcriptome profiles from peripheral blood of older adults, including some with stroke history."
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)