p3/preprocess/Stroke/code/GSE186798.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Stroke"
cohort = "GSE186798"

# Input paths
in_trait_dir = "../DATA/GEO/Stroke"
in_cohort_dir = "../DATA/GEO/Stroke/GSE186798"

# Output paths
out_data_file = "./output/preprocess/3/Stroke/GSE186798.csv"
out_gene_data_file = "./output/preprocess/3/Stroke/gene_data/GSE186798.csv"
out_clinical_data_file = "./output/preprocess/3/Stroke/clinical_data/GSE186798.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
is_gene_available = True  # Based on background info mentioning microarray analysis

# 2.1 Data Availability
trait_row = 1  # 'condition' row contains stroke/control status
gender_row = 0  # 'gender' row contains gender info
age_row = None  # Age information not available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].strip()
    if value == 'Control':
        return 0
    elif value in ['PSND', 'PSD']:  # Both are post-stroke cases
        return 1
    return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].strip()
    if value == 'F':
        return 0
    elif value == 'M':
        return 1
    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=trait_row is not None)

# 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,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the processed clinical data
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save clinical features
    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)
# From the identifiers visible in the first few rows (e.g., "AFFX-BkGr-GC03_st"),
# these appear to be Affymetrix probe IDs rather than standard human gene symbols.
# They need to be mapped to their corresponding gene symbols.
requires_gene_mapping = True
# From looking at the annotation data, we can see this is mouse data (Mus musculus) 
# rather than human data. This makes the dataset unsuitable for human stroke studies.
# Therefore we need to stop processing this cohort.

# Save metadata indicating this dataset is not usable
validate_and_save_cohort_info(is_final=False,
                           cohort=cohort, 
                           info_path=json_path,
                           is_gene_available=False, # Set to False since mouse data can't be used
                           is_trait_available=True, # We did find stroke/control data
                           note="Dataset contains mouse rather than human gene expression data")

# Exit further processing as dataset is not suitable
print("WARNING: This dataset contains mouse gene expression data rather than human data.")
print("Stopping processing as mouse data is not suitable for human stroke studies.")