p3/preprocess/Hypertension/code/GSE151158.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hypertension"
cohort = "GSE151158"

# Input paths
in_trait_dir = "../DATA/GEO/Hypertension"
in_cohort_dir = "../DATA/GEO/Hypertension/GSE151158"

# Output paths
out_data_file = "./output/preprocess/3/Hypertension/GSE151158.csv"
out_gene_data_file = "./output/preprocess/3/Hypertension/gene_data/GSE151158.csv"
out_clinical_data_file = "./output/preprocess/3/Hypertension/clinical_data/GSE151158.csv"
json_path = "./output/preprocess/3/Hypertension/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 summary, this dataset studies transcriptional changes and gene expression
# of 594 genes in liver tissue, so gene expression data is available
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Find row indices
# Trait (Hypertension) data is available in row 7
trait_row = 7
# Age data is available in row 1
age_row = 1  
# Gender data is available in row 2
gender_row = 2

# 2.2 Data type conversion functions
def convert_trait(x):
    if pd.isna(x):
        return None
    # Extract value after colon and strip whitespace
    value = x.split(":")[1].strip()
    # Convert to binary: Y->1, N->0
    if value == "Y":
        return 1
    elif value == "N":
        return 0
    return None

def convert_age(x):
    if pd.isna(x):
        return None
    # Extract value after colon and convert to float
    try:
        value = float(x.split(":")[1].strip())
        return value
    except:
        return None

def convert_gender(x):
    if pd.isna(x):
        return None
    # Extract value after colon and strip whitespace
    value = x.split(":")[1].strip()
    # Convert to binary: F->0, M->1
    if value == "F":
        return 0
    elif value == "M":
        return 1
    return None

# 3. 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)

# 4. Clinical Feature Extraction 
# Since trait_row is not None, extract clinical features from the existing clinical_data
selected_clinical_df = 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 data
print(preview_df(selected_clinical_df))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical_df.to_csv(out_clinical_data_file)
# 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 gene identifiers appear to be standard human gene symbols (e.g. ABCB1, ABCF1, ABL1)
# They match official HUGO gene nomenclature committee (HGNC) symbols
# No mapping needed - these are already canonical gene symbols

requires_gene_mapping = False
# 1. Load clinical data and save normalized gene data
selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
gene_data.index = gene_data.index.str.replace('-mRNA', '')
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. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)

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

# 4. Check for biased features and remove them if needed 
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 comparing transcriptional profiles between idiopathic non-cirrhotic portal hypertension patients, cirrhosis patients, and normal controls"
)

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