p1/preprocess/Cardiovascular_Disease/code/GSE262419.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE262419"

# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE262419"

# Output paths
out_data_file = "./output/preprocess/1/Cardiovascular_Disease/GSE262419.csv"
out_gene_data_file = "./output/preprocess/1/Cardiovascular_Disease/gene_data/GSE262419.csv"
out_clinical_data_file = "./output/preprocess/1/Cardiovascular_Disease/clinical_data/GSE262419.csv"
json_path = "./output/preprocess/1/Cardiovascular_Disease/cohort_info.json"

# STEP1
from tools.preprocess import *

# 1. Attempt to identify the paths to the SOFT file and the matrix file
try:
    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
except AssertionError:
    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
    soft_file, matrix_file = None, None

if soft_file is None or matrix_file is None:
    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
else:
    # 2. Read the matrix file to obtain background information and sample characteristics data
    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
    background_info, clinical_data = get_background_and_clinical_data(matrix_file,
                                                                      background_prefixes,
                                                                      clinical_prefixes)

    # 3. Obtain the sample characteristics dictionary from the clinical dataframe
    sample_characteristics_dict = get_unique_values_by_row(clinical_data)

    # 4. Explicitly print out all the background information and the sample characteristics dictionary
    print("Background Information:")
    print(background_info)
    print("\nSample Characteristics Dictionary:")
    print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on the background info about transcriptomic profiling

# 2. Variable Availability
#   Checking if there's any row that records 'Cardiovascular_Disease', 'age', or 'gender' data.
#   Here, we see only "cell type: iPSC-Cardiomyocytes" and "treatment: ...",
#   so none of these variables are truly present or have multiple unique values.
trait_row = None
age_row = None
gender_row = None

# 2.2 Data Type Conversion (dummy converters returning None since no data)
def convert_trait(x):
    return None

def convert_age(x):
    return None

def convert_gender(x):
    return None

# 3. Save Metadata (initial filtering)
is_trait_available = (trait_row is not None)
is_usable = 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 (skip, because trait_row is None)
# STEP3
import gzip
import io
import pandas as pd

lines = []
found_begin = False

# Read lines between "!series_matrix_table_begin" and "!series_matrix_table_end"
with gzip.open(matrix_file, 'rt') as f:
    for line in f:
        # If we haven't found the start marker yet, check if it's in the current line
        if not found_begin:
            if "!series_matrix_table_begin" in line:
                found_begin = True
            continue
        # If we see the end marker, stop reading further
        if "!series_matrix_table_end" in line:
            break
        # Otherwise, collect this line to parse later
        lines.append(line)

# Attempt to parse the collected lines as a tab-delimited table
if lines:
    data_str = "".join(lines)
    gene_data = pd.read_csv(
        io.StringIO(data_str),
        sep="\t",
        on_bad_lines="skip"
    )
    # If there's an ID or ID_REF column, try to use it as index
    if "ID_REF" in gene_data.columns:
        gene_data.rename(columns={"ID_REF": "ID"}, inplace=True)
    if "ID" in gene_data.columns:
        gene_data["ID"] = gene_data["ID"].astype(str)
        gene_data.set_index("ID", inplace=True)
else:
    gene_data = pd.DataFrame()

# Print the first 20 row IDs (if any) to verify data structure
if not gene_data.empty:
    print("First 20 row IDs in the gene expression data:")
    print(gene_data.index[:20])
else:
    print("[INFO] The gene expression DataFrame is empty or no data lines were found.")