output/preprocess/Atrial_Fibrillation/code/GSE143924.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE143924"

# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE143924"

# Output paths
out_data_file = "./output/z1/preprocess/Atrial_Fibrillation/GSE143924.csv"
out_gene_data_file = "./output/z1/preprocess/Atrial_Fibrillation/gene_data/GSE143924.csv"
out_clinical_data_file = "./output/z1/preprocess/Atrial_Fibrillation/clinical_data/GSE143924.csv"
json_path = "./output/z1/preprocess/Atrial_Fibrillation/cohort_info.json"


# Step 1: Initial Data Loading
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 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("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)

# Step 2: Dataset Analysis and Clinical Feature Extraction
import os
import re
import pandas as pd

# 1) Gene expression availability
is_gene_available = True  # Transcriptome analysis from EAT indicates gene expression data is present.

# 2) Variable availability and converters
# From Sample Characteristics Dictionary:
# {0: ['tissue: epicardial adipose tissue'],
#  1: ['patient diagnosis: sinus rhythm after surgery', 'patient diagnosis: postoperative atrial fibrillation after surgery (POAF)']}
trait_row = 1  # POAF vs SR after surgery
age_row = None
gender_row = None

def convert_trait(x):
    if pd.isna(x):
        return None
    s = str(x)
    # Extract value after colon if present
    if ':' in s:
        s = s.split(':', 1)[1]
    s = s.strip().lower()
    # Remove parenthetical content
    s = re.sub(r'\(.*?\)', '', s).strip()
    # Map to binary: POAF=1, SR=0
    if any(k in s for k in ['postoperative atrial fibrillation', 'atrial fibrillation', 'poaf']):
        return 1
    if any(k in s for k in ['sinus rhythm', 'sr']):
        return 0
    return None

def convert_age(x):
    # Not available; generic parser if ever needed
    if pd.isna(x):
        return None
    s = str(x)
    if ':' in s:
        s = s.split(':', 1)[1]
    s = s.strip().lower()
    # Extract first number as age
    m = re.search(r'(\d+(\.\d+)?)', s)
    if m:
        try:
            return float(m.group(1))
        except Exception:
            return None
    return None

def convert_gender(x):
    # Not available; generic parser if ever needed
    if pd.isna(x):
        return None
    s = str(x)
    if ':' in s:
        s = s.split(':', 1)[1]
    s = s.strip().lower()
    if s in ['male', 'm']:
        return 1
    if s in ['female', 'f']:
        return 0
    return None

# 3) Save metadata (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 (only if trait data available)
if trait_row is not None:
    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 = preview_df(selected_clinical_df, n=5)
    print(preview)

    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)

# Step 3: Gene Data Extraction
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])

# Step 4: Gene Identifier Review
print("requires_gene_mapping = False")

# Step 5: Data Normalization and Linking
import os
import pandas as pd

# Ensure matrix file and gene_data are available
try:
    matrix_file  # noqa: F401
except NameError:
    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

try:
    gene_data  # noqa: F401
except NameError:
    gene_data = get_genetic_data(matrix_file)

# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
# Load clinical data from previous step if not in memory
try:
    selected_clinical_df  # noqa: F401
    clinical_df = selected_clinical_df
except NameError:
    clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

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

# 4. Bias check and remove biased demographic features
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation 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_trait_biased,
    df=unbiased_linked_data,
    note="INFO: Gene symbols normalized using NCBI synonym mapping; clinical features include the target trait only."
)

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