output/preprocess/Arrhythmia/code/GSE47727.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE47727"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE47727"

# Output paths
out_data_file = "./output/z1/preprocess/Arrhythmia/GSE47727.csv"
out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE47727.csv"
out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE47727.csv"
json_path = "./output/z1/preprocess/Arrhythmia/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 re

# 1) Gene expression data availability
# Platform: Illumina HumanHT-12 v3.0 gene expression microarray -> gene data available
is_gene_available = True

# 2) Variable availability and conversion functions

# Trait (Arrhythmia) availability:
# Sample characteristics only include age, gender, and tissue; no disease status.
# Background indicates "control participants" only -> trait not variable (constant/absent).
trait_row = None  # Not available

# Age availability
age_row = 0  # 'age (yrs): ...'

# Gender availability
gender_row = 1  # 'gender: female' / 'gender: male'

# Conversion functions
def convert_trait(x):
    # Generic mapper for Arrhythmia/AF presence; not used here since trait_row is None.
    if x is None:
        return None
    s = str(x)
    if ':' in s:
        s = s.split(':', 1)[1]
    v = s.strip().lower()
    # Positive mappings
    pos_terms = {
        'arrhythmia', 'atrial fibrillation', 'af', 'yes', 'present', 'case', '1', 'true', 'y'
    }
    neg_terms = {
        'no arrhythmia', 'no af', 'none', 'no', 'absent', 'control', '0', 'false', 'n', 'healthy', 'normal'
    }
    if v in pos_terms:
        return 1
    if v in neg_terms:
        return 0
    # Heuristics for strings containing keywords
    if any(k in v for k in ['atrial fibrillation', 'af', 'arrhythmia']):
        # If explicitly negated, map to 0
        if any(k in v for k in ['no ', 'absent', 'without', 'free of']):
            return 0
        return 1
    return None

def convert_age(x):
    if x is None:
        return None
    s = str(x)
    if ':' in s:
        s = s.split(':', 1)[1]
    s = s.strip()
    m = re.search(r'(\d+(\.\d+)?)', s)
    if not m:
        return None
    try:
        return float(m.group(1))
    except Exception:
        return None

def convert_gender(x):
    if x is None:
        return None
    s = str(x)
    if ':' in s:
        s = s.split(':', 1)[1]
    v = s.strip().lower()
    if v in ['female', 'f', '0']:
        return 0
    if v in ['male', 'm', '1']:
        return 1
    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 (skip because trait_row is None)
# If in future a trait field is identified, uncomment the following block:
# if trait_row is not None:
#     selected_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_df, n=5)
#     os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
#     selected_df.to_csv(out_clinical_data_file)