output/preprocess/Arrhythmia/code/GSE136992.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE136992"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE136992"

# Output paths
out_data_file = "./output/z1/preprocess/Arrhythmia/GSE136992.csv"
out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE136992.csv"
out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE136992.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
import math

# 1) Gene expression data availability
# Background indicates "mRNA expression ... Illumina whole genome gene expression DASL HT assay" -> gene data available.
is_gene_available = True

# 2) Variable availability and conversion functions

# Trait (Arrhythmia): Not available in this dataset; "condition" is Infection vs Control and does not indicate arrhythmia.
trait_row = None

# Age: Available at row 2 (values like "age: 0.5 weeks", etc.)
age_row = 2

# Gender: Available at row 3 ("gender: male/female")
gender_row = 3

def convert_trait(x):
    # Trait not available; return None for any input
    return None

def _extract_value_after_colon(x):
    if x is None:
        return None
    s = str(x)
    if ':' in s:
        return s.split(':', 1)[1].strip()
    return s.strip()

def convert_age(x):
    """
    Convert age string like 'age: 12 weeks' into a float number of weeks.
    Unknown or unparsable values -> None.
    """
    val = _extract_value_after_colon(x)
    if val is None:
        return None
    v = val.lower().strip()
    # Accept numbers possibly with unit; default unit weeks if not specified
    # Handle common units
    m = re.match(r'^([0-9]*\.?[0-9]+)\s*(week|weeks|wk|wks|day|days|d|month|months|mo|year|years|yr|yrs)?$', v)
    if not m:
        return None
    num = float(m.group(1))
    unit = m.group(2) if m.group(2) else 'weeks'
    unit = unit.lower()
    # Convert all to weeks
    if unit in ['week', 'weeks', 'wk', 'wks']:
        weeks = num
    elif unit in ['day', 'days', 'd']:
        weeks = num / 7.0
    elif unit in ['month', 'months', 'mo']:
        weeks = num * (365.25 / 12.0) / 7.0
    elif unit in ['year', 'years', 'yr', 'yrs']:
        weeks = num * 52.17857  # approx
    else:
        weeks = num  # default to weeks
    return weeks

def convert_gender(x):
    """
    Convert gender to binary: female -> 0, male -> 1; unknown -> None.
    """
    val = _extract_value_after_colon(x)
    if val is None:
        return None
    g = val.strip().lower()
    if g in ['female', 'f']:
        return 0
    if g in ['male', 'm']:
        return 1
    return None

# 3) Save metadata with 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: skipped because trait_row is None