output/preprocess/Depression/code/GSE273630.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE273630"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE273630"

# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE273630.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE273630.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE273630.csv"
json_path = "./output/z2/preprocess/Depression/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
# Based on Nanostring digital transcript panel for inflammatory genes -> gene expression available
is_gene_available = True

# 2. Variable availability and data type conversion

# No usable clinical keys for trait/age/gender found in the sample characteristics.
# Background indicates all participants are male (constant -> not usable). Age not present as a field.
trait_row = None
age_row = None
gender_row = None

def _extract_value(x):
    if x is None:
        return None
    if isinstance(x, (int, float)):
        return x
    s = str(x)
    # take substring after the last colon if present
    parts = s.split(":")
    val = parts[-1].strip() if len(parts) > 1 else s.strip()
    return val if val != "" else None

# Depression (trait): choose binary mapping if present
def convert_trait(x):
    val = _extract_value(x)
    if val is None:
        return None
    v = str(val).strip().lower()
    # common positive indicators
    pos = {"depression", "depressed", "mdd", "major depressive disorder", "case", "patient", "yes", "mds"}
    neg = {"control", "healthy", "non-depressed", "no depression", "no", "hc"}
    if v in pos:
        return 1
    if v in neg:
        return 0
    # heuristic patterns
    if "depress" in v or "mdd" in v:
        return 1
    if "control" in v or "healthy" in v:
        return 0
    return None  # unknown or non-depression-related field

# Age: continuous
def convert_age(x):
    val = _extract_value(x)
    if val is None:
        return None
    v = str(val).lower()
    nums = re.findall(r"\d+\.?\d*", v)
    if not nums:
        return None
    try:
        age_val = float(nums[0])
        if 0 < age_val < 120:
            return age_val
    except Exception:
        return None
    return None

# Gender: binary female->0, male->1
def convert_gender(x):
    val = _extract_value(x)
    if val is None:
        return None
    v = str(val).strip().lower()
    if v in {"male", "m", "man", "boy"}:
        return 1
    if v in {"female", "f", "woman", "girl"}:
        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 (skip because trait_row is None)
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_df(selected_clinical_df)
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)