output/preprocess/Cystic_Fibrosis/code/GSE53543.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE53543"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE53543"

# Output paths
out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE53543.csv"
out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE53543.csv"
out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE53543.csv"
json_path = "./output/z2/preprocess/Cystic_Fibrosis/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
is_gene_available = True  # Illumina HumanHT-12 v4 Expression BeadChip indicates mRNA gene expression data

# 2) Variable availability based on the provided Sample Characteristics Dictionary
# Keys observed:
# 0: subject id
# 1: gender
# 2: sample group (Uninfected / RV_infected) - experimental condition, not the human trait
# 3: cell type (constant)
# 4: treated with (experimental condition)
trait_row = None      # No cystic fibrosis status available; treat as not available
age_row = None        # No age information
gender_row = 1        # Gender available

# 2.2) Converters
def _after_colon(value: str) -> str:
    if value is None:
        return ""
    parts = str(value).split(":", 1)
    return parts[1].strip() if len(parts) == 2 else str(value).strip()

def convert_trait(value):
    # Binary: 1 = cystic fibrosis, 0 = non-cystic fibrosis
    v = _after_colon(value).lower()
    if not v:
        return None
    # Heuristics for CF status if ever present
    # Positive indicators
    pos_patterns = [
        r"\bcystic fibrosis\b", r"\bcf\b", r"\bpatient\b", r"\bdisease\b\s*[:=]?\s*(cf|cystic fibrosis)",
        r"\bcase\b", r"\baffected\b"
    ]
    # Negative indicators
    neg_patterns = [
        r"\bcontrol\b", r"\bhealthy\b", r"\bnon-?cf\b", r"\bno cystic fibrosis\b",
        r"\bunaffected\b"
    ]
    if any(re.search(p, v) for p in pos_patterns):
        # Exclude clear negatives overriding positives
        if any(re.search(p, v) for p in neg_patterns):
            return 0
        return 1
    if any(re.search(p, v) for p in neg_patterns):
        return 0
    # Explicit yes/no
    if v in {"yes", "y", "true", "1"}:
        return 1
    if v in {"no", "n", "false", "0"}:
        return 0
    return None

def convert_age(value):
    # Continuous age in years; extract first float-like number
    v = _after_colon(value).lower()
    if not v or v in {"na", "n/a", "nan", "none", "unknown", "missing"}:
        return None
    m = re.search(r"(-?\d+(?:\.\d+)?)", v)
    if not m:
        return None
    try:
        age = float(m.group(1))
        if age < 0 or age > 120:
            return None
        return age
    except Exception:
        return None

def convert_gender(value):
    # Binary: female=0, male=1
    v = _after_colon(value).strip().lower()
    if v in {"female", "f", "woman", "women", "girl"}:
        return 0
    if v in {"male", "m", "man", "men", "boy"}:
        return 1
    return None

# 3) Initial filtering and save metadata
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 since trait_row is None)
if is_trait_available:
    selected = 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, n=5)
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected.to_csv(out_clinical_data_file)