output/preprocess/Adrenocortical_Cancer/code/GSE76019.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE76019"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE76019"

# Output paths
out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE76019.csv"
out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE76019.csv"
out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE76019.csv"
json_path = "./output/z1/preprocess/Adrenocortical_Cancer/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  # Background indicates gene expression microarrays were used.

# 2. Variable Availability
# Based on the provided Sample Characteristics Dictionary:
# 0: histology: ACC (constant)
# 1: Stage: I/II/III/IV
# 2: efs.time
# 3: efs.event
# No explicit age or gender. Trait (Adrenocortical_Cancer) is constant "ACC" -> not usable.
trait_row = None
age_row = None
gender_row = None

# 2.2 Data Type Conversion

def _after_colon(value: str) -> str:
    if value is None:
        return ""
    parts = str(value).split(":", 1)
    val = parts[1] if len(parts) > 1 else parts[0]
    return val.strip()

def convert_trait(value):
    """
    Binary: 1 = Adrenocortical cancer present, 0 = no cancer/benign/normal.
    Unknown -> None.
    """
    v = _after_colon(value).lower()
    if not v:
        return None
    # Positive indicators
    pos_markers = ["acc", "adrenocortical carcinoma", "adrenocortical cancer", "carcinoma"]
    if any(tok == v or tok in v for tok in pos_markers):
        return 1
    # Negative indicators
    neg_markers = ["normal", "control", "benign", "adenoma", "healthy", "non-cancer", "noncancer"]
    if any(tok in v for tok in neg_markers):
        return 0
    return None

def convert_age(value):
    """
    Continuous: age in years (float). Tries to parse numeric; converts months/days to years if indicated.
    Unknown -> None.
    """
    v = _after_colon(value).lower()
    if not v or v in {"na", "nan", "none", "unknown", ""}:
        return None
    # Find first float/integers in the string
    m = re.search(r"[-+]?\d*\.?\d+", v)
    if not m:
        return None
    num = float(m.group())
    # Unit heuristics
    if "month" in v or "mo" in v:
        return num / 12.0
    if "day" in v or "d " in v or v.endswith("d"):
        return num / 365.25
    # Default assume years
    return num

def convert_gender(value):
    """
    Binary: female -> 0, male -> 1. Unknown -> None.
    """
    v = _after_colon(value).lower()
    if not v:
        return None
    v = v.strip()
    if v in {"female", "f", "woman", "girl"} or "female" in v:
        return 0
    if v in {"male", "m", "man", "boy"} or "male" in v:
        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
# Skipped because trait_row is None (no usable trait variability).