output/preprocess/Adrenocortical_Cancer/code/GSE68606.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE68606"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE68606"

# Output paths
out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE68606.csv"
out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE68606.csv"
out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE68606.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
# 1. Gene expression data availability
is_gene_available = True  # Affymetrix HG-U133A; Assay Type: Gene Expression

# 2. Variable availability and conversion functions

# From the sample characteristics dictionary:
# - trait (Adrenocortical_Cancer): Not available (no evidence of carcinoma; only "Adrenal Cortical Adenoma")
trait_row = None

# - Age is available at key 6
age_row = 6

# - Gender is available at key 5
gender_row = 5

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

def convert_trait(x):
    # Not used since trait_row is None. Provided for completeness if needed later.
    val = _after_colon(x)
    if val is None or val == "" or val == "--":
        return None
    v = val.lower()
    # Positive (1): adrenocortical carcinoma
    if ("adrenocortical" in v or "adrenal cortical" in v or "adrenal cortex" in v) and ("carcinoma" in v or "cancer" in v):
        return 1
    # Explicit negatives (0): adenoma or benign adrenal
    if ("adrenal cortical adenoma" in v) or ("adenoma" in v and ("adrenal" in v or "adrenocortical" in v)):
        return 0
    # If explicitly healthy/control, map to 0
    if any(tok in v for tok in ["normal", "control", "benign"]):
        return 0
    # Otherwise, unknown relative to this trait
    return None

def convert_age(x):
    val = _after_colon(x)
    if val is None or val == "" or val in {"--", "na", "n/a", "NA", "unknown"}:
        return None
    try:
        num = float(val)
        # Return integer if it's whole number
        return int(num) if num.is_integer() else num
    except Exception:
        return None

def convert_gender(x):
    val = _after_colon(x)
    if val is None or val == "" or val in {"--", "na", "n/a", "NA", "unknown"}:
        return None
    v = val.strip().lower()
    if v in {"male", "m"}:
        return 1
    if v in {"female", "f"}:
        return 0
    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 (skip because trait_row is None)
# If trait_row becomes available in future, uncomment the following:
# 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 = preview_df(selected_clinical_df, n=5)
# selected_clinical_df.to_csv(out_clinical_data_file)