output/preprocess/Adrenocortical_Cancer/code/GSE67766.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE67766"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE67766"

# Output paths
out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE67766.csv"
out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE67766.csv"
out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE67766.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 availability based on background info (cell line study likely includes expression profiling)
is_gene_available = True

# 2) Variable availability assessment from sample characteristics:
# Sample Characteristics showed only: {0: ['cell line: SW-13']}
# No human clinical variation; trait is constant (all SW-13 adrenocortical carcinoma cell line), age/gender not provided.
trait_row = None
age_row = None
gender_row = None

# 2.2) Converters
def _after_colon(x):
    if x is None:
        return None
    s = str(x)
    return s.split(":", 1)[1].strip() if ":" in s else s.strip()

def convert_trait(x):
    v = _after_colon(x)
    if not v:
        return None
    vl = v.lower()
    # Map plausible labels to case/control
    positive_markers = [
        "adrenocortical cancer", "adrenocortical carcinoma",
        "adrenal cortex carcinoma", "adrenal carcinoma", "acc", "sw-13", "sw13"
    ]
    negative_markers = ["normal", "control", "healthy", "adjacent normal", "benign"]
    if any(p in vl for p in positive_markers):
        return 1
    if any(n in vl for n in negative_markers):
        return 0
    return None

def convert_age(x):
    v = _after_colon(x)
    if not v:
        return None
    vl = v.lower()
    # Extract number and possible unit
    m = re.search(r'([-+]?\d*\.?\d+)', vl)
    if not m:
        return None
    num = float(m.group(1))
    if "month" in vl:
        return num / 12.0
    # Assume years otherwise
    return num

def convert_gender(x):
    v = _after_colon(x)
    if not v:
        return None
    vl = v.strip().lower()
    # Common mappings
    if vl in ["female", "f", "woman", "women"]:
        return 0
    if vl in ["male", "m", "man", "men"]:
        return 1
    # Sometimes embedded like "sex: female" handled by _after_colon
    if "female" in vl:
        return 0
    if "male" in vl:
        return 1
    return None

# 3) Initial filtering metadata save
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 clinical data existed and trait_row was available, we would extract and save features as below:
# 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_df(selected)
# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
# selected.to_csv(out_clinical_data_file, index=True)

# Step 3: Gene Data Extraction
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])

# Step 4: Gene Identifier Review
print("requires_gene_mapping = True")

# Step 5: Gene Annotation
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))

# Step 6: Gene Identifier Mapping
# 1-2. Determine appropriate columns and create mapping dataframe
# Probe IDs align with 'ID' and gene symbols are in 'Symbol'
mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col='ID', gene_col='Symbol')

# 3. Apply mapping to convert probe-level to gene-level expression
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Step 7: Data Normalization and Linking
import os
import pandas as pd

# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2-6. Branch based on clinical trait availability
if 'trait_row' not in locals() or trait_row is None:
    # No clinical data available; skip linking and final QC
    linked_data = None
    # Record metadata as unusable for linkage analysis
    _ = validate_and_save_cohort_info(
        is_final=False,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False
    )
else:
    # Clinical data available: proceed with linking and downstream processing
    # Retrieve clinical data if not in memory
    if 'selected_clinical_data' not in locals() or selected_clinical_data is None:
        if os.path.exists(out_clinical_data_file):
            selected_clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
        else:
            raise RuntimeError("Clinical data not found in memory or on disk, cannot proceed with linking.")

    # 2. Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

    # 3. Handle missing values
    linked_data = handle_missing_values(linked_data, trait)

    # 4. Bias checks and removal of biased demographic features
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Final validation and metadata save
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=True,
        is_biased=is_trait_biased,
        df=unbiased_linked_data,
        note="INFO: Proceeded with clinical-genetic linking and QC."
    )

    # 6. Save linked data only if usable
    if is_usable:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        unbiased_linked_data.to_csv(out_data_file)