p1/preprocess/Chronic_kidney_disease/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Chronic_kidney_disease"

# Input paths
tcga_root_dir = "../DATA/TCGA"

# Output paths
out_data_file = "./output/preprocess/1/Chronic_kidney_disease/TCGA.csv"
out_gene_data_file = "./output/preprocess/1/Chronic_kidney_disease/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/1/Chronic_kidney_disease/clinical_data/TCGA.csv"
json_path = "./output/preprocess/1/Chronic_kidney_disease/cohort_info.json"

import os
import pandas as pd

# 1. Identify a suitable subdirectory for "Chronic_kidney_disease"
subdirs = [
    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)',
    'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)', 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)',
    'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)', 'TCGA_Glioblastoma_(GBM)',
    'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)',
    'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)', 'TCGA_Breast_Cancer_(BRCA)',
    'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)', 'TCGA_Adrenocortical_Cancer_(ACC)',
    'TCGA_Acute_Myeloid_Leukemia_(LAML)'
]

# Filter for possible kidney-related subdirectories
kidney_subdirs = [s for s in subdirs if 'kidney' in s.lower()]

# If none found, skip. Otherwise, pick the most specific one for this trait; here we choose clear cell carcinoma
if not kidney_subdirs:
    print("No matching subdirectory found for Chronic_kidney_disease. Skipping this trait.")
else:
    chosen_subdir = 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)'  # chosen after manual inspection

    # 2. Identify file paths for clinical and genetic data
    cohort_dir = os.path.join(tcga_root_dir, chosen_subdir)
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

    # 3. Load data into Pandas DataFrames
    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')

    # 4. Print the column names of the clinical data
    print("Clinical Data Columns:", clinical_df.columns.tolist())
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

age_preview = clinical_df[candidate_age_cols].head(5).to_dict(orient='list') if candidate_age_cols else {}
gender_preview = clinical_df[candidate_gender_cols].head(5).to_dict(orient='list') if candidate_gender_cols else {}

age_preview, gender_preview
# In this code snippet, we assume that the columns "age_at_diagnosis" (for age)
# and "gender" (for gender) have been verified to contain valid and sufficiently complete data.
# If no suitable column was found for either attribute, we would set it to None.

age_col = "age_at_diagnosis"
gender_col = "gender"

print("Chosen age_col:", age_col)
print("Chosen gender_col:", gender_col)
# 1) Extract and standardize clinical features
age_col = "age_at_initial_pathologic_diagnosis"  # Updated valid age column
gender_col = "gender"  # Remains valid

selected_clinical_df = tcga_select_clinical_features(
    clinical_df=clinical_df,
    trait=trait,
    age_col=age_col,
    gender_col=gender_col
)

# 2) Normalize gene symbols and save
normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
normalized_gene_df.to_csv(out_gene_data_file)

# 3) Link the clinical and genetic data
linked_data = selected_clinical_df.join(normalized_gene_df, how='inner')

# 4) Handle missing values
processed_linked_data = handle_missing_values(linked_data, trait)

# 5) Determine whether the trait/demographic features are biased
is_trait_biased, final_data = judge_and_remove_biased_features(processed_linked_data, trait)

# 6) Conduct final validation
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_trait_biased,
    df=final_data,
    note="Preprocessing complete for Chronic_kidney_disease (TCGA)."
)

# 7) If usable, save the final linked data and clinical subset
if is_usable:
    final_data.to_csv(out_data_file)
    clinical_cols = [c for c in [trait, "Age", "Gender"] if c in final_data.columns]
    if clinical_cols:
        final_data[clinical_cols].to_csv(out_clinical_data_file)