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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Adrenocortical_Cancer"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/TCGA.csv"
out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/TCGA.csv"
json_path = "./output/preprocess/1/Adrenocortical_Cancer/cohort_info.json"
import os
import pandas as pd
# 1. Identify the relevant subdirectory for the trait "Obesity"
subdirectories = [
'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)'
]
trait_keyword = trait
target_subdir = None
for sd in subdirectories:
if trait_keyword.lower() in sd.lower():
target_subdir = sd
break
if target_subdir is None:
# No suitable data found for this trait; mark as completed
print("No TCGA subdirectory found for the trait. Skipping.")
else:
# 2. Locate clinical and genetic data files
cohort_dir = os.path.join(tcga_root_dir, target_subdir)
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
# 3. Load the data
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 column names of clinical data
print(clinical_df.columns)
candidate_age_cols = ["age_at_initial_pathologic_diagnosis"]
candidate_gender_cols = []
candidate_demo_cols = candidate_age_cols + candidate_gender_cols
if candidate_demo_cols:
extracted_df = clinical_df[candidate_demo_cols]
preview_data = preview_df(extracted_df)
print(preview_data)
# Based on the inspection of the provided dictionaries for age and gender:
age_col = "age_at_initial_pathologic_diagnosis"
gender_col = None
print("Chosen age_col:", age_col)
print("Chosen gender_col:", gender_col)
# 1. Extract and standardize the clinical features
selected_clinical_df = tcga_select_clinical_features(
clinical_df=clinical_df,
trait=trait,
age_col=age_col,
gender_col=gender_col
)
# (Optional) Save the selected clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# 2. Normalize gene symbols in the genetic data
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 on sample IDs
linked_data = selected_clinical_df.join(normalized_gene_df.T, how="inner")
# 4. Handle missing values
cleaned_df = handle_missing_values(linked_data, trait)
# 5. Determine if the trait or demographic features are biased
is_biased, final_df = judge_and_remove_biased_features(cleaned_df, trait)
# 6. Final quality validation
is_gene_available = not normalized_gene_df.empty
is_trait_available = trait in final_df.columns
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort="TCGA",
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=is_trait_available,
is_biased=is_biased,
df=final_df,
note=""
)
# 7. If the dataset is usable, save the final dataframe
if is_usable:
final_df.to_csv(out_data_file) |