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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Colon_and_Rectal_Cancer"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/preprocess/1/Colon_and_Rectal_Cancer/TCGA.csv"
out_gene_data_file = "./output/preprocess/1/Colon_and_Rectal_Cancer/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/1/Colon_and_Rectal_Cancer/clinical_data/TCGA.csv"
json_path = "./output/preprocess/1/Colon_and_Rectal_Cancer/cohort_info.json"
import os
import pandas as pd
# 1. Identify a suitable subdirectory for "Colon_and_Rectal_Cancer"
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 subdirectories for ones containing 'colon_and_rectal_cancer'
candidate_subdirs = [s for s in subdirs if 'colon_and_rectal_cancer' in s.lower()]
if not candidate_subdirs:
print("No matching subdirectory found for Colon_and_Rectal_Cancer. Skipping this trait.")
else:
# We expect "TCGA_Colon_and_Rectal_Cancer_(COADREAD)" to be our best match
chosen_subdir = candidate_subdirs[0]
cohort_dir = os.path.join(tcga_root_dir, chosen_subdir)
# 2. Identify file paths for clinical and genetic data
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']
# Extract columns (if any)
age_subset = clinical_df[candidate_age_cols] if candidate_age_cols else pd.DataFrame()
gender_subset = clinical_df[candidate_gender_cols] if candidate_gender_cols else pd.DataFrame()
# Print the candidate column lists
print("candidate_age_cols =", candidate_age_cols)
print("candidate_gender_cols =", candidate_gender_cols)
# Preview the extracted data
if not age_subset.empty:
print("Age Data Preview:", preview_df(age_subset, n=5))
if not gender_subset.empty:
print("Gender Data Preview:", preview_df(gender_subset, n=5))
age_col = "age_at_initial_pathologic_diagnosis"
gender_col = "gender"
print("Chosen age column:", age_col)
print("Chosen gender column:", gender_col)
# 1) Extract and standardize clinical features
age_col = "age_at_initial_pathologic_diagnosis"
gender_col = "gender"
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 in the index 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
# Transpose the gene dataframe so sample IDs match the clinical dataframe's index
linked_data = selected_clinical_df.join(normalized_gene_df.T, how='inner')
# 4) Handle missing values in the linked data
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 and save metadata
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 Colon_and_Rectal_Cancer (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) |