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GenoTEX / output /preprocess /Bone_Density /code /TCGA.py

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# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bone_Density"

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

# Output paths
out_data_file = "./output/z2/preprocess/Bone_Density/TCGA.csv"
out_gene_data_file = "./output/z2/preprocess/Bone_Density/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z2/preprocess/Bone_Density/clinical_data/TCGA.csv"
json_path = "./output/z2/preprocess/Bone_Density/cohort_info.json"


# Step 1: Initial Data Loading
import os
import pandas as pd

# Step 1: List subdirectories and select the most relevant cohort for Bone_Density
all_subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]

# Keywords indicative of bone density-related traits
keywords = ['bone', 'bmd', 'osteop', 'skelet', 'bone_density', 'bone mineral density', 'osteopenia']

def is_relevant(name: str, kws) -> bool:
    lname = name.lower()
    return any(kw in lname for kw in kws)

candidate_dirs = [d for d in all_subdirs if is_relevant(d, keywords)]

selected_cohort_dir = None
if candidate_dirs:
    # Choose the most specific (longest name) if multiple matches
    selected_cohort_dir = sorted(candidate_dirs, key=len, reverse=True)[0]
    cohort_path = os.path.join(tcga_root_dir, selected_cohort_dir)

    # Step 2: Identify clinical and genetic file paths
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_path)

    # Step 3: Load both files as DataFrames
    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)

    # Step 4: Print the column names of the clinical data
    print(list(clinical_df.columns))
else:
    print(f"No suitable TCGA cohort found for trait '{trait}'. Skipping this trait.")
    # Record unavailability in cohort info
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
    # Define empty placeholders to avoid potential NameErrors downstream
    clinical_df = pd.DataFrame()
    genetic_df = pd.DataFrame()

	# Path Configuration
	from tools.preprocess import *

	# Processing context
	trait = "Bone_Density"

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

	# Output paths
	out_data_file = "./output/z2/preprocess/Bone_Density/TCGA.csv"
	out_gene_data_file = "./output/z2/preprocess/Bone_Density/gene_data/TCGA.csv"
	out_clinical_data_file = "./output/z2/preprocess/Bone_Density/clinical_data/TCGA.csv"
	json_path = "./output/z2/preprocess/Bone_Density/cohort_info.json"


	# Step 1: Initial Data Loading
	import os
	import pandas as pd

	# Step 1: List subdirectories and select the most relevant cohort for Bone_Density
	all_subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]

	# Keywords indicative of bone density-related traits
	keywords = ['bone', 'bmd', 'osteop', 'skelet', 'bone_density', 'bone mineral density', 'osteopenia']

	def is_relevant(name: str, kws) -> bool:
	lname = name.lower()
	return any(kw in lname for kw in kws)

	candidate_dirs = [d for d in all_subdirs if is_relevant(d, keywords)]

	selected_cohort_dir = None
	if candidate_dirs:
	# Choose the most specific (longest name) if multiple matches
	selected_cohort_dir = sorted(candidate_dirs, key=len, reverse=True)[0]
	cohort_path = os.path.join(tcga_root_dir, selected_cohort_dir)

	# Step 2: Identify clinical and genetic file paths
	clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_path)

	# Step 3: Load both files as DataFrames
	clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
	genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)

	# Step 4: Print the column names of the clinical data
	print(list(clinical_df.columns))
	else:
	print(f"No suitable TCGA cohort found for trait '{trait}'. Skipping this trait.")
	# Record unavailability in cohort info
	validate_and_save_cohort_info(
	is_final=False,
	cohort="TCGA",
	info_path=json_path,
	is_gene_available=False,
	is_trait_available=False
	)
	# Define empty placeholders to avoid potential NameErrors downstream
	clinical_df = pd.DataFrame()
	genetic_df = pd.DataFrame()