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

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# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autoinflammatory_Disorders"

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

# Output paths
out_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/TCGA.csv"
out_gene_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/clinical_data/TCGA.csv"
json_path = "./output/z1/preprocess/Autoinflammatory_Disorders/cohort_info.json"


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

# Discover available TCGA cohort directories
available_dirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]

# Try to find a TCGA cohort relevant to Autoinflammatory Disorders (unlikely among cancer cohorts)
keywords = [
    "autoinflammatory", "auto-inflammatory", "autoinflammation", "autoinflamm",
    "periodic_fever", "periodic-fever", "fmf", "traps", "hids", "caps", "nlrp", "inflam"
]
matches = []
for d in available_dirs:
    name_l = d.lower()
    score = sum(1 for k in keywords if k in name_l)
    if score > 0:
        # Prefer more keyword hits and shorter names (more specific)
        matches.append((score, -len(d), d))

if not matches:
    # No suitable TCGA cohort for autoinflammatory disorders; record and skip
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
    selected_dir = None
    clinical_df = pd.DataFrame()
    genetic_df = pd.DataFrame()
else:
    # Select the best match
    matches.sort(reverse=True)
    selected_dir = matches[0][2]

if selected_dir:
    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, compression='infer', low_memory=False)
    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, compression='infer', low_memory=False)

    print(list(clinical_df.columns))

# Step 2: Initial Data Loading
import os
import pandas as pd

# Use the provided list of subdirectories but ensure they exist on disk
provided_subdirs = [
    '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)'
]
available_dirs = [d for d in provided_subdirs if os.path.isdir(os.path.join(tcga_root_dir, d))]

# Search for directories matching autoinflammatory disorders (unlikely in TCGA cancer cohorts)
keywords = [
    "autoinflammatory", "auto-inflammatory", "autoinflammation", "autoinflamm",
    "periodic_fever", "periodic-fever", "fmf", "traps", "hids", "caps", "nlrp", "inflam"
]
matches = []
for d in available_dirs:
    name_l = d.lower()
    score = sum(1 for k in keywords if k in name_l)
    if score > 0:
        matches.append((score, -len(d), d))

if not matches:
    # No suitable TCGA cohort for Autoinflammatory Disorders; record and skip
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
    selected_dir = None
    clinical_df = pd.DataFrame()
    genetic_df = pd.DataFrame()
else:
    # Select the most specific match (more keywords, shorter name)
    matches.sort(reverse=True)
    selected_dir = matches[0][2]

# If a directory was selected, locate files and load data
if selected_dir:
    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, compression='infer', low_memory=False)
    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, compression='infer', low_memory=False)

    print(list(clinical_df.columns))

	# Path Configuration
	from tools.preprocess import *

	# Processing context
	trait = "Autoinflammatory_Disorders"

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

	# Output paths
	out_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/TCGA.csv"
	out_gene_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/gene_data/TCGA.csv"
	out_clinical_data_file = "./output/z1/preprocess/Autoinflammatory_Disorders/clinical_data/TCGA.csv"
	json_path = "./output/z1/preprocess/Autoinflammatory_Disorders/cohort_info.json"


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

	# Discover available TCGA cohort directories
	available_dirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]

	# Try to find a TCGA cohort relevant to Autoinflammatory Disorders (unlikely among cancer cohorts)
	keywords = [
	"autoinflammatory", "auto-inflammatory", "autoinflammation", "autoinflamm",
	"periodic_fever", "periodic-fever", "fmf", "traps", "hids", "caps", "nlrp", "inflam"
	]
	matches = []
	for d in available_dirs:
	name_l = d.lower()
	score = sum(1 for k in keywords if k in name_l)
	if score > 0:
	# Prefer more keyword hits and shorter names (more specific)
	matches.append((score, -len(d), d))

	if not matches:
	# No suitable TCGA cohort for autoinflammatory disorders; record and skip
	validate_and_save_cohort_info(
	is_final=False,
	cohort="TCGA",
	info_path=json_path,
	is_gene_available=False,
	is_trait_available=False
	)
	selected_dir = None
	clinical_df = pd.DataFrame()
	genetic_df = pd.DataFrame()
	else:
	# Select the best match
	matches.sort(reverse=True)
	selected_dir = matches[0][2]

	if selected_dir:
	cohort_dir = os.path.join(tcga_root_dir, selected_dir)
	clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

	clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, compression='infer', low_memory=False)
	genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, compression='infer', low_memory=False)

	print(list(clinical_df.columns))

	# Step 2: Initial Data Loading
	import os
	import pandas as pd

	# Use the provided list of subdirectories but ensure they exist on disk
	provided_subdirs = [
	'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)'
	]
	available_dirs = [d for d in provided_subdirs if os.path.isdir(os.path.join(tcga_root_dir, d))]

	# Search for directories matching autoinflammatory disorders (unlikely in TCGA cancer cohorts)
	keywords = [
	"autoinflammatory", "auto-inflammatory", "autoinflammation", "autoinflamm",
	"periodic_fever", "periodic-fever", "fmf", "traps", "hids", "caps", "nlrp", "inflam"
	]
	matches = []
	for d in available_dirs:
	name_l = d.lower()
	score = sum(1 for k in keywords if k in name_l)
	if score > 0:
	matches.append((score, -len(d), d))

	if not matches:
	# No suitable TCGA cohort for Autoinflammatory Disorders; record and skip
	validate_and_save_cohort_info(
	is_final=False,
	cohort="TCGA",
	info_path=json_path,
	is_gene_available=False,
	is_trait_available=False
	)
	selected_dir = None
	clinical_df = pd.DataFrame()
	genetic_df = pd.DataFrame()
	else:
	# Select the most specific match (more keywords, shorter name)
	matches.sort(reverse=True)
	selected_dir = matches[0][2]

	# If a directory was selected, locate files and load data
	if selected_dir:
	cohort_dir = os.path.join(tcga_root_dir, selected_dir)
	clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

	clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, compression='infer', low_memory=False)
	genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, compression='infer', low_memory=False)

	print(list(clinical_df.columns))