File size: 5,103 Bytes
56598a1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | # 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)) |