File size: 2,356 Bytes
fcf7aea | 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 | # Path Configuration
from tools.preprocess import *
# Processing context
trait = "Aniridia"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/z1/preprocess/Aniridia/TCGA.csv"
out_gene_data_file = "./output/z1/preprocess/Aniridia/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z1/preprocess/Aniridia/clinical_data/TCGA.csv"
json_path = "./output/z1/preprocess/Aniridia/cohort_info.json"
# Step 1: Initial Data Loading
import os
import pandas as pd
# 1) Select the most relevant TCGA cohort directory for the trait "Aniridia"
subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
# Heuristic keyword scoring to approximate phenotypic overlap with "Aniridia"
keywords_weights = [
("aniridia", 10),
("iris", 6),
("ocular", 5),
("eye", 4),
("uveal", 4),
("uvea", 4),
("retina", 3),
("optic", 2),
("ophthalm", 2)
]
def score_dir(name: str) -> int:
ln = name.lower()
return sum(w for k, w in keywords_weights if k in ln)
scored = [(d, score_dir(d)) for d in subdirs]
scored.sort(key=lambda x: x[1], reverse=True)
selected_dir = scored[0][0] if scored and scored[0][1] > 0 else None
if selected_dir is None:
# No suitable cohort; record and exit step gracefully
validate_and_save_cohort_info(
is_final=False,
cohort="TCGA",
info_path=json_path,
is_gene_available=False,
is_trait_available=False
)
print("No suitable TCGA cohort directory found for the trait. Skipping.")
else:
cohort_dir = os.path.join(tcga_root_dir, selected_dir)
# 2) Identify clinical and genetic file paths
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
# 3) Load both files as DataFrames
clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
# Keep references for downstream steps
SELECTED_TCGA_DIR = selected_dir
SELECTED_CLINICAL_PATH = clinical_file_path
SELECTED_GENETIC_PATH = genetic_file_path
TCGA_CLINICAL_DF = clinical_df
TCGA_GENETIC_DF = genetic_df
# 4) Print the column names of the clinical data
print(list(clinical_df.columns)) |