|
|
|
from tools.preprocess import * |
|
|
|
|
|
trait = "Adrenocortical_Cancer" |
|
|
|
|
|
tcga_root_dir = "../DATA/TCGA" |
|
|
|
|
|
out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/TCGA.csv" |
|
out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/TCGA.csv" |
|
out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/TCGA.csv" |
|
json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json" |
|
|
|
|
|
cohort = "TCGA_Adrenocortical_Cancer_(ACC)" |
|
cohort_dir = os.path.join(tcga_root_dir, cohort) |
|
|
|
|
|
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir) |
|
|
|
|
|
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') |
|
|
|
|
|
print("Clinical data columns:") |
|
print(clinical_df.columns.tolist()) |
|
|
|
|
|
is_gene_available = len(genetic_df) > 0 |
|
is_trait_available = len(clinical_df) > 0 and any(tcga_convert_trait(idx) != -1 for idx in clinical_df.index) |
|
|
|
|
|
validate_and_save_cohort_info( |
|
is_final=False, |
|
cohort=cohort, |
|
info_path=json_path, |
|
is_gene_available=is_gene_available, |
|
is_trait_available=is_trait_available |
|
) |
|
|
|
candidate_age_cols = ['age_at_initial_pathologic_diagnosis'] |
|
candidate_gender_cols = ['gender'] |
|
|
|
|
|
clinical_cohort_dir = os.path.join(tcga_root_dir, "TCGA_Adrenocortical_Cancer_(ACC)") |
|
clinical_file_path, _ = tcga_get_relevant_filepaths(clinical_cohort_dir) |
|
clinical_df = pd.read_csv(clinical_file_path, index_col=0) |
|
|
|
age_preview = {} |
|
gender_preview = {} |
|
|
|
if candidate_age_cols: |
|
age_data = clinical_df[candidate_age_cols] |
|
age_preview = preview_df(age_data) |
|
|
|
if candidate_gender_cols: |
|
gender_data = clinical_df[candidate_gender_cols] |
|
gender_preview = preview_df(gender_data) |
|
|
|
print("\nAge columns preview:") |
|
print(age_preview) |
|
print("\nGender columns preview:") |
|
print(gender_preview) |
|
|
|
candidate_age_cols = ['age', 'age_at_diagnosis', 'age_at_initial_pathologic_diagnosis', 'days_to_initial_pathologic_diagnosis'] |
|
candidate_gender_cols = ['gender', 'sex'] |
|
|
|
|
|
age_preview = {col: ['<sample_value>'] * 5 for col in candidate_age_cols} |
|
gender_preview = {col: ['<sample_value>'] * 5 for col in candidate_gender_cols} |
|
|
|
print("Age columns preview:") |
|
print(age_preview) |
|
print("\nGender columns preview:") |
|
print(gender_preview) |
|
|
|
age_col = "age_at_initial_pathologic_diagnosis" |
|
gender_col = "gender" |
|
|
|
|
|
print(f"Selected age column: {age_col}") |
|
print(f"Selected gender column: {gender_col}") |
|
|
|
|
|
clinical_features = tcga_select_clinical_features( |
|
clinical_df, |
|
trait=trait, |
|
age_col='age_at_initial_pathologic_diagnosis', |
|
gender_col='gender' |
|
) |
|
|
|
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) |
|
clinical_features.to_csv(out_clinical_data_file) |
|
|
|
|
|
normalized_gene_df = normalize_gene_symbols_in_index(genetic_df) |
|
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) |
|
normalized_gene_df.to_csv(out_gene_data_file) |
|
|
|
|
|
linked_data = pd.merge( |
|
clinical_features, |
|
normalized_gene_df.T, |
|
left_index=True, |
|
right_index=True, |
|
how='inner' |
|
) |
|
|
|
|
|
linked_data = handle_missing_values(linked_data, trait) |
|
|
|
|
|
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) |
|
|
|
|
|
note = "Contains molecular data from tumor and normal samples with patient demographics." |
|
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=trait_biased, |
|
df=linked_data, |
|
note=note |
|
) |
|
|
|
|
|
if is_usable: |
|
os.makedirs(os.path.dirname(out_data_file), exist_ok=True) |
|
linked_data.to_csv(out_data_file) |