# Path Configuration from tools.preprocess import * # Processing context trait = "Bile_Duct_Cancer" # Input paths tcga_root_dir = "../DATA/TCGA" # Output paths out_data_file = "./output/z1/preprocess/Bile_Duct_Cancer/TCGA.csv" out_gene_data_file = "./output/z1/preprocess/Bile_Duct_Cancer/gene_data/TCGA.csv" out_clinical_data_file = "./output/z1/preprocess/Bile_Duct_Cancer/clinical_data/TCGA.csv" json_path = "./output/z1/preprocess/Bile_Duct_Cancer/cohort_info.json" # Step 1: Initial Data Loading import os import pandas as pd # Find the most appropriate TCGA cohort directory for the trait subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))] lower_map = {d: d.lower() for d in subdirs} # Prioritize exact trait phrase, then synonyms selected_dir = None exact_key = 'bile_duct_cancer' synonym_keys = ['(chol', 'cholangio'] # CHOL code and cholangiocarcinoma keyword # Exact match candidates_exact = [d for d, dl in lower_map.items() if exact_key in dl] if candidates_exact: # Choose the most specific (shortest name) if multiple selected_dir = sorted(candidates_exact, key=len)[0] else: # Synonym-based match candidates_syn = [d for d, dl in lower_map.items() if any(k in dl for k in synonym_keys)] if candidates_syn: selected_dir = sorted(candidates_syn, key=len)[0] if selected_dir is None: # No suitable cohort found; mark and stop further processing in this step 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 found for the trait. Skipping.") else: tcga_cohort_dir = os.path.join(tcga_root_dir, selected_dir) # Identify clinical and genetic file paths tcga_clinical_file, tcga_genetic_file = tcga_get_relevant_filepaths(tcga_cohort_dir) # Load dataframes tcga_clinical_df = pd.read_csv(tcga_clinical_file, sep='\t', index_col=0, low_memory=False, compression='infer') tcga_genetic_df = pd.read_csv(tcga_genetic_file, sep='\t', index_col=0, low_memory=False, compression='infer') # Print clinical column names for further analysis print(list(tcga_clinical_df.columns)) # Step 2: Find Candidate Demographic Features import os import pandas as pd # Column names from the previous step column_names = ['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'age_at_initial_pathologic_diagnosis', 'albumin_result_lower_limit', 'albumin_result_specified_value', 'albumin_result_upper_limit', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'bilirubin_lower_limit', 'bilirubin_upper_limit', 'ca_19_9_level', 'ca_19_9_level_lower', 'ca_19_9_level_upper', 'cancer_first_degree_relative', 'child_pugh_classification_grade', 'cholangitis_tissue_evidence', 'creatinine_lower_level', 'creatinine_upper_limit', 'creatinine_value_in_mg_dl', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_after_initial_treatment', 'eastern_cancer_oncology_group', 'family_cancer_type_txt', 'family_member_relationship_type', 'fetoprotein_outcome_lower_limit', 'fetoprotein_outcome_upper_limit', 'fetoprotein_outcome_value', 'fibrosis_ishak_score', 'form_completion_date', 'gender', 'height', 'hist_hepato_carc_fact', 'hist_hepato_carcinoma_risk', 'histological_type', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_weight', 'inter_norm_ratio_lower_limit', 'intern_norm_ratio_upper_limit', 'is_ffpe', 'lost_follow_up', 'neoplasm_histologic_grade', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type', 'new_tumor_event_ablation_embo_tx', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'new_tumor_event_liver_transplant', 'oct_embedded', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'perineural_invasion_present', 'person_neoplasm_cancer_status', 'platelet_result_count', 'platelet_result_lower_limit', 'platelet_result_upper_limit', 'post_op_ablation_embolization_tx', 'postoperative_rx_tx', 'prothrombin_time_result_value', 'radiation_therapy', 'relative_family_cancer_history', 'residual_tumor', 'sample_type', 'sample_type_id', 'specimen_collection_method_name', 'system_version', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'total_bilirubin_upper_limit', 'tumor_tissue_site', 'vascular_tumor_cell_type', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_CHOL_mutation_broad_gene', '_GENOMIC_ID_TCGA_CHOL_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_CHOL_hMethyl450', '_GENOMIC_ID_TCGA_CHOL_exp_HiSeqV2', '_GENOMIC_ID_TCGA_CHOL_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_CHOL_mutation_bcm_gene', '_GENOMIC_ID_TCGA_CHOL_miRNA_HiSeq', '_GENOMIC_ID_TCGA_CHOL_gistic2thd', '_GENOMIC_ID_TCGA_CHOL_gistic2', '_GENOMIC_ID_TCGA_CHOL_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_CHOL_exp_HiSeqV2_exon', '_GENOMIC_ID_data/public/TCGA/CHOL/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_CHOL_mutation_ucsc_maf_gene', '_GENOMIC_ID_TCGA_CHOL_PDMRNAseq', '_GENOMIC_ID_TCGA_CHOL_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_CHOL_RPPA'] # Step 1: Identify candidate demographic columns candidate_age_cols = [col for col in column_names if col in ['age_at_initial_pathologic_diagnosis', 'days_to_birth']] candidate_gender_cols = [col for col in column_names if col.lower() == 'gender'] print(f"candidate_age_cols = {candidate_age_cols}") print(f"candidate_gender_cols = {candidate_gender_cols}") # Step 2: Extract candidate columns from clinical data and preview clinical_df = None clinical_file_path = None # Try to locate the CHOL clinical file under tcga_root_dir try: # Prefer directories that contain CHOL found = False for root, dirs, files in os.walk(tcga_root_dir): try: cpath, _ = tcga_get_relevant_filepaths(root) if os.path.exists(cpath) and ('chol' in cpath.lower() or 'chol' in root.lower()): clinical_file_path = cpath found = True break except Exception: pass # Fallback: directly search for clinical files mentioning CHOL if not found: for root, dirs, files in os.walk(tcga_root_dir): for f in files: fl = f.lower() if 'clinical' in fl and 'matrix' in fl and 'chol' in fl: clinical_file_path = os.path.join(root, f) found = True break if found: break if clinical_file_path and os.path.exists(clinical_file_path): # Xena clinicalMatrix is tab-separated; sample IDs as index clinical_df = pd.read_csv(clinical_file_path, sep='\t', header=0, index_col=0, dtype=str) except Exception: clinical_df = None age_preview = {} gender_preview = {} if clinical_df is not None: age_cols_present = [c for c in candidate_age_cols if c in clinical_df.columns] gender_cols_present = [c for c in candidate_gender_cols if c in clinical_df.columns] if age_cols_present: age_preview = preview_df(clinical_df[age_cols_present], n=5) if gender_cols_present: gender_preview = preview_df(clinical_df[gender_cols_present], n=5) print(age_preview) print(gender_preview) # Step 3: Select Demographic Features # Robust selection of demographic columns using provided candidate lists and preview dictionaries. # Incorporates value plausibility checks and handles empty inputs. # Helper to safely get global variables by name def _get_global(name, default=None): return globals()[name] if name in globals() else default # Try to find the preview dictionary by known names or by heuristic overlap with candidate columns def _find_preview_dict(candidates, preferred_names): for n in preferred_names: d = _get_global(n, None) if isinstance(d, dict): return d # Heuristic scan: choose dict with the largest overlap with candidates best = None best_overlap = 0 for k, v in globals().items(): if isinstance(v, dict) and v: try: overlap = len(set(v.keys()) & set(candidates)) except Exception: overlap = 0 if overlap > best_overlap: best = v best_overlap = overlap return best if best_overlap > 0 else {} # Parsing helpers def _parse_int_with_sign(x): if x is None: return None s = str(x).strip() m = re.search(r'-?\d+', s) return int(m.group()) if m else None def _is_valid_age_column(col, values): if not isinstance(values, list) or len(values) == 0: return False n = len(values) min_valid = max(1, int((0.6 * n) + 0.9999)) # ceil(0.6*n) lc = col.lower() if 'days' in lc and 'birth' in lc: parsed = [_parse_int_with_sign(v) for v in values] valid = [p for p in parsed if isinstance(p, int)] if len(valid) < min_valid: return False plausible = [abs(v) / 365.25 for v in valid] plausible_cnt = sum(0 <= yr <= 120 for yr in plausible) return plausible_cnt >= min_valid else: # Treat as age in years parsed = [tcga_convert_age(v) for v in values] valid = [p for p in parsed if isinstance(p, int)] if len(valid) < min_valid: return False plausible_cnt = sum(0 <= p <= 120 for p in valid) return plausible_cnt >= min_valid def _is_valid_gender_values(values): if not isinstance(values, list) or len(values) == 0: return False n = len(values) min_valid = max(1, int((0.6 * n) + 0.9999)) # ceil(0.6*n) mapped = [tcga_convert_gender(v) for v in values] valid = [m for m in mapped if m in (0, 1)] return len(valid) >= min_valid def select_age_col(candidates, age_preview_dict): if not candidates or not isinstance(age_preview_dict, dict) or not age_preview_dict: return None # Priority: explicit age in years over derived days priority_order = [ "age_at_initial_pathologic_diagnosis", "age_at_diagnosis", "age_at_index", "age" ] ordered = [p for p in priority_order if p in candidates] ordered += [c for c in candidates if c not in ordered] for c in ordered: if c in age_preview_dict and _is_valid_age_column(c, age_preview_dict[c]): return c # As a last resort, if days_to_birth is available and valid, use it for c in candidates: if c.lower() == 'days_to_birth' and c in age_preview_dict and _is_valid_age_column(c, age_preview_dict[c]): return c return None def select_gender_col(candidates, gender_preview_dict): if not candidates or not isinstance(gender_preview_dict, dict) or not gender_preview_dict: return None priority_order = ["gender", "sex"] ordered = [p for p in priority_order if p in candidates] ordered += [c for c in candidates if c not in ordered] for c in ordered: if c in gender_preview_dict and _is_valid_gender_values(gender_preview_dict[c]): return c return None # Retrieve candidate lists candidate_age_cols = _get_global('candidate_age_cols', []) candidate_gender_cols = _get_global('candidate_gender_cols', []) # Retrieve preview dicts (try known names, then heuristic) age_preview_dict = _find_preview_dict(candidate_age_cols, ['age_preview_dict', 'age_preview']) gender_preview_dict = _find_preview_dict(candidate_gender_cols, ['gender_preview_dict', 'gender_preview']) # Select columns using both candidates and previews age_col = select_age_col(candidate_age_cols, age_preview_dict) gender_col = select_gender_col(candidate_gender_cols, gender_preview_dict) # Explicitly print chosen columns and their preview values (first 5) print(f"Chosen age_col: {age_col}") if age_col is not None and isinstance(age_preview_dict, dict) and age_col in age_preview_dict: print(f"age_col preview values: {age_preview_dict[age_col]}") else: print("age_col preview values: None or not available") print(f"Chosen gender_col: {gender_col}") if gender_col is not None and isinstance(gender_preview_dict, dict) and gender_col in gender_preview_dict: print(f"gender_col preview values: {gender_preview_dict[gender_col]}") else: print("gender_col preview values: None or not available") # Step 4: Feature Engineering and Validation import os # 1) Extract and standardize clinical features selected_clinical_df = tcga_select_clinical_features( clinical_df=tcga_clinical_df, trait=trait, age_col=age_col, gender_col=gender_col ) # 2) Normalize gene symbols and save normalized_gene_df = normalize_gene_symbols_in_index(tcga_genetic_df.copy()) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) normalized_gene_df.to_csv(out_gene_data_file) # 3) Link clinical and genetic data gene_t = normalized_gene_df.T # samples as index linked_data = selected_clinical_df.join(gene_t, how='inner') # 4) Handle missing values processed_df = handle_missing_values(linked_data.copy(), trait_col=trait) # 5) Determine bias and remove biased demographic features if needed is_biased, processed_df = judge_and_remove_biased_features(processed_df, trait=trait) # 6) Final validation and save cohort info # Cast to Python bool to avoid numpy.bool_ JSON serialization issues is_gene_available = bool((normalized_gene_df.shape[0] > 0) and (normalized_gene_df.shape[1] > 0)) is_trait_available = bool((trait in selected_clinical_df.columns) and bool(selected_clinical_df[trait].notna().any())) is_biased_bool = bool(is_biased) note = ( f"INFO: Age column used: {age_col}; Gender column used: {gender_col}. " f"Linked samples (pre-QC): {linked_data.shape[0]}, genes: {linked_data.shape[1] - selected_clinical_df.shape[1]}." ) is_usable = validate_and_save_cohort_info( is_final=True, cohort="TCGA", info_path=json_path, is_gene_available=is_gene_available, is_trait_available=is_trait_available, is_biased=is_biased_bool, df=processed_df, note=note ) # 7) Save linked data if usable if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) processed_df.to_csv(out_data_file)