File size: 28,289 Bytes
7ae1978 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "e831f067",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:36:22.626862Z",
"iopub.status.busy": "2025-03-25T07:36:22.626597Z",
"iopub.status.idle": "2025-03-25T07:36:22.809471Z",
"shell.execute_reply": "2025-03-25T07:36:22.809137Z"
}
},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
"\n",
"# Path Configuration\n",
"from tools.preprocess import *\n",
"\n",
"# Processing context\n",
"trait = \"Longevity\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Longevity/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Longevity/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Longevity/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Longevity/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "d8a86f74",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cd52ef19",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:36:22.810911Z",
"iopub.status.busy": "2025-03-25T07:36:22.810771Z",
"iopub.status.idle": "2025-03-25T07:36:25.500654Z",
"shell.execute_reply": "2025-03-25T07:36:25.500306Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Available TCGA subdirectories: ['TCGA_Liver_Cancer_(LIHC)', 'TCGA_Lower_Grade_Glioma_(LGG)', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Mesothelioma_(MESO)', 'TCGA_Ocular_melanomas_(UVM)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Rectal_Cancer_(READ)', 'TCGA_Sarcoma_(SARC)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Thymoma_(THYM)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Uterine_Carcinosarcoma_(UCS)', '.DS_Store', 'CrawlData.ipynb', 'TCGA_Acute_Myeloid_Leukemia_(LAML)', 'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Bile_Duct_Cancer_(CHOL)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Cervical_Cancer_(CESC)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Endometrioid_Cancer_(UCEC)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Glioblastoma_(GBM)', 'TCGA_Head_and_Neck_Cancer_(HNSC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)']\n",
"Searching for datasets suitable for longevity analysis...\n",
"Selected directory: TCGA_Breast_Cancer_(BRCA) for longevity analysis\n",
"Clinical file: TCGA.BRCA.sampleMap_BRCA_clinicalMatrix\n",
"Genetic file: TCGA.BRCA.sampleMap_HiSeqV2_PANCAN.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Clinical data columns:\n",
"['AJCC_Stage_nature2012', 'Age_at_Initial_Pathologic_Diagnosis_nature2012', 'CN_Clusters_nature2012', 'Converted_Stage_nature2012', 'Days_to_Date_of_Last_Contact_nature2012', 'Days_to_date_of_Death_nature2012', 'ER_Status_nature2012', 'Gender_nature2012', 'HER2_Final_Status_nature2012', 'Integrated_Clusters_no_exp__nature2012', 'Integrated_Clusters_unsup_exp__nature2012', 'Integrated_Clusters_with_PAM50__nature2012', 'Metastasis_Coded_nature2012', 'Metastasis_nature2012', 'Node_Coded_nature2012', 'Node_nature2012', 'OS_Time_nature2012', 'OS_event_nature2012', 'PAM50Call_RNAseq', 'PAM50_mRNA_nature2012', 'PR_Status_nature2012', 'RPPA_Clusters_nature2012', 'SigClust_Intrinsic_mRNA_nature2012', 'SigClust_Unsupervised_mRNA_nature2012', 'Survival_Data_Form_nature2012', 'Tumor_T1_Coded_nature2012', 'Tumor_nature2012', 'Vital_Status_nature2012', '_INTEGRATION', '_PANCAN_CNA_PANCAN_K8', '_PANCAN_Cluster_Cluster_PANCAN', '_PANCAN_DNAMethyl_BRCA', '_PANCAN_DNAMethyl_PANCAN', '_PANCAN_RPPA_PANCAN_K8', '_PANCAN_UNC_RNAseq_PANCAN_K16', '_PANCAN_miRNA_PANCAN', '_PANCAN_mirna_BRCA', '_PANCAN_mutation_PANCAN', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'additional_surgery_locoregional_procedure', 'additional_surgery_metastatic_procedure', 'age_at_initial_pathologic_diagnosis', 'anatomic_neoplasm_subdivision', 'axillary_lymph_node_stage_method_type', 'axillary_lymph_node_stage_other_method_descriptive_text', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'breast_cancer_surgery_margin_status', 'breast_carcinoma_estrogen_receptor_status', 'breast_carcinoma_immunohistochemistry_er_pos_finding_scale', 'breast_carcinoma_immunohistochemistry_pos_cell_score', 'breast_carcinoma_immunohistochemistry_prgstrn_rcptr_ps_fndng_scl', 'breast_carcinoma_primary_surgical_procedure_name', 'breast_carcinoma_progesterone_receptor_status', 'breast_carcinoma_surgical_procedure_name', 'breast_neoplasm_other_surgical_procedure_descriptive_text', 'cytokeratin_immunohistochemistry_staining_method_mcrmtstss_ndctr', 'days_to_additional_surgery_locoregional_procedure', 'days_to_additional_surgery_metastatic_procedure', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_last_known_alive', 'days_to_new_tumor_event_additional_surgery_procedure', 'days_to_new_tumor_event_after_initial_treatment', 'disease_code', 'distant_metastasis_present_ind2', 'er_detection_method_text', 'er_level_cell_percentage_category', 'fluorescence_in_st_hybrdztn_dgnstc_prcdr_chrmsm_17_sgnl_rslt_rng', 'followup_case_report_form_submission_reason', 'form_completion_date', 'gender', 'her2_and_centromere_17_positive_finding_other_measuremnt_scl_txt', 'her2_erbb_method_calculation_method_text', 'her2_erbb_pos_finding_cell_percent_category', 'her2_erbb_pos_finding_fluorescence_n_st_hybrdztn_clcltn_mthd_txt', 'her2_immunohistochemistry_level_result', 'her2_neu_and_centromere_17_copy_number_analysis_npt_ttl_nmbr_cnt', 'her2_neu_breast_carcinoma_copy_analysis_input_total_number', 'her2_neu_chromosone_17_signal_ratio_value', 'her2_neu_metastatic_breast_carcinoma_copy_analysis_inpt_ttl_nmbr', 'histological_type', 'histological_type_other', 'history_of_neoadjuvant_treatment', 'hr2_n_nd_cntrmr_17_cpy_nmbr_mtsttc_brst_crcnm_nlyss_npt_ttl_nmbr', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'immunohistochemistry_positive_cell_score', 'informed_consent_verified', 'init_pathology_dx_method_other', 'initial_pathologic_diagnosis_method', 'initial_weight', 'is_ffpe', 'lab_proc_her2_neu_immunohistochemistry_receptor_status', 'lab_procedure_her2_neu_in_situ_hybrid_outcome_type', 'lost_follow_up', 'lymph_node_examined_count', 'margin_status', 'menopause_status', 'metastatic_breast_carcinm_ps_fndng_prgstrn_rcptr_thr_msr_scl_txt', 'metastatic_breast_carcinom_lb_prc_hr2_n_mmnhstchmstry_rcptr_stts', 'metastatic_breast_carcinoma_erbb2_immunohistochemistry_levl_rslt', 'metastatic_breast_carcinoma_estrogen_receptor_detection_mthd_txt', 'metastatic_breast_carcinoma_estrogen_receptor_status', 'metastatic_breast_carcinoma_estrogen_receptr_lvl_cll_prcnt_ctgry', 'metastatic_breast_carcinoma_her2_erbb_method_calculatin_mthd_txt', 'metastatic_breast_carcinoma_her2_erbb_pos_findng_cll_prcnt_ctgry', 'metastatic_breast_carcinoma_her2_neu_chromosone_17_signal_rat_vl', 'metastatic_breast_carcinoma_immunhstchmstry_r_pstv_fndng_scl_typ', 'metastatic_breast_carcinoma_immunohistochemistry_er_pos_cell_scr', 'metastatic_breast_carcinoma_immunohistochemistry_pr_pos_cell_scr', 'metastatic_breast_carcinoma_lab_proc_hr2_n_n_st_hybrdztn_tcm_typ', 'metastatic_breast_carcinoma_pos_finding_hr2_rbb2_thr_msr_scl_txt', 'metastatic_breast_carcinoma_progestern_rcptr_lvl_cll_prcnt_ctgry', 'metastatic_breast_carcinoma_progesterone_receptor_dtctn_mthd_txt', 'metastatic_breast_carcinoma_progesterone_receptor_status', 'metastatic_site_at_diagnosis', 'metastatic_site_at_diagnosis_other', 'methylation_Clusters_nature2012', 'miRNA_Clusters_nature2012', 'mtsttc_brst_crcnm_flrscnc_n_st_hybrdztn_dgnstc_prc_cntrmr_17_sgn', 'mtsttc_brst_crcnm_hr2_rbb_ps_fndng_flrscnc_n_st_hybrdztn_clcltn', 'mtsttc_brst_crcnm_mmnhstchmstry_prgstrn_rcptr_pstv_fndng_scl_typ', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type', 'new_neoplasm_occurrence_anatomic_site_text', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'number_of_lymphnodes_positive_by_he', 'number_of_lymphnodes_positive_by_ihc', 'oct_embedded', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'person_neoplasm_cancer_status', 'pgr_detection_method_text', 'pos_finding_her2_erbb2_other_measurement_scale_text', 'pos_finding_metastatic_brst_crcnm_strgn_rcptr_thr_msrmnt_scl_txt', 'pos_finding_progesterone_receptor_other_measurement_scale_text', 'positive_finding_estrogen_receptor_other_measurement_scale_text', 'postoperative_rx_tx', 'primary_lymph_node_presentation_assessment', 'progesterone_receptor_level_cell_percent_category', 'project_code', 'radiation_therapy', 'sample_type', 'sample_type_id', 'surgical_procedure_purpose_other_text', 'system_version', 'targeted_molecular_therapy', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tumor_tissue_site', 'vial_number', 'vital_status', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_BRCA_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_BRCA_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_BRCA_RPPA_RBN', '_GENOMIC_ID_TCGA_BRCA_mutation', '_GENOMIC_ID_TCGA_BRCA_PDMRNAseq', '_GENOMIC_ID_TCGA_BRCA_hMethyl450', '_GENOMIC_ID_TCGA_BRCA_RPPA', '_GENOMIC_ID_TCGA_BRCA_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_BRCA_mutation_curated_wustl_gene', '_GENOMIC_ID_TCGA_BRCA_hMethyl27', '_GENOMIC_ID_TCGA_BRCA_PDMarrayCNV', '_GENOMIC_ID_TCGA_BRCA_miRNA_HiSeq', '_GENOMIC_ID_TCGA_BRCA_mutation_wustl_gene', '_GENOMIC_ID_TCGA_BRCA_miRNA_GA', '_GENOMIC_ID_TCGA_BRCA_exp_HiSeqV2_percentile', '_GENOMIC_ID_data/public/TCGA/BRCA/miRNA_GA_gene', '_GENOMIC_ID_TCGA_BRCA_gistic2thd', '_GENOMIC_ID_data/public/TCGA/BRCA/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_BRCA_G4502A_07_3', '_GENOMIC_ID_TCGA_BRCA_exp_HiSeqV2', '_GENOMIC_ID_TCGA_BRCA_gistic2', '_GENOMIC_ID_TCGA_BRCA_PDMarray']\n",
"\n",
"Clinical data shape: (1247, 193)\n",
"Genetic data shape: (20530, 1218)\n",
"\n",
"Survival-related columns found:\n",
"['Days_to_Date_of_Last_Contact_nature2012', 'Days_to_date_of_Death_nature2012', 'Survival_Data_Form_nature2012', 'Vital_Status_nature2012', 'bcr_followup_barcode', 'days_to_additional_surgery_locoregional_procedure', 'days_to_additional_surgery_metastatic_procedure', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_last_known_alive', 'days_to_new_tumor_event_additional_surgery_procedure', 'days_to_new_tumor_event_after_initial_treatment', 'followup_case_report_form_submission_reason', 'lost_follow_up', 'vital_status']\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"\n",
"# 1. List all subdirectories in the TCGA root directory\n",
"subdirectories = os.listdir(tcga_root_dir)\n",
"print(f\"Available TCGA subdirectories: {subdirectories}\")\n",
"\n",
"# The target trait is Longevity\n",
"target_trait = trait.lower() # \"longevity\"\n",
"\n",
"# For longevity, we should look for datasets with good survival/aging data\n",
"# rather than matching the term directly to a cancer type\n",
"print(f\"Searching for datasets suitable for {target_trait} analysis...\")\n",
"\n",
"# Since longevity relates to survival time and patient outcomes, \n",
"# we'll select a cancer type with typically good survival data coverage\n",
"# Choosing TCGA_Breast_Cancer_(BRCA) as it typically has a large cohort with varied survival outcomes\n",
"selected_dir = \"TCGA_Breast_Cancer_(BRCA)\"\n",
"\n",
"# Verify the directory exists\n",
"if selected_dir in subdirectories:\n",
" print(f\"Selected directory: {selected_dir} for longevity analysis\")\n",
" \n",
" # 2. Get the clinical and genetic data file paths\n",
" cohort_dir = os.path.join(tcga_root_dir, selected_dir)\n",
" clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
" \n",
" print(f\"Clinical file: {os.path.basename(clinical_file_path)}\")\n",
" print(f\"Genetic file: {os.path.basename(genetic_file_path)}\")\n",
" \n",
" # 3. Load the data files\n",
" clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
" genetic_df = pd.read_csv(genetic_file_path, sep='\\t', index_col=0)\n",
" \n",
" # 4. Print clinical data columns for inspection\n",
" print(\"\\nClinical data columns:\")\n",
" print(clinical_df.columns.tolist())\n",
" \n",
" # Print basic information about the datasets\n",
" print(f\"\\nClinical data shape: {clinical_df.shape}\")\n",
" print(f\"Genetic data shape: {genetic_df.shape}\")\n",
" \n",
" # Check if the clinical data contains survival-related columns\n",
" survival_columns = [col for col in clinical_df.columns if any(term in col.lower() for term in \n",
" ['survival', 'death', 'follow', 'vital', 'days_to'])]\n",
" print(\"\\nSurvival-related columns found:\")\n",
" print(survival_columns)\n",
" \n",
" # Check if we have both gene and survival data\n",
" is_gene_available = genetic_df.shape[0] > 0\n",
" is_trait_available = clinical_df.shape[0] > 0 and len(survival_columns) > 0\n",
" \n",
"else:\n",
" print(f\"Directory {selected_dir} not found. Checking for alternative datasets...\")\n",
" # Alternative approach: search for a dataset with good survival information\n",
" is_gene_available = False\n",
" is_trait_available = False\n",
" \n",
" for subdir in subdirectories:\n",
" if os.path.isdir(os.path.join(tcga_root_dir, subdir)) and not subdir.startswith('.'):\n",
" try:\n",
" cohort_dir = os.path.join(tcga_root_dir, subdir)\n",
" clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
" \n",
" # Quick check of clinical data\n",
" clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
" genetic_df = pd.read_csv(genetic_file_path, sep='\\t', index_col=0)\n",
" \n",
" # Look for survival columns\n",
" survival_columns = [col for col in clinical_df.columns if any(term in col.lower() for term in \n",
" ['survival', 'death', 'follow', 'vital', 'days_to'])]\n",
" \n",
" if len(survival_columns) > 0 and clinical_df.shape[0] > 100 and genetic_df.shape[0] > 0:\n",
" print(f\"Selected alternative directory: {subdir}\")\n",
" print(f\"Clinical file: {os.path.basename(clinical_file_path)}\")\n",
" print(f\"Genetic file: {os.path.basename(genetic_file_path)}\")\n",
" print(f\"\\nClinical data shape: {clinical_df.shape}\")\n",
" print(f\"Genetic data shape: {genetic_df.shape}\")\n",
" print(\"\\nSurvival-related columns found:\")\n",
" print(survival_columns)\n",
" \n",
" is_gene_available = True\n",
" is_trait_available = True\n",
" break\n",
" \n",
" except Exception as e:\n",
" print(f\"Error processing {subdir}: {e}\")\n",
" continue\n",
"\n",
"# Record the data availability\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# Exit if no suitable directory was found\n",
"if not is_gene_available or not is_trait_available:\n",
" print(\"Skipping this trait as no suitable survival data was found in TCGA.\")\n"
]
},
{
"cell_type": "markdown",
"id": "bb1da3f7",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "546f8403",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:36:25.501866Z",
"iopub.status.busy": "2025-03-25T07:36:25.501753Z",
"iopub.status.idle": "2025-03-25T07:36:25.523465Z",
"shell.execute_reply": "2025-03-25T07:36:25.523149Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age columns preview:\n",
"{'Age_at_Initial_Pathologic_Diagnosis_nature2012': [nan, nan, nan, nan, nan], 'age_at_initial_pathologic_diagnosis': [55.0, 50.0, 62.0, 52.0, 50.0], 'days_to_birth': [-20211.0, -18538.0, -22848.0, -19074.0, -18371.0]}\n",
"\n",
"Gender columns preview:\n",
"{'Gender_nature2012': [nan, nan, nan, nan, nan], 'gender': ['FEMALE', 'FEMALE', 'FEMALE', 'FEMALE', 'FEMALE']}\n"
]
}
],
"source": [
"# Find candidate age columns\n",
"candidate_age_cols = [\n",
" \"Age_at_Initial_Pathologic_Diagnosis_nature2012\",\n",
" \"age_at_initial_pathologic_diagnosis\",\n",
" \"days_to_birth\" # Can be used to calculate age\n",
"]\n",
"\n",
"# Find candidate gender columns\n",
"candidate_gender_cols = [\n",
" \"Gender_nature2012\",\n",
" \"gender\"\n",
"]\n",
"\n",
"# Preview the selected columns\n",
"# First, get the clinical file path\n",
"cohort_dir = os.path.join(tcga_root_dir, \"TCGA_Breast_Cancer_(BRCA)\")\n",
"clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)\n",
"\n",
"# Load the clinical data\n",
"clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"\n",
"# Extract candidate age columns\n",
"age_preview = {}\n",
"for col in candidate_age_cols:\n",
" if col in clinical_df.columns:\n",
" age_preview[col] = clinical_df[col].head(5).tolist()\n",
"\n",
"# Extract candidate gender columns\n",
"gender_preview = {}\n",
"for col in candidate_gender_cols:\n",
" if col in clinical_df.columns:\n",
" gender_preview[col] = clinical_df[col].head(5).tolist()\n",
"\n",
"# Display previews\n",
"print(\"Age columns preview:\")\n",
"print(age_preview)\n",
"print(\"\\nGender columns preview:\")\n",
"print(gender_preview)\n"
]
},
{
"cell_type": "markdown",
"id": "221adb17",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8f978524",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:36:25.524705Z",
"iopub.status.busy": "2025-03-25T07:36:25.524593Z",
"iopub.status.idle": "2025-03-25T07:36:25.528293Z",
"shell.execute_reply": "2025-03-25T07:36:25.528014Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected age column: age_at_initial_pathologic_diagnosis\n",
"Selected gender column: gender\n"
]
}
],
"source": [
"# Analyzing available demographic columns\n",
"\n",
"# For age columns\n",
"age_columns = {'Age_at_Initial_Pathologic_Diagnosis_nature2012': [float('nan'), float('nan'), float('nan'), float('nan'), float('nan')], \n",
" 'age_at_initial_pathologic_diagnosis': [55.0, 50.0, 62.0, 52.0, 50.0], \n",
" 'days_to_birth': [-20211.0, -18538.0, -22848.0, -19074.0, -18371.0]}\n",
"\n",
"# For gender columns\n",
"gender_columns = {'Gender_nature2012': [float('nan'), float('nan'), float('nan'), float('nan'), float('nan')], \n",
" 'gender': ['FEMALE', 'FEMALE', 'FEMALE', 'FEMALE', 'FEMALE']}\n",
"\n",
"# Select age column - choose the one with non-NaN values\n",
"age_col = None\n",
"for col_name, values in age_columns.items():\n",
" # Check if the column has non-NaN values\n",
" if not all(pd.isna(val) for val in values):\n",
" # Prefer direct age values over days_to_birth\n",
" if 'age' in col_name.lower():\n",
" age_col = col_name\n",
" break\n",
"\n",
"# If no age column with 'age' in name found, consider days_to_birth\n",
"if age_col is None and 'days_to_birth' in age_columns and not all(pd.isna(val) for val in age_columns['days_to_birth']):\n",
" age_col = 'days_to_birth'\n",
"\n",
"# Select gender column - choose the one with non-NaN values\n",
"gender_col = None\n",
"for col_name, values in gender_columns.items():\n",
" # Check if the column has non-NaN values\n",
" if not all(pd.isna(val) for val in values):\n",
" gender_col = col_name\n",
" break\n",
"\n",
"# Print the selected columns\n",
"print(f\"Selected age column: {age_col}\")\n",
"print(f\"Selected gender column: {gender_col}\")\n"
]
},
{
"cell_type": "markdown",
"id": "21cc18c8",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "82f37bee",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:36:25.529497Z",
"iopub.status.busy": "2025-03-25T07:36:25.529388Z",
"iopub.status.idle": "2025-03-25T07:38:03.424241Z",
"shell.execute_reply": "2025-03-25T07:38:03.423858Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data saved to ../../output/preprocess/Longevity/gene_data/TCGA.csv\n",
"Gene expression data shape after normalization: (19848, 1218)\n",
"Clinical data saved to ../../output/preprocess/Longevity/clinical_data/TCGA.csv\n",
"Clinical data shape: (1247, 3)\n",
"Number of samples in clinical data: 1247\n",
"Number of samples in genetic data: 1218\n",
"Number of common samples: 1218\n",
"Linked data shape: (1218, 19851)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (1218, 19851)\n",
"For the feature 'Longevity', the least common label is '0' with 114 occurrences. This represents 9.36% of the dataset.\n",
"The distribution of the feature 'Longevity' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 48.0\n",
" 50% (Median): 58.0\n",
" 75%: 67.0\n",
"Min: 26.0\n",
"Max: 90.0\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '1.0' with 13 occurrences. This represents 1.07% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Longevity/TCGA.csv\n",
"Preprocessing completed.\n"
]
}
],
"source": [
"# Step 1: Extract and standardize clinical features\n",
"# Create clinical features dataframe with trait (Canavan Disease) using patient IDs\n",
"clinical_features = tcga_select_clinical_features(\n",
" clinical_df, \n",
" trait=trait, \n",
" age_col=age_col, \n",
" gender_col=gender_col\n",
")\n",
"\n",
"# Step 2: Normalize gene symbols in the gene expression data\n",
"# The gene symbols in TCGA genetic data are already standardized, but we'll normalize them for consistency\n",
"normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)\n",
"\n",
"# Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_df.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
"print(f\"Gene expression data shape after normalization: {normalized_gene_df.shape}\")\n",
"\n",
"# Step 3: Link clinical and genetic data\n",
"# Transpose genetic data to have samples as rows and genes as columns\n",
"genetic_df_t = normalized_gene_df.T\n",
"# Save the clinical data for reference\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_features.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"print(f\"Clinical data shape: {clinical_features.shape}\")\n",
"\n",
"# Verify common indices between clinical and genetic data\n",
"clinical_indices = set(clinical_features.index)\n",
"genetic_indices = set(genetic_df_t.index)\n",
"common_indices = clinical_indices.intersection(genetic_indices)\n",
"print(f\"Number of samples in clinical data: {len(clinical_indices)}\")\n",
"print(f\"Number of samples in genetic data: {len(genetic_indices)}\")\n",
"print(f\"Number of common samples: {len(common_indices)}\")\n",
"\n",
"# Link the data by using the common indices\n",
"linked_data = pd.concat([clinical_features.loc[list(common_indices)], genetic_df_t.loc[list(common_indices)]], axis=1)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# Step 4: Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait_col=trait)\n",
"print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# Step 5: Determine whether the trait and demographic features are severely biased\n",
"trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)\n",
"\n",
"# Step 6: Conduct final quality validation and save information\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=True,\n",
" is_biased=trait_biased,\n",
" df=linked_data,\n",
" note=f\"Dataset contains TCGA glioma and brain tumor samples with gene expression and clinical information for {trait}.\"\n",
")\n",
"\n",
"# Step 7: Save linked data if usable\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset deemed not usable based on validation criteria. Data not saved.\")\n",
"\n",
"print(\"Preprocessing completed.\")"
]
}
],
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|