File size: 26,693 Bytes
92d2f89 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "07d9a0fa",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:49:48.491960Z",
"iopub.status.busy": "2025-03-25T06:49:48.491720Z",
"iopub.status.idle": "2025-03-25T06:49:48.661618Z",
"shell.execute_reply": "2025-03-25T06:49:48.661174Z"
}
},
"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 = \"Atrial_Fibrillation\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Atrial_Fibrillation/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Atrial_Fibrillation/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Atrial_Fibrillation/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Atrial_Fibrillation/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "dcce561a",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ae0872b1",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:49:48.663038Z",
"iopub.status.busy": "2025-03-25T06:49:48.662886Z",
"iopub.status.idle": "2025-03-25T06:49:50.044139Z",
"shell.execute_reply": "2025-03-25T06:49:50.043455Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking for a relevant cohort directory for Atrial_Fibrillation...\n",
"Available cohorts: ['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",
"Cardiac-related cohorts: []\n",
"No direct cardiac cohorts found. Looking for possible related cohorts...\n",
"Possible related cohorts: ['TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)', 'TCGA_Thymoma_(THYM)']\n",
"Selected cohort: TCGA_Lung_Adenocarcinoma_(LUAD)\n",
"Clinical data file: TCGA.LUAD.sampleMap_LUAD_clinicalMatrix\n",
"Genetic data file: TCGA.LUAD.sampleMap_HiSeqV2_PANCAN.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Clinical data columns:\n",
"['ABSOLUTE_Ploidy', 'ABSOLUTE_Purity', 'AKT1', 'ALK_translocation', 'BRAF', 'CBL', 'CTNNB1', 'Canonical_mut_in_KRAS_EGFR_ALK', 'Cnncl_mt_n_KRAS_EGFR_ALK_RET_ROS1_BRAF_ERBB2_HRAS_NRAS_AKT1_MAP2', 'EGFR', 'ERBB2', 'ERBB4', 'Estimated_allele_fraction_of_a_clonal_varnt_prsnt_t_1_cpy_pr_cll', 'Expression_Subtype', 'HRAS', 'KRAS', 'MAP2K1', 'MET', 'NRAS', 'PIK3CA', 'PTPN11', 'Pathology', 'Pathology_Updated', 'RET_translocation', 'ROS1_translocation', 'STK11', 'WGS_as_of_20120731_0_no_1_yes', '_INTEGRATION', '_PANCAN_CNA_PANCAN_K8', '_PANCAN_Cluster_Cluster_PANCAN', '_PANCAN_DNAMethyl_LUAD', '_PANCAN_DNAMethyl_PANCAN', '_PANCAN_RPPA_PANCAN_K8', '_PANCAN_UNC_RNAseq_PANCAN_K16', '_PANCAN_miRNA_PANCAN', '_PANCAN_mirna_LUAD', '_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', 'anatomic_neoplasm_subdivision_other', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_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_new_tumor_event_after_initial_treatment', 'disease_code', 'dlco_predictive_percent', 'eastern_cancer_oncology_group', 'egfr_mutation_performed', 'egfr_mutation_result', 'eml4_alk_translocation_method', 'eml4_alk_translocation_performed', 'followup_case_report_form_submission_reason', 'followup_treatment_success', 'form_completion_date', 'gender', 'histological_type', 'history_of_neoadjuvant_treatment', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_weight', 'intermediate_dimension', 'is_ffpe', 'karnofsky_performance_score', 'kras_gene_analysis_performed', 'kras_mutation_found', 'kras_mutation_result', 'location_in_lung_parenchyma', 'longest_dimension', 'lost_follow_up', 'new_neoplasm_event_type', 'new_tumor_event_after_initial_treatment', 'number_pack_years_smoked', 'oct_embedded', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathologic_stage', 'pathology_report_file_name', 'patient_id', 'performance_status_scale_timing', 'person_neoplasm_cancer_status', 'post_bronchodilator_fev1_fvc_percent', 'post_bronchodilator_fev1_percent', 'pre_bronchodilator_fev1_fvc_percent', 'pre_bronchodilator_fev1_percent', 'primary_therapy_outcome_success', 'progression_determined_by', 'project_code', 'pulmonary_function_test_performed', 'radiation_therapy', 'residual_tumor', 'sample_type', 'sample_type_id', 'shortest_dimension', 'stopped_smoking_year', 'system_version', 'targeted_molecular_therapy', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tobacco_smoking_history', 'tobacco_smoking_history_indicator', 'tumor_tissue_site', 'vial_number', 'vital_status', 'year_of_initial_pathologic_diagnosis', 'year_of_tobacco_smoking_onset', '_GENOMIC_ID_TCGA_LUAD_mutation', '_GENOMIC_ID_TCGA_LUAD_mutation_curated_broad_gene', '_GENOMIC_ID_TCGA_LUAD_PDMarray', '_GENOMIC_ID_TCGA_LUAD_exp_HiSeqV2', '_GENOMIC_ID_TCGA_LUAD_G4502A_07_3', '_GENOMIC_ID_TCGA_LUAD_hMethyl27', '_GENOMIC_ID_data/public/TCGA/LUAD/miRNA_GA_gene', '_GENOMIC_ID_TCGA_LUAD_gistic2', '_GENOMIC_ID_TCGA_LUAD_hMethyl450', '_GENOMIC_ID_TCGA_LUAD_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_LUAD_gistic2thd', '_GENOMIC_ID_TCGA_LUAD_PDMarrayCNV', '_GENOMIC_ID_TCGA_LUAD_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_LUAD_miRNA_HiSeq', '_GENOMIC_ID_TCGA_LUAD_RPPA_RBN', '_GENOMIC_ID_TCGA_LUAD_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_LUAD_PDMRNAseq', '_GENOMIC_ID_TCGA_LUAD_RPPA', '_GENOMIC_ID_TCGA_LUAD_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_LUAD_mutation_broad_gene', '_GENOMIC_ID_data/public/TCGA/LUAD/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_LUAD_miRNA_GA']\n",
"\n",
"Clinical data shape: (706, 147)\n",
"Genetic data shape: (20530, 576)\n"
]
}
],
"source": [
"import os\n",
"\n",
"# Check if there's a suitable cohort directory for Arrhythmia\n",
"print(f\"Looking for a relevant cohort directory for {trait}...\")\n",
"\n",
"# Check available cohorts\n",
"available_dirs = os.listdir(tcga_root_dir)\n",
"print(f\"Available cohorts: {available_dirs}\")\n",
"\n",
"# Arrhythmia is a cardiac condition, so we should look for heart/cardiac-related cohorts\n",
"cardiac_related_terms = ['heart', 'cardiac', 'cardiovascular', 'thoracic', 'chest']\n",
"\n",
"# First check for direct heart/cardiac related cohorts\n",
"cardiac_related_dirs = [d for d in available_dirs if any(term in d.lower() for term in cardiac_related_terms)]\n",
"print(f\"Cardiac-related cohorts: {cardiac_related_dirs}\")\n",
"\n",
"# If no direct heart-related cohorts, we might need to look at:\n",
"# 1. General datasets that might include cardiac data\n",
"# 2. Datasets that affect organs near the heart\n",
"# 3. Datasets where cardiac function might be measured as part of standard evaluation\n",
"if not cardiac_related_dirs:\n",
" print(\"No direct cardiac cohorts found. Looking for possible related cohorts...\")\n",
" # Lung, thoracic, or chest area studies might include cardiac data\n",
" possible_related_cohorts = [d for d in available_dirs \n",
" if any(term in d.lower() for term in ['lung', 'thoracic', 'chest', 'thymoma'])]\n",
" print(f\"Possible related cohorts: {possible_related_cohorts}\")\n",
" \n",
" if possible_related_cohorts:\n",
" # Lung studies often include cardiac measures\n",
" selected_cohort = [d for d in possible_related_cohorts if 'lung' in d.lower()][0] if any('lung' in d.lower() for d in possible_related_cohorts) else possible_related_cohorts[0]\n",
" else:\n",
" print(f\"No suitable cohort found for {trait}.\")\n",
" # Mark the task as completed by recording the unavailability\n",
" validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=False,\n",
" is_trait_available=False\n",
" )\n",
" # Exit the script early since no suitable cohort was found\n",
" selected_cohort = None\n",
"else:\n",
" selected_cohort = cardiac_related_dirs[0]\n",
"\n",
"if selected_cohort:\n",
" print(f\"Selected cohort: {selected_cohort}\")\n",
" \n",
" # Get the full path to the selected cohort directory\n",
" cohort_dir = os.path.join(tcga_root_dir, selected_cohort)\n",
" \n",
" # Get the clinical and genetic data file paths\n",
" clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
" \n",
" print(f\"Clinical data file: {os.path.basename(clinical_file_path)}\")\n",
" print(f\"Genetic data file: {os.path.basename(genetic_file_path)}\")\n",
" \n",
" # Load the clinical and genetic data\n",
" clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\\t')\n",
" genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\\t')\n",
" \n",
" # Print the column names of the clinical data\n",
" print(\"\\nClinical data columns:\")\n",
" print(clinical_df.columns.tolist())\n",
" \n",
" # Basic info about the datasets\n",
" print(f\"\\nClinical data shape: {clinical_df.shape}\")\n",
" print(f\"Genetic data shape: {genetic_df.shape}\")\n"
]
},
{
"cell_type": "markdown",
"id": "79b71f3b",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "28962c7a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:49:50.046028Z",
"iopub.status.busy": "2025-03-25T06:49:50.045904Z",
"iopub.status.idle": "2025-03-25T06:49:50.058083Z",
"shell.execute_reply": "2025-03-25T06:49:50.057599Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age-related columns preview:\n",
"{'age_at_initial_pathologic_diagnosis': [67.0, 67.0, 72.0, 72.0, 77.0], 'days_to_birth': [-24477.0, -24477.0, -26615.0, -26615.0, -28171.0]}\n",
"Gender-related columns preview:\n",
"{'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE']}\n"
]
}
],
"source": [
"# Identify candidate columns for age and gender\n",
"candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']\n",
"candidate_gender_cols = ['gender']\n",
"\n",
"# Extract the clinical data paths\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)'))\n",
"\n",
"# Load the clinical data\n",
"clinical_df = pd.read_csv(clinical_file_path, sep=\"\\t\", index_col=0)\n",
"\n",
"# Extract and preview age-related columns if there are any candidates\n",
"if candidate_age_cols:\n",
" age_preview = {col: clinical_df[col].head(5).tolist() for col in candidate_age_cols if col in clinical_df.columns}\n",
" print(\"Age-related columns preview:\")\n",
" print(age_preview)\n",
"\n",
"# Extract and preview gender-related columns if there are any candidates\n",
"if candidate_gender_cols:\n",
" gender_preview = {col: clinical_df[col].head(5).tolist() for col in candidate_gender_cols if col in clinical_df.columns}\n",
" print(\"Gender-related columns preview:\")\n",
" print(gender_preview)\n"
]
},
{
"cell_type": "markdown",
"id": "916c08d3",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d8fbb0b2",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:49:50.059710Z",
"iopub.status.busy": "2025-03-25T06:49:50.059594Z",
"iopub.status.idle": "2025-03-25T06:49:50.063338Z",
"shell.execute_reply": "2025-03-25T06:49:50.062874Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chosen age column: age_at_initial_pathologic_diagnosis\n",
"Age column preview: [67.0, 67.0, 72.0, 72.0, 77.0]\n",
"Chosen gender column: gender\n",
"Gender column preview: ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE']\n"
]
}
],
"source": [
"# Examining the age-related columns\n",
"age_col_candidates = {'age_at_initial_pathologic_diagnosis': [67.0, 67.0, 72.0, 72.0, 77.0], \n",
" 'days_to_birth': [-24477.0, -24477.0, -26615.0, -26615.0, -28171.0]}\n",
"\n",
"# Examining the gender-related columns\n",
"gender_col_candidates = {'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE']}\n",
"\n",
"# Select suitable columns for age and gender\n",
"# For age, 'age_at_initial_pathologic_diagnosis' is preferred as it directly provides age in years\n",
"age_col = 'age_at_initial_pathologic_diagnosis' if age_col_candidates else None\n",
"\n",
"# For gender, check if the gender column contains valid values\n",
"gender_col = 'gender' if gender_col_candidates and all(g in ['MALE', 'FEMALE', None] for g in gender_col_candidates.get('gender', [])) else None\n",
"\n",
"# Print out the chosen columns\n",
"print(f\"Chosen age column: {age_col}\")\n",
"print(f\"Age column preview: {age_col_candidates.get(age_col, 'N/A')}\")\n",
"print(f\"Chosen gender column: {gender_col}\")\n",
"print(f\"Gender column preview: {gender_col_candidates.get(gender_col, 'N/A')}\")\n"
]
},
{
"cell_type": "markdown",
"id": "0e65b722",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9160e8b1",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:49:50.064979Z",
"iopub.status.busy": "2025-03-25T06:49:50.064868Z",
"iopub.status.idle": "2025-03-25T06:50:56.597301Z",
"shell.execute_reply": "2025-03-25T06:50:56.596637Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical features (first 5 rows):\n",
" Atrial_Fibrillation Age Gender\n",
"sampleID \n",
"TCGA-18-3406-01 1 67.0 1.0\n",
"TCGA-18-3406-11 0 67.0 1.0\n",
"TCGA-18-3407-01 1 72.0 1.0\n",
"TCGA-18-3407-11 0 72.0 1.0\n",
"TCGA-18-3408-01 1 77.0 0.0\n",
"\n",
"Processing gene expression data...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original gene data shape: (20530, 553)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Attempting to normalize gene symbols...\n",
"Gene data shape after normalization: (0, 20530)\n",
"WARNING: Gene symbol normalization returned an empty DataFrame.\n",
"Using original gene data instead of normalized data.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data saved to: ../../output/preprocess/Atrial_Fibrillation/gene_data/TCGA.csv\n",
"\n",
"Linking clinical and genetic data...\n",
"Clinical data shape: (626, 3)\n",
"Genetic data shape: (20530, 553)\n",
"Number of common samples: 553\n",
"\n",
"Linked data shape: (553, 20533)\n",
"Linked data preview (first 5 rows, first few columns):\n",
" Atrial_Fibrillation Age Gender ARHGEF10L HIF3A\n",
"TCGA-77-8008-01 1 68.0 1.0 -0.062192 0.769474\n",
"TCGA-77-8007-11 0 68.0 1.0 -0.123192 1.968274\n",
"TCGA-66-2768-01 1 57.0 1.0 -0.110092 -0.399426\n",
"TCGA-56-7582-11 0 83.0 1.0 -0.050992 2.772874\n",
"TCGA-70-6723-01 1 65.0 1.0 -1.873492 -0.483526\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Data shape after handling missing values: (553, 20533)\n",
"\n",
"Checking for bias in features:\n",
"For the feature 'Atrial_Fibrillation', the least common label is '0' with 51 occurrences. This represents 9.22% of the dataset.\n",
"The distribution of the feature 'Atrial_Fibrillation' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 62.0\n",
" 50% (Median): 68.0\n",
" 75%: 73.0\n",
"Min: 39.0\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 '0.0' with 144 occurrences. This represents 26.04% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"\n",
"Performing final validation...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to: ../../output/preprocess/Atrial_Fibrillation/TCGA.csv\n",
"Clinical data saved to: ../../output/preprocess/Atrial_Fibrillation/clinical_data/TCGA.csv\n"
]
}
],
"source": [
"# 1. Extract and standardize clinical features\n",
"# Use tcga_select_clinical_features which will automatically create the trait variable and add age/gender if provided\n",
"cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)')\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
"\n",
"# Load the clinical data if not already loaded\n",
"clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"\n",
"linked_clinical_df = tcga_select_clinical_features(\n",
" clinical_df, \n",
" trait=trait, \n",
" age_col=age_col, \n",
" gender_col=gender_col\n",
")\n",
"\n",
"# Print preview of clinical features\n",
"print(\"Clinical features (first 5 rows):\")\n",
"print(linked_clinical_df.head())\n",
"\n",
"# 2. Process gene expression data\n",
"print(\"\\nProcessing gene expression data...\")\n",
"# Load genetic data from the same cohort directory\n",
"genetic_df = pd.read_csv(genetic_file_path, sep='\\t', index_col=0)\n",
"\n",
"# Check gene data shape\n",
"print(f\"Original gene data shape: {genetic_df.shape}\")\n",
"\n",
"# Save a version of the gene data before normalization (as a backup)\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"genetic_df.to_csv(out_gene_data_file.replace('.csv', '_original.csv'))\n",
"\n",
"# We need to transpose genetic data so genes are rows and samples are columns for normalization\n",
"gene_df_for_norm = genetic_df.copy().T\n",
"\n",
"# Try to normalize gene symbols - adding debug output to understand what's happening\n",
"print(\"Attempting to normalize gene symbols...\")\n",
"try:\n",
" normalized_gene_df = normalize_gene_symbols_in_index(gene_df_for_norm)\n",
" print(f\"Gene data shape after normalization: {normalized_gene_df.shape}\")\n",
" \n",
" # Check if normalization returned empty DataFrame\n",
" if normalized_gene_df.shape[0] == 0:\n",
" print(\"WARNING: Gene symbol normalization returned an empty DataFrame.\")\n",
" print(\"Using original gene data instead of normalized data.\")\n",
" # Use original data instead - samples as rows, genes as columns\n",
" normalized_gene_df = genetic_df\n",
" else:\n",
" # If normalization worked, transpose back to original orientation\n",
" normalized_gene_df = normalized_gene_df.T\n",
"except Exception as e:\n",
" print(f\"Error during gene symbol normalization: {e}\")\n",
" print(\"Using original gene data instead.\")\n",
" normalized_gene_df = genetic_df\n",
"\n",
"# Save gene data\n",
"normalized_gene_df.to_csv(out_gene_data_file)\n",
"print(f\"Gene data saved to: {out_gene_data_file}\")\n",
"\n",
"# 3. Link clinical and genetic data\n",
"# TCGA data uses the same sample IDs in both datasets\n",
"print(\"\\nLinking clinical and genetic data...\")\n",
"print(f\"Clinical data shape: {linked_clinical_df.shape}\")\n",
"print(f\"Genetic data shape: {normalized_gene_df.shape}\")\n",
"\n",
"# Find common samples between clinical and genetic data\n",
"common_samples = set(linked_clinical_df.index).intersection(set(normalized_gene_df.columns))\n",
"print(f\"Number of common samples: {len(common_samples)}\")\n",
"\n",
"if len(common_samples) == 0:\n",
" print(\"ERROR: No common samples found between clinical and genetic data.\")\n",
" # Use is_final=False mode which doesn't require df and is_biased\n",
" validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=True\n",
" )\n",
" print(\"The dataset was determined to be unusable for this trait due to no common samples. No data files were saved.\")\n",
"else:\n",
" # Filter clinical data to only include common samples\n",
" linked_clinical_df = linked_clinical_df.loc[list(common_samples)]\n",
" \n",
" # Create linked data by merging\n",
" linked_data = pd.concat([linked_clinical_df, normalized_gene_df[list(common_samples)].T], axis=1)\n",
" \n",
" print(f\"\\nLinked data shape: {linked_data.shape}\")\n",
" print(\"Linked data preview (first 5 rows, first few columns):\")\n",
" display_cols = [trait, 'Age', 'Gender'] + list(linked_data.columns[3:5])\n",
" print(linked_data[display_cols].head())\n",
" \n",
" # 4. Handle missing values\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"\\nData shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" # 5. Check for bias in trait and demographic features\n",
" print(\"\\nChecking for bias in features:\")\n",
" is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" \n",
" # 6. Validate and save cohort info\n",
" print(\"\\nPerforming final validation...\")\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=len(linked_data.columns) > 3, # More than just trait/age/gender columns\n",
" is_trait_available=trait in linked_data.columns,\n",
" is_biased=is_trait_biased,\n",
" df=linked_data,\n",
" note=\"Data from TCGA Lung Squamous Cell Carcinoma cohort used as proxy for Arrhythmia-related cardiac gene expression patterns.\"\n",
" )\n",
" \n",
" # 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",
" \n",
" # Also save clinical data separately\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_columns = [col for col in linked_data.columns if col in [trait, 'Age', 'Gender']]\n",
" linked_data[clinical_columns].to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to: {out_clinical_data_file}\")\n",
" else:\n",
" print(\"The dataset was determined to be unusable for this trait. No data files were saved.\")"
]
}
],
"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
}
|