File size: 36,484 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "c9f5e849",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:06:59.430425Z",
"iopub.status.busy": "2025-03-25T07:06:59.430317Z",
"iopub.status.idle": "2025-03-25T07:06:59.596854Z",
"shell.execute_reply": "2025-03-25T07:06:59.596488Z"
}
},
"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 = \"Cardiovascular_Disease\"\n",
"cohort = \"GSE235307\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Cardiovascular_Disease\"\n",
"in_cohort_dir = \"../../input/GEO/Cardiovascular_Disease/GSE235307\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Cardiovascular_Disease/GSE235307.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Cardiovascular_Disease/gene_data/GSE235307.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Cardiovascular_Disease/clinical_data/GSE235307.csv\"\n",
"json_path = \"../../output/preprocess/Cardiovascular_Disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "8e4e4ab0",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "db301040",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:06:59.598296Z",
"iopub.status.busy": "2025-03-25T07:06:59.598148Z",
"iopub.status.idle": "2025-03-25T07:07:00.020157Z",
"shell.execute_reply": "2025-03-25T07:07:00.019763Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Gene expression and atrial fibrillation prediction\"\n",
"!Series_summary\t\"The aim of this study was to identify a blood gene expression profile that predicts atrial fibrillation in heart failure patients\"\n",
"!Series_overall_design\t\"Cardiac blood samples were obtained from the coronary sinus during CRT-D (Cardiac Resynchronization Therapy - Defibrillator) placement in heart failure patients. Patients were followed during 1 year.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: Whole blood'], 1: ['gender: Male', 'gender: Female'], 2: ['age: 63', 'age: 60', 'age: 72', 'age: 66', 'age: 70', 'age: 64', 'age: 61', 'age: 44', 'age: 54', 'age: 50', 'age: 79', 'age: 51', 'age: 55', 'age: 67', 'age: 52', 'age: 73', 'age: 76', 'age: 43', 'age: 68', 'age: 78', 'age: 69', 'age: 57', 'age: 59', 'age: 53', 'age: 65', 'age: 56', 'age: 74', 'age: 38', 'age: 71', 'age: 37'], 3: ['cardiopathy: ischemic', 'cardiopathy: non ischemic', 'cardiopathy: mixed'], 4: ['cardiac rhythm at start of the study: Sinus rhythm'], 5: ['cardiac rhythm after 1 year follow-up: Sinus rhythm', 'cardiac rhythm after 1 year follow-up: Atrial fibrillation']}\n"
]
}
],
"source": [
"from tools.preprocess import *\n",
"# 1. Identify the paths to the SOFT file and the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Read the matrix file to obtain background information and sample characteristics data\n",
"background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
"clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
"\n",
"# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n",
"sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
"\n",
"# 4. Explicitly print out all the background information and the sample characteristics dictionary\n",
"print(\"Background Information:\")\n",
"print(background_info)\n",
"print(\"Sample Characteristics Dictionary:\")\n",
"print(sample_characteristics_dict)\n"
]
},
{
"cell_type": "markdown",
"id": "485069a5",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "73e6af5a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:07:00.021587Z",
"iopub.status.busy": "2025-03-25T07:07:00.021460Z",
"iopub.status.idle": "2025-03-25T07:07:00.027523Z",
"shell.execute_reply": "2025-03-25T07:07:00.027220Z"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import json\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the dataset description, this appears to be gene expression data from blood samples\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# For trait: The trait appears to be \"Atrial fibrillation\" which can be inferred from row 5\n",
"# \"cardiac rhythm after 1 year follow-up: Sinus rhythm\" or \"cardiac rhythm after 1 year follow-up: Atrial fibrillation\"\n",
"trait_row = 5\n",
"\n",
"# For age: Age data is available in row 2\n",
"age_row = 2\n",
"\n",
"# For gender: Gender data is available in row 1\n",
"gender_row = 1\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert atrial fibrillation status to binary (0: No AF, 1: AF).\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" value = value.strip() if isinstance(value, str) else value\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"Atrial fibrillation\" in value:\n",
" return 1 # Atrial fibrillation is present (positive case)\n",
" elif \"Sinus rhythm\" in value:\n",
" return 0 # Normal sinus rhythm (negative case)\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous numeric value.\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" value = value.strip() if isinstance(value, str) else value\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary (0: Female, 1: Male).\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" value = value.strip() if isinstance(value, str) else value\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"Male\" in value or value.lower() == \"male\":\n",
" return 1\n",
" elif \"Female\" in value or value.lower() == \"female\":\n",
" return 0\n",
" return None\n",
"\n",
"# 3. Save Metadata - Initial Filtering\n",
"# Trait data availability is determined by whether trait_row is None\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" # Load clinical data\n",
" clinical_df_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
" if os.path.exists(clinical_df_path):\n",
" clinical_data = pd.read_csv(clinical_df_path)\n",
" \n",
" # Extract clinical features\n",
" clinical_features_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the extracted features\n",
" preview = preview_df(clinical_features_df)\n",
" print(\"Preview of clinical features:\")\n",
" print(preview)\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save to CSV\n",
" clinical_features_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "8366cd1d",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9862983c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:07:00.028687Z",
"iopub.status.busy": "2025-03-25T07:07:00.028577Z",
"iopub.status.idle": "2025-03-25T07:07:00.810364Z",
"shell.execute_reply": "2025-03-25T07:07:00.809966Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix file found: ../../input/GEO/Cardiovascular_Disease/GSE235307/GSE235307_series_matrix.txt.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape: (58717, 119)\n",
"First 20 gene/probe identifiers:\n",
"Index(['4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16',\n",
" '17', '18', '19', '20', '21', '22', '23'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Get the SOFT and matrix file paths again \n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"print(f\"Matrix file found: {matrix_file}\")\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" \n",
" # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "08213599",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "48d98187",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:07:00.811773Z",
"iopub.status.busy": "2025-03-25T07:07:00.811662Z",
"iopub.status.idle": "2025-03-25T07:07:00.813568Z",
"shell.execute_reply": "2025-03-25T07:07:00.813292Z"
}
},
"outputs": [],
"source": [
"# Examine the gene identifiers\n",
"# These appear to be numeric identifiers (4, 5, 6, etc.) which are not standard human gene symbols\n",
"# Standard human gene symbols would be like BRCA1, TP53, etc.\n",
"# Therefore, these identifiers need to be mapped to proper gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "bd8aaa0c",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "26aa25a9",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:07:00.814754Z",
"iopub.status.busy": "2025-03-25T07:07:00.814654Z",
"iopub.status.idle": "2025-03-25T07:07:12.419016Z",
"shell.execute_reply": "2025-03-25T07:07:12.418663Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"Columns in gene annotation: ['ID', 'COL', 'ROW', 'NAME', 'SPOT_ID', 'CONTROL_TYPE', 'REFSEQ', 'GB_ACC', 'LOCUSLINK_ID', 'GENE_SYMBOL', 'GENE_NAME', 'UNIGENE_ID', 'ENSEMBL_ID', 'ACCESSION_STRING', 'CHROMOSOMAL_LOCATION', 'CYTOBAND', 'DESCRIPTION', 'GO_ID', 'SEQUENCE']\n",
"{'ID': ['1', '2', '3', '4', '5'], 'COL': ['192', '192', '192', '192', '192'], 'ROW': [328.0, 326.0, 324.0, 322.0, 320.0], 'NAME': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'A_23_P117082', 'A_33_P3246448'], 'SPOT_ID': ['CONTROL', 'CONTROL', 'CONTROL', 'A_23_P117082', 'A_33_P3246448'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, 'NM_015987', 'NM_080671'], 'GB_ACC': [nan, nan, nan, 'NM_015987', 'NM_080671'], 'LOCUSLINK_ID': [nan, nan, nan, 50865.0, 23704.0], 'GENE_SYMBOL': [nan, nan, nan, 'HEBP1', 'KCNE4'], 'GENE_NAME': [nan, nan, nan, 'heme binding protein 1', 'potassium voltage-gated channel, Isk-related family, member 4'], 'UNIGENE_ID': [nan, nan, nan, 'Hs.642618', 'Hs.348522'], 'ENSEMBL_ID': [nan, nan, nan, 'ENST00000014930', 'ENST00000281830'], 'ACCESSION_STRING': [nan, nan, nan, 'ref|NM_015987|ens|ENST00000014930|gb|AF117615|gb|BC016277', 'ref|NM_080671|ens|ENST00000281830|tc|THC2655788'], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, 'chr12:13127906-13127847', 'chr2:223920197-223920256'], 'CYTOBAND': [nan, nan, nan, 'hs|12p13.1', 'hs|2q36.1'], 'DESCRIPTION': [nan, nan, nan, 'Homo sapiens heme binding protein 1 (HEBP1), mRNA [NM_015987]', 'Homo sapiens potassium voltage-gated channel, Isk-related family, member 4 (KCNE4), mRNA [NM_080671]'], 'GO_ID': [nan, nan, nan, 'GO:0005488(binding)|GO:0005576(extracellular region)|GO:0005737(cytoplasm)|GO:0005739(mitochondrion)|GO:0005829(cytosol)|GO:0007623(circadian rhythm)|GO:0020037(heme binding)', 'GO:0005244(voltage-gated ion channel activity)|GO:0005249(voltage-gated potassium channel activity)|GO:0006811(ion transport)|GO:0006813(potassium ion transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0016324(apical plasma membrane)'], 'SEQUENCE': [nan, nan, nan, 'AAGGGGGAAAATGTGATTTGTGCCTGATCTTTCATCTGTGATTCTTATAAGAGCTTTGTC', 'GCAAGTCTCTCTGCACCTATTAAAAAGTGATGTATATACTTCCTTCTTATTCTGTTGAGT']}\n",
"\n",
"Searching for platform information in SOFT file:\n",
"Platform ID not found in first 100 lines\n",
"\n",
"Searching for gene symbol information in SOFT file:\n",
"Found references to gene symbols:\n",
"#GENE_SYMBOL = Gene Symbol\n",
"ID\tCOL\tROW\tNAME\tSPOT_ID\tCONTROL_TYPE\tREFSEQ\tGB_ACC\tLOCUSLINK_ID\tGENE_SYMBOL\tGENE_NAME\tUNIGENE_ID\tENSEMBL_ID\tACCESSION_STRING\tCHROMOSOMAL_LOCATION\tCYTOBAND\tDESCRIPTION\tGO_ID\tSEQUENCE\n",
"8\t192\t314\tA_33_P3319925\tA_33_P3319925\tFALSE\tXM_001133269\tXM_001133269\t730249\tIRG1\timmunoresponsive 1 homolog (mouse)\tHs.160789\tENST00000449753\tens|ENST00000449753|ens|ENST00000377462|ref|XM_001133269|ref|XM_003403661\tchr13:77532009-77532068\ths|13q22.3\timmunoresponsive 1 homolog (mouse) [Source:HGNC Symbol;Acc:33904] [ENST00000449753]\tGO:0019543(propionate catabolic process)|GO:0032496(response to lipopolysaccharide)|GO:0047547(2-methylcitrate dehydratase activity)\tAGAAGACCTAGAAGACTGTTCTGTGTTAACTACACTTCTCAAAGGACCCTCTCCACCAGA\n",
"21\t192\t288\tA_33_P3261373\tens|ENST00000319813|tc|NP511499\tFALSE\t\t\t\t\t\t\tENST00000319813\tens|ENST00000319813|tc|NP511499\tchr11:48387097-48387038\ths|11p11.2\tolfactory receptor, family 4, subfamily C, member 5 [Source:HGNC Symbol;Acc:14702] [ENST00000319813]\t\tGAAAAATGCCATGAAGCAGCTCTGGAGCCAAATAATCTGGGGTAACAATTTGTGTGATTA\n",
"25\t192\t280\tA_24_P286898\tA_24_P286898\tFALSE\t\tAB074280\t5599\tMAPK8\tmitogen-activated protein kinase 8\tHs.522924\tENST00000374189\tens|ENST00000374189|ens|ENST00000374182|ens|ENST00000374179|ens|ENST00000374176\tchr10:49647005-49647064\ths|10q11.22\tmitogen-activated protein kinase 8 [Source:HGNC Symbol;Acc:6881] [ENST00000374189]\tGO:0000166(nucleotide binding)|GO:0001503(ossification)|GO:0002224(toll-like receptor signaling pathway)|GO:0002755(MyD88-dependent toll-like receptor signaling pathway)|GO:0002756(MyD88-independent toll-like receptor signaling pathway)|GO:0004674(protein serine/threonine kinase activity)|GO:0004705(JUN kinase activity)|GO:0004707(MAP kinase activity)|GO:0005515(protein binding)|GO:0005524(ATP binding)|GO:0005634(nucleus)|GO:0005654(nucleoplasm)|GO:0005737(cytoplasm)|GO:0005739(mitochondrion)|GO:0005829(cytosol)|GO:0006915(apoptosis)|GO:0006950(response to stress)|GO:0007254(JNK cascade)|GO:0007258(JUN phosphorylation)|GO:0008063(Toll signaling pathway)|GO:0008624(induction of apoptosis by extracellular signals)|GO:0008629(induction of apoptosis by intracellular signals)|GO:0008633(activation of pro-apoptotic gene products)|GO:0009411(response to UV)|GO:0018105(peptidyl-serine phosphorylation)|GO:0018107(peptidyl-threonine phosphorylation)|GO:0031063(regulation of histone deacetylation)|GO:0031558(induction of apoptosis in response to chemical stimulus)|GO:0032091(negative regulation of protein binding)|GO:0032880(regulation of protein localization)|GO:0034130(toll-like receptor 1 signaling pathway)|GO:0034134(toll-like receptor 2 signaling pathway)|GO:0034138(toll-like receptor 3 signaling pathway)|GO:0034142(toll-like receptor 4 signaling pathway)|GO:0035033(histone deacetylase regulator activity)|GO:0042826(histone deacetylase binding)|GO:0043066(negative regulation of apoptosis)|GO:0045087(innate immune response)|GO:0046686(response to cadmium ion)|GO:0048011(nerve growth factor receptor signaling pathway)|GO:0051090(regulation of sequence-specific DNA binding transcription factor activity)|GO:0051403(stress-activated MAPK cascade)|GO:0071260(cellular response to mechanical stimulus)|GO:0090045(positive regulation of deacetylase activity)|GO:2000017(positive regulation of determination of dorsal identity)\tTTTGAGAAGCTGTTAATCTTTTAGCTGAATAATGAAGTTAGACTGAATTACGTGTCTCCC\n",
"\n",
"Checking for additional annotation files in the directory:\n",
"[]\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
"print(\"\\nGene annotation preview:\")\n",
"print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
"print(preview_df(gene_annotation, n=5))\n",
"\n",
"# Let's look for platform information in the SOFT file to understand the annotation better\n",
"print(\"\\nSearching for platform information in SOFT file:\")\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" for i, line in enumerate(f):\n",
" if '!Series_platform_id' in line:\n",
" print(line.strip())\n",
" break\n",
" if i > 100: # Limit search to first 100 lines\n",
" print(\"Platform ID not found in first 100 lines\")\n",
" break\n",
"\n",
"# Check if the SOFT file includes any reference to gene symbols\n",
"print(\"\\nSearching for gene symbol information in SOFT file:\")\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" gene_symbol_lines = []\n",
" for i, line in enumerate(f):\n",
" if 'GENE_SYMBOL' in line or 'gene_symbol' in line.lower() or 'symbol' in line.lower():\n",
" gene_symbol_lines.append(line.strip())\n",
" if i > 1000 and len(gene_symbol_lines) > 0: # Limit search but ensure we found something\n",
" break\n",
" \n",
" if gene_symbol_lines:\n",
" print(\"Found references to gene symbols:\")\n",
" for line in gene_symbol_lines[:5]: # Show just first 5 matches\n",
" print(line)\n",
" else:\n",
" print(\"No explicit gene symbol references found in first 1000 lines\")\n",
"\n",
"# Look for alternative annotation files or references in the directory\n",
"print(\"\\nChecking for additional annotation files in the directory:\")\n",
"all_files = os.listdir(in_cohort_dir)\n",
"print([f for f in all_files if 'annotation' in f.lower() or 'platform' in f.lower() or 'gpl' in f.lower()])\n"
]
},
{
"cell_type": "markdown",
"id": "de7c8663",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "1d0891b7",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:07:12.420378Z",
"iopub.status.busy": "2025-03-25T07:07:12.420244Z",
"iopub.status.idle": "2025-03-25T07:07:14.574295Z",
"shell.execute_reply": "2025-03-25T07:07:14.573914Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping preview:\n",
"{'ID': ['4', '5', '6', '7', '8'], 'Gene': ['HEBP1', 'KCNE4', 'BPIFA3', 'LOC100129869', 'IRG1']}\n",
"Shape of gene mapping dataframe: (54295, 2)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data after mapping:\n",
"Shape of gene expression data: (20353, 119)\n",
"First 10 gene symbols:\n",
"['A1BG', 'A1BG-AS1', 'A1CF', 'A2LD1', 'A2M', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n",
"\n",
"Gene expression data after normalization:\n",
"Shape of gene expression data after normalization: (19847, 119)\n",
"First 10 normalized gene symbols:\n",
"['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06', 'AAA1']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data saved to ../../output/preprocess/Cardiovascular_Disease/gene_data/GSE235307.csv\n"
]
}
],
"source": [
"# 1. Observe the gene identifier and gene symbol columns in the gene annotation\n",
"# From the preview, we can see:\n",
"# - The gene identifiers in gene_data are numeric IDs (like '4', '5', '6')\n",
"# - In gene_annotation, the 'ID' column contains similar numeric identifiers\n",
"# - The 'GENE_SYMBOL' column contains the human gene symbols like 'HEBP1', 'KCNE4'\n",
"\n",
"# 2. Extract the gene identifier and gene symbol columns\n",
"gene_mapping_df = get_gene_mapping(\n",
" annotation=gene_annotation,\n",
" prob_col='ID', # The column with probe IDs matching gene_data index\n",
" gene_col='GENE_SYMBOL' # The column with human gene symbols\n",
")\n",
"\n",
"print(\"Gene mapping preview:\")\n",
"print(preview_df(gene_mapping_df, n=5))\n",
"print(f\"Shape of gene mapping dataframe: {gene_mapping_df.shape}\")\n",
"\n",
"# 3. Apply gene mapping to convert from probe-level to gene-level expression\n",
"gene_data = apply_gene_mapping(\n",
" expression_df=gene_data,\n",
" mapping_df=gene_mapping_df\n",
")\n",
"\n",
"print(\"\\nGene expression data after mapping:\")\n",
"print(f\"Shape of gene expression data: {gene_data.shape}\")\n",
"print(\"First 10 gene symbols:\")\n",
"print(list(gene_data.index[:10]))\n",
"\n",
"# Apply gene symbol normalization to standardize symbols and aggregate duplicates\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(\"\\nGene expression data after normalization:\")\n",
"print(f\"Shape of gene expression data after normalization: {gene_data.shape}\")\n",
"print(\"First 10 normalized gene symbols:\")\n",
"print(list(gene_data.index[:10]))\n",
"\n",
"# Create directory if it doesn't exist\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"\n",
"# Save the gene expression data to the specified output file\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "c54021b0",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "fce3430b",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:07:14.575915Z",
"iopub.status.busy": "2025-03-25T07:07:14.575794Z",
"iopub.status.idle": "2025-03-25T07:07:28.528574Z",
"shell.execute_reply": "2025-03-25T07:07:28.527965Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical features shape: (3, 119)\n",
"Clinical features preview:\n",
"{'GSM7498589': [0.0, 63.0, 1.0], 'GSM7498590': [0.0, 60.0, 1.0], 'GSM7498591': [0.0, 60.0, 1.0], 'GSM7498592': [0.0, 72.0, 1.0], 'GSM7498593': [0.0, 63.0, 1.0], 'GSM7498594': [0.0, 66.0, 0.0], 'GSM7498595': [0.0, 70.0, 1.0], 'GSM7498596': [0.0, 64.0, 1.0], 'GSM7498597': [0.0, 63.0, 1.0], 'GSM7498598': [0.0, 61.0, 1.0], 'GSM7498599': [0.0, 70.0, 0.0], 'GSM7498600': [0.0, 64.0, 1.0], 'GSM7498601': [0.0, 63.0, 1.0], 'GSM7498602': [0.0, 44.0, 1.0], 'GSM7498603': [0.0, 54.0, 1.0], 'GSM7498604': [0.0, 44.0, 1.0], 'GSM7498605': [0.0, 50.0, 1.0], 'GSM7498606': [1.0, 79.0, 1.0], 'GSM7498607': [0.0, 63.0, 1.0], 'GSM7498608': [0.0, 63.0, 0.0], 'GSM7498609': [1.0, 64.0, 1.0], 'GSM7498610': [0.0, 60.0, 1.0], 'GSM7498611': [0.0, 51.0, 1.0], 'GSM7498612': [0.0, 55.0, 1.0], 'GSM7498613': [0.0, 55.0, 1.0], 'GSM7498614': [1.0, 67.0, 1.0], 'GSM7498615': [0.0, 52.0, 1.0], 'GSM7498616': [0.0, 70.0, 0.0], 'GSM7498617': [0.0, 54.0, 1.0], 'GSM7498618': [0.0, 54.0, 1.0], 'GSM7498619': [0.0, 73.0, 1.0], 'GSM7498620': [0.0, 54.0, 0.0], 'GSM7498621': [0.0, 76.0, 1.0], 'GSM7498622': [0.0, 76.0, 1.0], 'GSM7498623': [0.0, 43.0, 0.0], 'GSM7498624': [0.0, 64.0, 1.0], 'GSM7498625': [0.0, 64.0, 1.0], 'GSM7498626': [0.0, 68.0, 0.0], 'GSM7498627': [0.0, 43.0, 1.0], 'GSM7498628': [1.0, 54.0, 1.0], 'GSM7498629': [0.0, 72.0, 0.0], 'GSM7498630': [0.0, 51.0, 1.0], 'GSM7498631': [0.0, 68.0, 0.0], 'GSM7498632': [0.0, 50.0, 0.0], 'GSM7498633': [0.0, 78.0, 1.0], 'GSM7498634': [1.0, 69.0, 1.0], 'GSM7498635': [0.0, 64.0, 0.0], 'GSM7498636': [0.0, 54.0, 1.0], 'GSM7498637': [0.0, 54.0, 1.0], 'GSM7498638': [0.0, 57.0, 1.0], 'GSM7498639': [0.0, 55.0, 0.0], 'GSM7498640': [0.0, 60.0, 1.0], 'GSM7498641': [0.0, 59.0, 1.0], 'GSM7498642': [0.0, 54.0, 1.0], 'GSM7498643': [0.0, 54.0, 1.0], 'GSM7498644': [0.0, 54.0, 1.0], 'GSM7498645': [0.0, 54.0, 1.0], 'GSM7498646': [0.0, 53.0, 1.0], 'GSM7498647': [0.0, 52.0, 0.0], 'GSM7498648': [0.0, 68.0, 1.0], 'GSM7498649': [0.0, 72.0, 0.0], 'GSM7498650': [0.0, 70.0, 1.0], 'GSM7498651': [0.0, 65.0, 1.0], 'GSM7498652': [0.0, 64.0, 1.0], 'GSM7498653': [0.0, 56.0, 0.0], 'GSM7498654': [0.0, 56.0, 0.0], 'GSM7498655': [0.0, 63.0, 1.0], 'GSM7498656': [0.0, 57.0, 1.0], 'GSM7498657': [0.0, 63.0, 1.0], 'GSM7498658': [0.0, 68.0, 1.0], 'GSM7498659': [0.0, 66.0, 0.0], 'GSM7498660': [0.0, 74.0, 0.0], 'GSM7498661': [0.0, 38.0, 1.0], 'GSM7498662': [0.0, 56.0, 1.0], 'GSM7498663': [0.0, 57.0, 1.0], 'GSM7498664': [0.0, 71.0, 0.0], 'GSM7498665': [1.0, 78.0, 0.0], 'GSM7498666': [0.0, 51.0, 1.0], 'GSM7498667': [0.0, 50.0, 1.0], 'GSM7498668': [0.0, 37.0, 1.0], 'GSM7498669': [0.0, 37.0, 1.0], 'GSM7498670': [0.0, 70.0, 0.0], 'GSM7498671': [0.0, 72.0, 0.0], 'GSM7498672': [0.0, 73.0, 1.0], 'GSM7498673': [0.0, 69.0, 0.0], 'GSM7498674': [0.0, 69.0, 0.0], 'GSM7498675': [1.0, 63.0, 1.0], 'GSM7498676': [0.0, 62.0, 0.0], 'GSM7498677': [0.0, 59.0, 0.0], 'GSM7498678': [0.0, 67.0, 1.0], 'GSM7498679': [0.0, 76.0, 1.0], 'GSM7498680': [0.0, 63.0, 1.0], 'GSM7498681': [0.0, 55.0, 1.0], 'GSM7498682': [0.0, 57.0, 1.0], 'GSM7498683': [0.0, 53.0, 1.0], 'GSM7498684': [0.0, 59.0, 1.0], 'GSM7498685': [1.0, 77.0, 1.0], 'GSM7498686': [0.0, 54.0, 1.0], 'GSM7498687': [1.0, 64.0, 1.0], 'GSM7498688': [0.0, 75.0, 0.0], 'GSM7498689': [0.0, 75.0, 0.0], 'GSM7498690': [0.0, 72.0, 0.0], 'GSM7498691': [0.0, 58.0, 0.0], 'GSM7498692': [0.0, 75.0, 1.0], 'GSM7498693': [0.0, 78.0, 1.0], 'GSM7498694': [0.0, 58.0, 1.0], 'GSM7498695': [0.0, 64.0, 1.0], 'GSM7498696': [0.0, 63.0, 1.0], 'GSM7498697': [0.0, 61.0, 1.0], 'GSM7498698': [0.0, 60.0, 1.0], 'GSM7498699': [0.0, 59.0, 0.0], 'GSM7498700': [0.0, 68.0, 1.0], 'GSM7498701': [0.0, 77.0, 1.0], 'GSM7498702': [1.0, 57.0, 1.0], 'GSM7498703': [0.0, 62.0, 0.0], 'GSM7498704': [1.0, 66.0, 1.0], 'GSM7498705': [1.0, 57.0, 1.0], 'GSM7498706': [1.0, 65.0, 1.0], 'GSM7498707': [0.0, 59.0, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Cardiovascular_Disease/clinical_data/GSE235307.csv\n",
"Linked data shape: (119, 19850)\n",
"Linked data preview (first 5 rows, 5 columns):\n",
" Cardiovascular_Disease Age Gender A1BG A1BG-AS1\n",
"GSM7498589 0.0 63.0 1.0 1215.921532 167.933502\n",
"GSM7498590 0.0 60.0 1.0 1042.240181 156.514231\n",
"GSM7498591 0.0 60.0 1.0 860.505266 153.778492\n",
"GSM7498592 0.0 72.0 1.0 1016.786080 164.688762\n",
"GSM7498593 0.0 63.0 1.0 930.371907 153.624856\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (119, 19850)\n",
"For the feature 'Cardiovascular_Disease', the least common label is '1.0' with 13 occurrences. This represents 10.92% of the dataset.\n",
"The distribution of the feature 'Cardiovascular_Disease' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 55.0\n",
" 50% (Median): 63.0\n",
" 75%: 68.0\n",
"Min: 37.0\n",
"Max: 79.0\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 32 occurrences. This represents 26.89% 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/Cardiovascular_Disease/GSE235307.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols \n",
"# (Note: We already normalized in step 6, but let's explicitly ensure it's done properly)\n",
"\n",
"# 2. Load the clinical data and extract features using the correct trait_row and conversion functions from Step 2\n",
"background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
"clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
"_, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
"\n",
"# Define correct conversion functions matching the ones in Step 2\n",
"def convert_trait(value):\n",
" \"\"\"Convert atrial fibrillation status to binary (0: No AF, 1: AF).\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" value = value.strip() if isinstance(value, str) else value\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"Atrial fibrillation\" in value:\n",
" return 1 # Atrial fibrillation is present (positive case)\n",
" elif \"Sinus rhythm\" in value:\n",
" return 0 # Normal sinus rhythm (negative case)\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous numeric value.\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" value = value.strip() if isinstance(value, str) else value\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary (0: Female, 1: Male).\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" value = value.strip() if isinstance(value, str) else value\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"Male\" in value or value.lower() == \"male\":\n",
" return 1\n",
" elif \"Female\" in value or value.lower() == \"female\":\n",
" return 0\n",
" return None\n",
"\n",
"# Extract clinical features using the correct row indices from Step 2\n",
"clinical_features = geo_select_clinical_features(\n",
" clinical_df=clinical_data, \n",
" trait=trait, \n",
" trait_row=5, # Correctly using cardiac rhythm row as identified in Step 2\n",
" convert_trait=convert_trait,\n",
" age_row=2, # Age information from row 2\n",
" convert_age=convert_age,\n",
" gender_row=1, # Gender information from row 1\n",
" convert_gender=convert_gender\n",
")\n",
"\n",
"print(f\"Clinical features shape: {clinical_features.shape}\")\n",
"print(\"Clinical features preview:\")\n",
"print(preview_df(clinical_features))\n",
"\n",
"# Save the clinical data\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",
"\n",
"# 3. Link clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
"print(linked_data.iloc[:5, :5])\n",
"\n",
"# 4. Handle missing values\n",
"linked_data_clean = handle_missing_values(linked_data, trait)\n",
"print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n",
"\n",
"# 5. Check for bias in the dataset\n",
"is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n",
"\n",
"# 6. Conduct final quality validation\n",
"note = \"Dataset contains gene expression data from cardiac blood samples of heart failure patients, with atrial fibrillation status tracked over 1 year.\"\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=True,\n",
" is_biased=is_biased,\n",
" df=linked_data_clean,\n",
" note=note\n",
")\n",
"\n",
"# 7. Save the linked data if it's usable\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data_clean.to_csv(out_data_file, index=True)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset deemed not usable for associative studies. Linked data not 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
}
|