File size: 48,024 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "875b8677",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:57:45.584925Z",
"iopub.status.busy": "2025-03-25T06:57:45.584708Z",
"iopub.status.idle": "2025-03-25T06:57:45.747050Z",
"shell.execute_reply": "2025-03-25T06:57:45.746703Z"
}
},
"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 = \"Bladder_Cancer\"\n",
"cohort = \"GSE244266\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Bladder_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Bladder_Cancer/GSE244266\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Bladder_Cancer/GSE244266.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Bladder_Cancer/gene_data/GSE244266.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Bladder_Cancer/clinical_data/GSE244266.csv\"\n",
"json_path = \"../../output/preprocess/Bladder_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "80497af3",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "82869a0f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:57:45.748343Z",
"iopub.status.busy": "2025-03-25T06:57:45.748213Z",
"iopub.status.idle": "2025-03-25T06:57:46.316659Z",
"shell.execute_reply": "2025-03-25T06:57:46.316298Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Association of Molecular Subtypes with Pathologic Response, PFS and OS in a Phase II Study of Coexpression Extrapolation (COXEN) with Neoadjuvant Chemotherapy (NAC) for Localized, Muscle-Invasive Bladder Cancer (SWOG S1314; NCT02177695)\"\n",
"!Series_summary\t\"Investigation of RNA-based molecular subtypes as additional predictive biomarkers for neoadjuvant chemotherapy response, progression-free survival and survival in patients treated in S1314.\"\n",
"!Series_overall_design\t\"randomized 2-arm phase II trial of neoadjuvant chemotherapy in muscle-invasive bladder cancer, translational analysis of tissue\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['treatment arm: DDMVAC+CYST', 'treatment arm: GC+CYST'], 1: ['disease: muscle-invasive bladder cancer'], 2: ['clinical_stage_strat_factor: Clinical Stage -T2', 'clinical_stage_strat_factor: Clinical Stage -T3 or T4a']}\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": "2140722f",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "87a6b41b",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:57:46.317980Z",
"iopub.status.busy": "2025-03-25T06:57:46.317873Z",
"iopub.status.idle": "2025-03-25T06:57:46.330416Z",
"shell.execute_reply": "2025-03-25T06:57:46.330136Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{'GSM7810144': [1.0], 'GSM7810145': [1.0], 'GSM7810146': [1.0], 'GSM7810147': [1.0], 'GSM7810148': [1.0], 'GSM7810149': [1.0], 'GSM7810150': [1.0], 'GSM7810151': [1.0], 'GSM7810152': [1.0], 'GSM7810153': [1.0], 'GSM7810154': [1.0], 'GSM7810155': [1.0], 'GSM7810156': [1.0], 'GSM7810157': [1.0], 'GSM7810158': [1.0], 'GSM7810159': [1.0], 'GSM7810160': [1.0], 'GSM7810161': [1.0], 'GSM7810162': [1.0], 'GSM7810163': [1.0], 'GSM7810164': [1.0], 'GSM7810165': [1.0], 'GSM7810166': [1.0], 'GSM7810167': [1.0], 'GSM7810168': [1.0], 'GSM7810169': [1.0], 'GSM7810170': [1.0], 'GSM7810171': [1.0], 'GSM7810172': [1.0], 'GSM7810173': [1.0], 'GSM7810174': [1.0], 'GSM7810175': [1.0], 'GSM7810176': [1.0], 'GSM7810177': [1.0], 'GSM7810178': [1.0], 'GSM7810179': [1.0], 'GSM7810180': [1.0], 'GSM7810181': [1.0], 'GSM7810182': [1.0], 'GSM7810183': [1.0], 'GSM7810184': [1.0], 'GSM7810185': [1.0], 'GSM7810186': [1.0], 'GSM7810187': [1.0], 'GSM7810188': [1.0], 'GSM7810189': [1.0], 'GSM7810190': [1.0], 'GSM7810191': [1.0], 'GSM7810192': [1.0], 'GSM7810193': [1.0], 'GSM7810194': [1.0], 'GSM7810195': [1.0], 'GSM7810196': [1.0], 'GSM7810197': [1.0], 'GSM7810198': [1.0], 'GSM7810199': [1.0], 'GSM7810200': [1.0], 'GSM7810201': [1.0], 'GSM7810202': [1.0], 'GSM7810203': [1.0], 'GSM7810204': [1.0], 'GSM7810205': [1.0], 'GSM7810206': [1.0], 'GSM7810207': [1.0], 'GSM7810208': [1.0], 'GSM7810209': [1.0], 'GSM7810210': [1.0], 'GSM7810211': [1.0], 'GSM7810212': [1.0], 'GSM7810213': [1.0], 'GSM7810214': [1.0], 'GSM7810215': [1.0], 'GSM7810216': [1.0], 'GSM7810217': [1.0], 'GSM7810218': [1.0], 'GSM7810219': [1.0], 'GSM7810220': [1.0], 'GSM7810221': [1.0], 'GSM7810222': [1.0], 'GSM7810223': [1.0], 'GSM7810224': [1.0], 'GSM7810225': [1.0], 'GSM7810226': [1.0], 'GSM7810227': [1.0], 'GSM7810228': [1.0], 'GSM7810229': [1.0], 'GSM7810230': [1.0], 'GSM7810231': [1.0], 'GSM7810232': [1.0], 'GSM7810233': [1.0], 'GSM7810234': [1.0], 'GSM7810235': [1.0], 'GSM7810236': [1.0], 'GSM7810237': [1.0], 'GSM7810238': [1.0], 'GSM7810239': [1.0], 'GSM7810240': [1.0], 'GSM7810241': [1.0], 'GSM7810242': [1.0], 'GSM7810243': [1.0], 'GSM7810244': [1.0], 'GSM7810245': [1.0], 'GSM7810246': [1.0], 'GSM7810247': [1.0], 'GSM7810248': [1.0], 'GSM7810249': [1.0], 'GSM7810250': [1.0], 'GSM7810251': [1.0], 'GSM7810252': [1.0], 'GSM7810253': [1.0], 'GSM7810254': [1.0], 'GSM7810255': [1.0], 'GSM7810256': [1.0], 'GSM7810257': [1.0], 'GSM7810258': [1.0], 'GSM7810259': [1.0], 'GSM7810260': [1.0], 'GSM7810261': [1.0], 'GSM7810262': [1.0], 'GSM7810263': [1.0], 'GSM7810264': [1.0], 'GSM7810265': [1.0], 'GSM7810266': [1.0], 'GSM7810267': [1.0], 'GSM7810268': [1.0], 'GSM7810269': [1.0], 'GSM7810270': [1.0], 'GSM7810271': [1.0], 'GSM7810272': [1.0], 'GSM7810273': [1.0], 'GSM7810274': [1.0], 'GSM7810275': [1.0], 'GSM7810276': [1.0], 'GSM7810277': [1.0], 'GSM7810278': [1.0], 'GSM7810279': [1.0], 'GSM7810280': [1.0], 'GSM7810281': [1.0], 'GSM7810282': [1.0], 'GSM7810283': [1.0], 'GSM7810284': [1.0], 'GSM7810285': [1.0], 'GSM7810286': [1.0], 'GSM7810287': [1.0], 'GSM7810288': [1.0], 'GSM7810289': [1.0], 'GSM7810290': [1.0], 'GSM7810291': [1.0], 'GSM7810292': [1.0], 'GSM7810293': [1.0], 'GSM7810294': [1.0], 'GSM7810295': [1.0], 'GSM7810296': [1.0], 'GSM7810297': [1.0], 'GSM7810298': [1.0], 'GSM7810299': [1.0], 'GSM7810300': [1.0], 'GSM7810301': [1.0], 'GSM7810302': [1.0], 'GSM7810303': [1.0], 'GSM7810304': [1.0], 'GSM7810305': [1.0], 'GSM7810306': [1.0], 'GSM7810307': [1.0], 'GSM7810308': [1.0], 'GSM7810309': [1.0], 'GSM7810310': [1.0], 'GSM7810311': [1.0], 'GSM7810312': [1.0], 'GSM7810313': [1.0], 'GSM7810314': [1.0], 'GSM7810315': [1.0], 'GSM7810316': [1.0], 'GSM7810317': [1.0], 'GSM7810318': [1.0], 'GSM7810319': [1.0], 'GSM7810320': [1.0], 'GSM7810321': [1.0], 'GSM7810322': [1.0], 'GSM7810323': [1.0], 'GSM7810324': [1.0], 'GSM7810325': [1.0], 'GSM7810326': [1.0], 'GSM7810327': [1.0], 'GSM7810328': [1.0], 'GSM7810329': [1.0], 'GSM7810330': [1.0], 'GSM7810331': [1.0], 'GSM7810332': [1.0]}\n",
"Clinical data saved to: ../../output/preprocess/Bladder_Cancer/clinical_data/GSE244266.csv\n"
]
}
],
"source": [
"# Step 1: Determine gene expression data availability\n",
"# Based on the information provided, this dataset appears to be a clinical trial (S1314) for bladder cancer\n",
"# with molecular subtypes (RNA-based) analysis, which suggests gene expression data.\n",
"is_gene_available = True\n",
"\n",
"# Step 2: Identify keys and create conversion functions\n",
"\n",
"# 2.1: Trait (Bladder Cancer)\n",
"# From sample characteristics, index 1 shows \"disease: muscle-invasive bladder cancer\"\n",
"trait_row = 1\n",
"\n",
"def convert_trait(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Since this is a bladder cancer study, convert to binary\n",
" if 'bladder cancer' in value.lower() or 'muscle-invasive bladder cancer' in value.lower():\n",
" return 1\n",
" return 0\n",
"\n",
"# 2.2: Age\n",
"# There's no age information in the sample characteristics\n",
"age_row = None\n",
"\n",
"def convert_age(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"# 2.3: Gender\n",
"# There's no gender information in the sample characteristics\n",
"gender_row = None\n",
"\n",
"def convert_gender(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" value = value.lower()\n",
" if 'female' in value or 'f' == value:\n",
" return 0\n",
" elif 'male' in value or 'm' == value:\n",
" return 1\n",
" return None\n",
"\n",
"# Step 3: Save metadata - perform initial filtering\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",
"# Step 4: Extract clinical features if available\n",
"if trait_row is not None:\n",
" # Create clinical dataframe using the geo_select_clinical_features function\n",
" clinical_selected = 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 dataframe\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview_df(clinical_selected))\n",
" \n",
" # Create the 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_selected.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to: {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "ecc11378",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bf553e6e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:57:46.331540Z",
"iopub.status.busy": "2025-03-25T06:57:46.331442Z",
"iopub.status.idle": "2025-03-25T06:57:47.334789Z",
"shell.execute_reply": "2025-03-25T06:57:47.334430Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n",
" '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n",
" '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n",
" '1552263_at', '1552264_a_at', '1552266_at'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "02c47cbb",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9b74ebb9",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:57:47.336354Z",
"iopub.status.busy": "2025-03-25T06:57:47.336237Z",
"iopub.status.idle": "2025-03-25T06:57:47.338087Z",
"shell.execute_reply": "2025-03-25T06:57:47.337809Z"
}
},
"outputs": [],
"source": [
"# These gene identifiers appear to be Affymetrix probe IDs from a microarray platform,\n",
"# not standard human gene symbols. For example, \"1007_s_at\" follows the Affymetrix naming pattern.\n",
"# These will need to be mapped to official gene symbols for standardized analysis.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "9ae46cda",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9b867a65",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:57:47.339243Z",
"iopub.status.busy": "2025-03-25T06:57:47.339144Z",
"iopub.status.idle": "2025-03-25T06:58:03.247185Z",
"shell.execute_reply": "2025-03-25T06:58:03.246757Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\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. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "e29745f5",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c78e3d92",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:58:03.248534Z",
"iopub.status.busy": "2025-03-25T06:58:03.248405Z",
"iopub.status.idle": "2025-03-25T06:58:03.985912Z",
"shell.execute_reply": "2025-03-25T06:58:03.985255Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Columns available in gene_annotation:\n",
"Index(['ID', 'GB_ACC', 'SPOT_ID', 'Species Scientific Name', 'Annotation Date',\n",
" 'Sequence Type', 'Sequence Source', 'Target Description',\n",
" 'Representative Public ID', 'Gene Title', 'Gene Symbol',\n",
" 'ENTREZ_GENE_ID', 'RefSeq Transcript ID',\n",
" 'Gene Ontology Biological Process', 'Gene Ontology Cellular Component',\n",
" 'Gene Ontology Molecular Function'],\n",
" dtype='object')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping dataframe shape: (45782, 2)\n",
"First few rows of gene mapping:\n",
" ID Gene\n",
"0 1007_s_at DDR1 /// MIR4640\n",
"1 1053_at RFC2\n",
"2 117_at HSPA6\n",
"3 121_at PAX8\n",
"4 1255_g_at GUCA1A\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression dataframe shape after mapping: (21278, 189)\n",
"First few gene symbols after mapping:\n",
"Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n",
" 'A4GALT', 'A4GNT', 'AA06'],\n",
" dtype='object', name='Gene')\n"
]
}
],
"source": [
"# 1. Identifying columns for gene mapping\n",
"# Looking at gene annotation, we need to map from the 'ID' column (Affymetrix probe IDs) to 'Gene Symbol' column\n",
"print(\"Columns available in gene_annotation:\")\n",
"print(gene_annotation.columns)\n",
"\n",
"# 2. Get a gene mapping dataframe\n",
"# Using get_gene_mapping function from the library to extract these columns\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
"print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n",
"print(\"First few rows of gene mapping:\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression\n",
"# Using apply_gene_mapping function from the library\n",
"gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)\n",
"print(f\"Gene expression dataframe shape after mapping: {gene_data.shape}\")\n",
"print(\"First few gene symbols after mapping:\")\n",
"print(gene_data.index[:10])\n"
]
},
{
"cell_type": "markdown",
"id": "975b32c2",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7ba5aca4",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:58:03.987816Z",
"iopub.status.busy": "2025-03-25T06:58:03.987685Z",
"iopub.status.idle": "2025-03-25T06:58:13.253785Z",
"shell.execute_reply": "2025-03-25T06:58:13.253245Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original gene count: 21278\n",
"Normalized gene count: 19845\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Bladder_Cancer/gene_data/GSE244266.csv\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data structure:\n",
"(3, 190)\n",
"First few rows of clinical data:\n",
" !Sample_geo_accession \\\n",
"0 !Sample_characteristics_ch1 \n",
"1 !Sample_characteristics_ch1 \n",
"2 !Sample_characteristics_ch1 \n",
"\n",
" GSM7810144 \\\n",
"0 treatment arm: DDMVAC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810145 \\\n",
"0 treatment arm: DDMVAC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T... \n",
"\n",
" GSM7810146 \\\n",
"0 treatment arm: DDMVAC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810147 \\\n",
"0 treatment arm: GC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810148 \\\n",
"0 treatment arm: GC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810149 \\\n",
"0 treatment arm: DDMVAC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810150 \\\n",
"0 treatment arm: DDMVAC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810151 \\\n",
"0 treatment arm: GC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810152 ... \\\n",
"0 treatment arm: DDMVAC+CYST ... \n",
"1 disease: muscle-invasive bladder cancer ... \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 ... \n",
"\n",
" GSM7810323 \\\n",
"0 treatment arm: DDMVAC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810324 \\\n",
"0 treatment arm: GC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810325 \\\n",
"0 treatment arm: DDMVAC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810326 \\\n",
"0 treatment arm: DDMVAC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810327 \\\n",
"0 treatment arm: GC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810328 \\\n",
"0 treatment arm: DDMVAC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810329 \\\n",
"0 treatment arm: DDMVAC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T... \n",
"\n",
" GSM7810330 \\\n",
"0 treatment arm: GC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810331 \\\n",
"0 treatment arm: GC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
" GSM7810332 \n",
"0 treatment arm: DDMVAC+CYST \n",
"1 disease: muscle-invasive bladder cancer \n",
"2 clinical_stage_strat_factor: Clinical Stage -T2 \n",
"\n",
"[3 rows x 190 columns]\n",
"Clinical data shape after extraction: (1, 189)\n",
"First few sample IDs in clinical data:\n",
"['GSM7810144', 'GSM7810145', 'GSM7810146', 'GSM7810147', 'GSM7810148']\n",
"First few sample IDs in gene data:\n",
"['GSM7810144', 'GSM7810145', 'GSM7810146', 'GSM7810147', 'GSM7810148']\n",
"Number of common samples between clinical and gene data: 189\n",
"Clinical data saved to ../../output/preprocess/Bladder_Cancer/clinical_data/GSE244266.csv\n",
"Linked data shape: (189, 19846)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (189, 19846)\n",
"Quartiles for 'Bladder_Cancer':\n",
" 25%: 1.0\n",
" 50% (Median): 1.0\n",
" 75%: 1.0\n",
"Min: 1.0\n",
"Max: 1.0\n",
"The distribution of the feature 'Bladder_Cancer' in this dataset is severely biased.\n",
"\n",
"The dataset was determined to be not usable for analysis due to bias in the trait distribution.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"# First, normalize gene symbols using the function from the library\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Original gene count: {len(gene_data)}\")\n",
"print(f\"Normalized gene count: {len(normalized_gene_data)}\")\n",
"\n",
"# Create directory for the gene data file if it doesn't exist\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"\n",
"# Save the normalized gene data to a CSV file\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Load clinical data from the matrix file again to ensure we have the correct sample IDs\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",
"print(\"Clinical data structure:\")\n",
"print(clinical_data.shape)\n",
"print(\"First few rows of clinical data:\")\n",
"print(clinical_data.head())\n",
"\n",
"# Extract clinical features with the correct sample IDs\n",
"selected_clinical_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",
"print(f\"Clinical data shape after extraction: {selected_clinical_df.shape}\")\n",
"print(\"First few sample IDs in clinical data:\")\n",
"print(list(selected_clinical_df.columns)[:5])\n",
"print(\"First few sample IDs in gene data:\")\n",
"print(list(normalized_gene_data.columns)[:5])\n",
"\n",
"# Check for column overlap\n",
"common_samples = set(selected_clinical_df.columns).intersection(set(normalized_gene_data.columns))\n",
"print(f\"Number of common samples between clinical and gene data: {len(common_samples)}\")\n",
"\n",
"# Save the clinical data for inspection\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"selected_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# Check if linking was successful\n",
"if len(linked_data) == 0 or trait not in linked_data.columns:\n",
" print(\"Linking clinical and genetic data failed - no valid rows or trait column missing\")\n",
" \n",
" # Check what columns are in the linked data\n",
" if len(linked_data.columns) > 0:\n",
" print(\"Columns in linked data:\")\n",
" print(list(linked_data.columns)[:10]) # Print first 10 columns\n",
" \n",
" # Set is_usable to False and save cohort info\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=True, # Consider it biased if linking fails\n",
" df=pd.DataFrame({trait: [], 'Gender': []}), \n",
" note=\"Data linking failed - unable to match sample IDs between clinical and gene expression data.\"\n",
" )\n",
" print(\"The dataset was determined to be not usable for analysis.\")\n",
"else:\n",
" # 3. Handle missing values in the linked data\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" \n",
" print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" # 4. Determine whether the trait and demographic features are severely biased\n",
" is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" \n",
" # 5. Conduct quality check and save the cohort information.\n",
" note = \"Dataset contains gene expression data from bladder cancer samples with molecular subtyping information.\"\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_trait_biased, \n",
" df=linked_data, \n",
" note=note\n",
" )\n",
" \n",
" # 6. If the linked data is usable, save it as a CSV file.\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(\"The dataset was determined to be not usable for analysis due to bias in the trait distribution.\")"
]
}
],
"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
}
|