File size: 39,588 Bytes
32677ff |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d66a6f7d",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:32:21.930922Z",
"iopub.status.busy": "2025-03-25T08:32:21.930437Z",
"iopub.status.idle": "2025-03-25T08:32:22.100818Z",
"shell.execute_reply": "2025-03-25T08:32:22.100463Z"
}
},
"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 = \"Crohns_Disease\"\n",
"cohort = \"GSE169568\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Crohns_Disease\"\n",
"in_cohort_dir = \"../../input/GEO/Crohns_Disease/GSE169568\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Crohns_Disease/GSE169568.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Crohns_Disease/gene_data/GSE169568.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Crohns_Disease/clinical_data/GSE169568.csv\"\n",
"json_path = \"../../output/preprocess/Crohns_Disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "80cee7d9",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bd213c76",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:32:22.102315Z",
"iopub.status.busy": "2025-03-25T08:32:22.102163Z",
"iopub.status.idle": "2025-03-25T08:32:22.313769Z",
"shell.execute_reply": "2025-03-25T08:32:22.313438Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"BeadChip microarray data of peripheral blood obtained from treatment-näive IBD patients and control individuals\"\n",
"!Series_summary\t\"Comperhensive analysis of blood transcriptomes obtained from treatment-näive IBD patients.\"\n",
"!Series_overall_design\t\"Total RNA extracted from peripheral blood samples (n = 205) was reverse transcribed and biotin-labeled using the TargetAmp-Nano Labeling Kit for Illumina Expression BeadChip (Epicentre) according to the manufacturer’s protocol. The labeled antisense RNA was hybridized to Human HT-12 v4 BeadChip array (Illumina) following the standard producer’s hybridization protocol. The array imaging was performed on an iScan system (Illumina) according to the standard manufacturer’s protocol.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['Sex: female', 'Sex: male'], 1: ['age: 20', 'age: 39', 'age: 56', 'age: 31', 'age: 22', 'age: 32', 'age: 30', 'age: 18', 'age: 60', 'age: 33', 'age: 27', 'age: 34', 'age: 57', 'age: 72', 'age: 35', 'age: 24', 'age: 21', 'age: 62', 'age: 41', 'age: 29', 'age: 46', 'age: 49', 'age: 76', 'age: 23', 'age: 37', 'age: 64', 'age: 26', 'age: 19', 'age: 17', 'age: 48'], 2: ['diagnosis: Symptomatic control', 'diagnosis: Ulcerative colitis', \"diagnosis: Crohn's disease\", 'diagnosis: Healthy control'], 3: ['annotation file: HumanHT-12_V4_0_R2_15002873_B.bgx']}\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": "213435cf",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "75f34f93",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:32:22.315062Z",
"iopub.status.busy": "2025-03-25T08:32:22.314936Z",
"iopub.status.idle": "2025-03-25T08:32:22.338382Z",
"shell.execute_reply": "2025-03-25T08:32:22.338069Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical Data Preview:\n",
"{'GSM5209429': [0.0, 20.0, 0.0], 'GSM5209430': [0.0, 39.0, 1.0], 'GSM5209431': [0.0, 56.0, 0.0], 'GSM5209432': [0.0, 31.0, 0.0], 'GSM5209433': [1.0, 22.0, 1.0], 'GSM5209434': [0.0, 32.0, 1.0], 'GSM5209435': [0.0, 32.0, 0.0], 'GSM5209436': [0.0, 30.0, 0.0], 'GSM5209437': [0.0, 30.0, 1.0], 'GSM5209438': [0.0, 18.0, 0.0], 'GSM5209439': [0.0, 60.0, 0.0], 'GSM5209440': [0.0, 33.0, 1.0], 'GSM5209441': [0.0, 27.0, 0.0], 'GSM5209442': [0.0, 30.0, 1.0], 'GSM5209443': [0.0, 34.0, 0.0], 'GSM5209444': [0.0, 57.0, 1.0], 'GSM5209445': [0.0, 27.0, 1.0], 'GSM5209446': [0.0, 20.0, 0.0], 'GSM5209447': [0.0, 30.0, 0.0], 'GSM5209448': [1.0, 27.0, 1.0], 'GSM5209449': [0.0, 32.0, 1.0], 'GSM5209450': [0.0, 72.0, 0.0], 'GSM5209451': [1.0, 35.0, 0.0], 'GSM5209452': [0.0, 24.0, 0.0], 'GSM5209453': [1.0, 21.0, 1.0], 'GSM5209454': [0.0, 62.0, 1.0], 'GSM5209455': [1.0, 41.0, 0.0], 'GSM5209456': [0.0, 22.0, 0.0], 'GSM5209457': [0.0, 18.0, 0.0], 'GSM5209458': [0.0, 20.0, 1.0], 'GSM5209459': [1.0, 29.0, 0.0], 'GSM5209460': [0.0, 46.0, 1.0], 'GSM5209461': [0.0, 31.0, 1.0], 'GSM5209462': [0.0, 34.0, 0.0], 'GSM5209463': [0.0, 32.0, 1.0], 'GSM5209464': [0.0, 49.0, 0.0], 'GSM5209465': [1.0, 76.0, 1.0], 'GSM5209466': [1.0, 23.0, 0.0], 'GSM5209467': [0.0, 37.0, 1.0], 'GSM5209468': [0.0, 30.0, 1.0], 'GSM5209469': [0.0, 64.0, 1.0], 'GSM5209470': [0.0, 23.0, 1.0], 'GSM5209471': [0.0, 24.0, 0.0], 'GSM5209472': [0.0, 26.0, 1.0], 'GSM5209473': [1.0, 19.0, 1.0], 'GSM5209474': [0.0, 60.0, 0.0], 'GSM5209475': [1.0, 17.0, 0.0], 'GSM5209476': [1.0, 41.0, 0.0], 'GSM5209477': [1.0, 48.0, 0.0], 'GSM5209478': [0.0, 26.0, 0.0], 'GSM5209479': [0.0, 35.0, 1.0], 'GSM5209480': [0.0, 22.0, 0.0], 'GSM5209481': [0.0, 73.0, 0.0], 'GSM5209482': [1.0, 69.0, 1.0], 'GSM5209483': [0.0, 57.0, 1.0], 'GSM5209484': [0.0, 50.0, 0.0], 'GSM5209485': [0.0, 27.0, 1.0], 'GSM5209486': [0.0, 69.0, 1.0], 'GSM5209487': [0.0, 28.0, 1.0], 'GSM5209488': [0.0, 51.0, 0.0], 'GSM5209489': [0.0, 64.0, 1.0], 'GSM5209490': [0.0, 52.0, 1.0], 'GSM5209491': [0.0, 55.0, 1.0], 'GSM5209492': [0.0, 47.0, 1.0], 'GSM5209493': [0.0, 61.0, 0.0], 'GSM5209494': [0.0, 29.0, 0.0], 'GSM5209495': [0.0, 36.0, 0.0], 'GSM5209496': [0.0, 24.0, 0.0], 'GSM5209497': [0.0, 24.0, 0.0], 'GSM5209498': [0.0, 21.0, 0.0], 'GSM5209499': [0.0, 54.0, 0.0], 'GSM5209500': [0.0, 24.0, 0.0], 'GSM5209501': [0.0, 78.0, 0.0], 'GSM5209502': [0.0, 23.0, 1.0], 'GSM5209503': [0.0, 27.0, 0.0], 'GSM5209504': [0.0, 21.0, 1.0], 'GSM5209505': [0.0, 34.0, 1.0], 'GSM5209506': [0.0, 51.0, 1.0], 'GSM5209507': [1.0, 31.0, 0.0], 'GSM5209508': [1.0, 40.0, 0.0], 'GSM5209509': [1.0, 24.0, 0.0], 'GSM5209510': [1.0, 24.0, 1.0], 'GSM5209511': [0.0, 23.0, 0.0], 'GSM5209512': [0.0, 33.0, 1.0], 'GSM5209513': [0.0, 25.0, 0.0], 'GSM5209514': [0.0, 23.0, 0.0], 'GSM5209515': [0.0, 41.0, 1.0], 'GSM5209516': [0.0, 32.0, 1.0], 'GSM5209517': [1.0, 23.0, 0.0], 'GSM5209518': [0.0, 36.0, 1.0], 'GSM5209519': [1.0, 26.0, 1.0], 'GSM5209520': [1.0, 23.0, 0.0], 'GSM5209521': [1.0, 36.0, 1.0], 'GSM5209522': [1.0, 40.0, 0.0], 'GSM5209523': [1.0, 26.0, 0.0], 'GSM5209524': [1.0, 18.0, 0.0], 'GSM5209525': [0.0, 35.0, 0.0], 'GSM5209526': [0.0, 24.0, 0.0], 'GSM5209527': [0.0, 32.0, 1.0], 'GSM5209528': [0.0, 61.0, 0.0], 'GSM5209529': [0.0, 34.0, 0.0], 'GSM5209530': [0.0, 54.0, 0.0], 'GSM5209531': [1.0, 21.0, 0.0], 'GSM5209532': [0.0, 28.0, 1.0], 'GSM5209533': [1.0, 38.0, 0.0], 'GSM5209534': [1.0, 69.0, 1.0], 'GSM5209535': [0.0, 28.0, 0.0], 'GSM5209536': [0.0, 27.0, 1.0], 'GSM5209537': [0.0, 33.0, 1.0], 'GSM5209538': [0.0, 24.0, 1.0], 'GSM5209539': [0.0, 19.0, 1.0], 'GSM5209540': [1.0, 32.0, 1.0], 'GSM5209541': [0.0, 40.0, 1.0], 'GSM5209542': [0.0, 39.0, 0.0], 'GSM5209543': [1.0, 29.0, 0.0], 'GSM5209544': [1.0, 26.0, 1.0], 'GSM5209545': [1.0, 26.0, 1.0], 'GSM5209546': [0.0, 18.0, 0.0], 'GSM5209547': [0.0, 38.0, 1.0], 'GSM5209548': [0.0, 59.0, 1.0], 'GSM5209549': [1.0, 53.0, 1.0], 'GSM5209550': [0.0, 41.0, 1.0], 'GSM5209551': [1.0, 24.0, 0.0], 'GSM5209552': [1.0, 28.0, 0.0], 'GSM5209553': [1.0, 30.0, 1.0], 'GSM5209554': [0.0, 31.0, 1.0], 'GSM5209555': [0.0, 47.0, 0.0], 'GSM5209556': [0.0, 76.0, 0.0], 'GSM5209557': [0.0, 27.0, 1.0], 'GSM5209558': [0.0, 36.0, 1.0], 'GSM5209559': [0.0, 19.0, 0.0], 'GSM5209560': [0.0, 38.0, 1.0], 'GSM5209561': [1.0, 24.0, 1.0], 'GSM5209562': [0.0, 33.0, 1.0], 'GSM5209563': [0.0, 23.0, 0.0], 'GSM5209564': [0.0, 20.0, 0.0], 'GSM5209565': [1.0, 38.0, 1.0], 'GSM5209566': [0.0, 68.0, 0.0], 'GSM5209567': [0.0, 23.0, 1.0], 'GSM5209568': [1.0, 39.0, 1.0], 'GSM5209569': [1.0, 23.0, 1.0], 'GSM5209570': [1.0, 23.0, 0.0], 'GSM5209571': [0.0, 39.0, 1.0], 'GSM5209572': [0.0, 38.0, 0.0], 'GSM5209573': [0.0, 20.0, 0.0], 'GSM5209574': [1.0, 54.0, 1.0], 'GSM5209575': [0.0, 41.0, 1.0], 'GSM5209576': [0.0, 48.0, 0.0], 'GSM5209577': [0.0, 74.0, 1.0], 'GSM5209578': [0.0, 69.0, 0.0], 'GSM5209579': [0.0, 42.0, 0.0], 'GSM5209580': [1.0, 25.0, 1.0], 'GSM5209581': [0.0, 35.0, 1.0], 'GSM5209582': [1.0, 30.0, 1.0], 'GSM5209583': [1.0, 23.0, 0.0], 'GSM5209584': [0.0, 36.0, 0.0], 'GSM5209585': [0.0, 61.0, 1.0], 'GSM5209586': [0.0, 37.0, 1.0], 'GSM5209587': [0.0, 50.0, 1.0], 'GSM5209588': [0.0, 46.0, 0.0], 'GSM5209589': [0.0, 22.0, 1.0], 'GSM5209590': [0.0, 21.0, 0.0], 'GSM5209591': [0.0, 44.0, 0.0], 'GSM5209592': [0.0, 24.0, 0.0], 'GSM5209593': [0.0, 24.0, 1.0], 'GSM5209594': [0.0, 23.0, 0.0], 'GSM5209595': [0.0, 47.0, 0.0], 'GSM5209596': [0.0, 21.0, 0.0], 'GSM5209597': [0.0, 19.0, 0.0], 'GSM5209598': [0.0, 56.0, 0.0], 'GSM5209599': [0.0, 25.0, 1.0], 'GSM5209600': [0.0, 54.0, 1.0], 'GSM5209601': [0.0, 51.0, 1.0], 'GSM5209602': [0.0, 43.0, 0.0], 'GSM5209603': [0.0, 53.0, 0.0], 'GSM5209604': [0.0, 66.0, 1.0], 'GSM5209605': [0.0, 69.0, 1.0], 'GSM5209606': [0.0, 22.0, 0.0], 'GSM5209607': [0.0, 56.0, 0.0], 'GSM5209608': [0.0, 51.0, 1.0], 'GSM5209609': [0.0, 69.0, 1.0], 'GSM5209610': [0.0, 53.0, 0.0], 'GSM5209611': [0.0, 61.0, 1.0], 'GSM5209612': [0.0, 52.0, 1.0], 'GSM5209613': [0.0, 42.0, 0.0], 'GSM5209614': [0.0, 56.0, 1.0], 'GSM5209615': [1.0, 58.0, 0.0], 'GSM5209616': [1.0, 20.0, 0.0], 'GSM5209617': [1.0, 17.0, 1.0], 'GSM5209618': [0.0, 40.0, 0.0], 'GSM5209619': [1.0, 44.0, 1.0], 'GSM5209620': [0.0, 45.0, 0.0], 'GSM5209621': [1.0, 19.0, 1.0], 'GSM5209622': [0.0, 28.0, 0.0], 'GSM5209623': [0.0, 57.0, 0.0], 'GSM5209624': [1.0, 41.0, 0.0], 'GSM5209625': [0.0, 34.0, 0.0], 'GSM5209626': [0.0, 54.0, 0.0], 'GSM5209627': [1.0, 59.0, 1.0], 'GSM5209628': [0.0, 20.0, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Crohns_Disease/clinical_data/GSE169568.csv\n"
]
}
],
"source": [
"# 1. Check if this dataset likely contains gene expression data\n",
"# Based on the background information, this dataset contains BeadChip microarray data (Illumina Human HT-12 v4), \n",
"# which is indeed gene expression data. So we set is_gene_available to True.\n",
"is_gene_available = True\n",
"\n",
"# 2. Identify keys and conversion functions for trait, age, and gender data\n",
"# 2.1 Data Availability\n",
"\n",
"# Trait - Crohn's Disease (key 2 contains diagnostic information)\n",
"trait_row = 2\n",
"\n",
"# Age data (key 1 contains age information)\n",
"age_row = 1\n",
"\n",
"# Gender data (key 0 contains sex information)\n",
"gender_row = 0\n",
"\n",
"# 2.2 Data Type Conversion\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert trait values to binary format:\n",
" 1 for Crohn's disease, 0 for controls (healthy or symptomatic)\n",
" \"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Convert to binary based on diagnosis\n",
" if \"Crohn's disease\" in value:\n",
" return 1\n",
" elif \"Healthy control\" in value or \"Symptomatic control\" in value or \"Ulcerative colitis\" in value:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"\n",
" Convert age values to continuous format\n",
" \"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after colon\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",
"def convert_gender(value):\n",
" \"\"\"\n",
" Convert gender values to binary format:\n",
" 0 for female, 1 for male\n",
" \"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" \n",
" if \"female\" in value:\n",
" return 0\n",
" elif \"male\" in value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Determine trait data availability and save metadata\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",
" # Extract clinical features using the provided library function\n",
" 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",
" # Preview the extracted clinical data\n",
" preview = preview_df(clinical_df)\n",
" print(\"Clinical Data Preview:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical data to the specified path\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "1db3cd6c",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "6e43c924",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:32:22.339554Z",
"iopub.status.busy": "2025-03-25T08:32:22.339439Z",
"iopub.status.idle": "2025-03-25T08:32:22.699990Z",
"shell.execute_reply": "2025-03-25T08:32:22.699537Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"First 20 gene/probe identifiers:\n",
"Index(['ILMN_1651209', 'ILMN_1651229', 'ILMN_1651254', 'ILMN_1651262',\n",
" 'ILMN_1651278', 'ILMN_1651282', 'ILMN_1651285', 'ILMN_1651315',\n",
" 'ILMN_1651336', 'ILMN_1651341', 'ILMN_1651343', 'ILMN_1651347',\n",
" 'ILMN_1651354', 'ILMN_1651358', 'ILMN_1651373', 'ILMN_1651378',\n",
" 'ILMN_1651385', 'ILMN_1651405', 'ILMN_1651415', 'ILMN_1651429'],\n",
" dtype='object', name='ID')\n",
"\n",
"Gene data dimensions: 11727 genes × 205 samples\n"
]
}
],
"source": [
"# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Extract the gene expression data from the matrix file\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers)\n",
"print(\"\\nFirst 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n",
"\n",
"# 4. Print the dimensions of the gene expression data\n",
"print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
"is_gene_available = True\n"
]
},
{
"cell_type": "markdown",
"id": "9bfd468e",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0c93598b",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:32:22.701393Z",
"iopub.status.busy": "2025-03-25T08:32:22.701261Z",
"iopub.status.idle": "2025-03-25T08:32:22.703303Z",
"shell.execute_reply": "2025-03-25T08:32:22.702987Z"
}
},
"outputs": [],
"source": [
"# Looking at the gene identifiers, I can see they use the format ILMN_XXXXXXX\n",
"# These are Illumina BeadArray probe IDs, not human gene symbols\n",
"# Illumina probe IDs need to be mapped to human gene symbols for biological interpretation\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "757f43ca",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0cd84a88",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:32:22.704495Z",
"iopub.status.busy": "2025-03-25T08:32:22.704380Z",
"iopub.status.idle": "2025-03-25T08:32:28.328415Z",
"shell.execute_reply": "2025-03-25T08:32:28.328019Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation dataframe column names:\n",
"Index(['ID', 'Species', 'Source', 'Search_Key', 'Transcript', 'ILMN_Gene',\n",
" 'Source_Reference_ID', 'RefSeq_ID', 'Unigene_ID', 'Entrez_Gene_ID',\n",
" 'GI', 'Accession', 'Symbol', 'Protein_Product', 'Probe_Id',\n",
" 'Array_Address_Id', 'Probe_Type', 'Probe_Start', 'SEQUENCE',\n",
" 'Chromosome', 'Probe_Chr_Orientation', 'Probe_Coordinates', 'Cytoband',\n",
" 'Definition', 'Ontology_Component', 'Ontology_Process',\n",
" 'Ontology_Function', 'Synonyms', 'Obsolete_Probe_Id', 'GB_ACC'],\n",
" dtype='object')\n",
"\n",
"Preview of gene annotation data:\n",
"{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050'], 'Species': [nan, nan, nan], 'Source': [nan, nan, nan], 'Search_Key': [nan, nan, nan], 'Transcript': [nan, nan, nan], 'ILMN_Gene': [nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan], 'RefSeq_ID': [nan, nan, nan], 'Unigene_ID': [nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan], 'GI': [nan, nan, nan], 'Accession': [nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low'], 'Protein_Product': [nan, nan, nan], 'Probe_Id': [nan, nan, nan], 'Array_Address_Id': [5090180.0, 6510136.0, 7560739.0], 'Probe_Type': [nan, nan, nan], 'Probe_Start': [nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT'], 'Chromosome': [nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan], 'Cytoband': [nan, nan, nan], 'Definition': [nan, nan, nan], 'Ontology_Component': [nan, nan, nan], 'Ontology_Process': [nan, nan, nan], 'Ontology_Function': [nan, nan, nan], 'Synonyms': [nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan], 'GB_ACC': [nan, nan, nan]}\n"
]
}
],
"source": [
"# 1. First get the file paths using geo_get_relevant_filepaths function\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Extract gene annotation data from the SOFT file\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 3. Preview the gene annotation dataframe\n",
"print(\"Gene annotation dataframe column names:\")\n",
"print(gene_annotation.columns)\n",
"\n",
"# Preview the first few rows to understand the data structure\n",
"print(\"\\nPreview of gene annotation data:\")\n",
"annotation_preview = preview_df(gene_annotation, n=3)\n",
"print(annotation_preview)\n",
"\n",
"# Maintain gene availability status as True based on previous steps\n",
"is_gene_available = True\n"
]
},
{
"cell_type": "markdown",
"id": "30f653d6",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f187ccba",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:32:28.329823Z",
"iopub.status.busy": "2025-03-25T08:32:28.329690Z",
"iopub.status.idle": "2025-03-25T08:32:28.529372Z",
"shell.execute_reply": "2025-03-25T08:32:28.529019Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Mapped gene data dimensions: 11148 genes × 205 samples\n",
"\n",
"First 10 gene symbols after mapping:\n",
"Index(['A2BP1', 'A2LD1', 'A4GNT', 'AAAS', 'AACS', 'AADACL1', 'AADACL4',\n",
" 'AAGAB', 'AAK1', 'AAMP'],\n",
" dtype='object', name='Gene')\n"
]
}
],
"source": [
"# 1. Identify the appropriate columns in the gene annotation dataframe\n",
"# The 'ID' column in gene_annotation contains probe IDs that match gene_data.index\n",
"# The 'Symbol' column contains gene symbols that we want to map to\n",
"\n",
"# 2. Get gene mapping dataframe\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
"\n",
"# Ensure no empty gene symbols\n",
"gene_mapping = gene_mapping.dropna(subset=['Gene'])\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Print dimensions of the processed gene expression data\n",
"print(f\"\\nMapped gene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
"\n",
"# Preview the first few gene symbols after mapping\n",
"print(\"\\nFirst 10 gene symbols after mapping:\")\n",
"print(gene_data.index[:10])\n"
]
},
{
"cell_type": "markdown",
"id": "767e30fc",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5a08b637",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:32:28.530833Z",
"iopub.status.busy": "2025-03-25T08:32:28.530713Z",
"iopub.status.idle": "2025-03-25T08:32:38.607836Z",
"shell.execute_reply": "2025-03-25T08:32:38.607384Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalizing gene symbols...\n",
"Gene data shape after normalization: 11039 genes × 205 samples\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data saved to ../../output/preprocess/Crohns_Disease/gene_data/GSE169568.csv\n",
"Extracting clinical features from the original source...\n",
"Extracted clinical features preview:\n",
"{'GSM5209429': [0.0, 20.0, 0.0], 'GSM5209430': [0.0, 39.0, 1.0], 'GSM5209431': [0.0, 56.0, 0.0], 'GSM5209432': [0.0, 31.0, 0.0], 'GSM5209433': [1.0, 22.0, 1.0], 'GSM5209434': [0.0, 32.0, 1.0], 'GSM5209435': [0.0, 32.0, 0.0], 'GSM5209436': [0.0, 30.0, 0.0], 'GSM5209437': [0.0, 30.0, 1.0], 'GSM5209438': [0.0, 18.0, 0.0], 'GSM5209439': [0.0, 60.0, 0.0], 'GSM5209440': [0.0, 33.0, 1.0], 'GSM5209441': [0.0, 27.0, 0.0], 'GSM5209442': [0.0, 30.0, 1.0], 'GSM5209443': [0.0, 34.0, 0.0], 'GSM5209444': [0.0, 57.0, 1.0], 'GSM5209445': [0.0, 27.0, 1.0], 'GSM5209446': [0.0, 20.0, 0.0], 'GSM5209447': [0.0, 30.0, 0.0], 'GSM5209448': [1.0, 27.0, 1.0], 'GSM5209449': [0.0, 32.0, 1.0], 'GSM5209450': [0.0, 72.0, 0.0], 'GSM5209451': [1.0, 35.0, 0.0], 'GSM5209452': [0.0, 24.0, 0.0], 'GSM5209453': [1.0, 21.0, 1.0], 'GSM5209454': [0.0, 62.0, 1.0], 'GSM5209455': [1.0, 41.0, 0.0], 'GSM5209456': [0.0, 22.0, 0.0], 'GSM5209457': [0.0, 18.0, 0.0], 'GSM5209458': [0.0, 20.0, 1.0], 'GSM5209459': [1.0, 29.0, 0.0], 'GSM5209460': [0.0, 46.0, 1.0], 'GSM5209461': [0.0, 31.0, 1.0], 'GSM5209462': [0.0, 34.0, 0.0], 'GSM5209463': [0.0, 32.0, 1.0], 'GSM5209464': [0.0, 49.0, 0.0], 'GSM5209465': [1.0, 76.0, 1.0], 'GSM5209466': [1.0, 23.0, 0.0], 'GSM5209467': [0.0, 37.0, 1.0], 'GSM5209468': [0.0, 30.0, 1.0], 'GSM5209469': [0.0, 64.0, 1.0], 'GSM5209470': [0.0, 23.0, 1.0], 'GSM5209471': [0.0, 24.0, 0.0], 'GSM5209472': [0.0, 26.0, 1.0], 'GSM5209473': [1.0, 19.0, 1.0], 'GSM5209474': [0.0, 60.0, 0.0], 'GSM5209475': [1.0, 17.0, 0.0], 'GSM5209476': [1.0, 41.0, 0.0], 'GSM5209477': [1.0, 48.0, 0.0], 'GSM5209478': [0.0, 26.0, 0.0], 'GSM5209479': [0.0, 35.0, 1.0], 'GSM5209480': [0.0, 22.0, 0.0], 'GSM5209481': [0.0, 73.0, 0.0], 'GSM5209482': [1.0, 69.0, 1.0], 'GSM5209483': [0.0, 57.0, 1.0], 'GSM5209484': [0.0, 50.0, 0.0], 'GSM5209485': [0.0, 27.0, 1.0], 'GSM5209486': [0.0, 69.0, 1.0], 'GSM5209487': [0.0, 28.0, 1.0], 'GSM5209488': [0.0, 51.0, 0.0], 'GSM5209489': [0.0, 64.0, 1.0], 'GSM5209490': [0.0, 52.0, 1.0], 'GSM5209491': [0.0, 55.0, 1.0], 'GSM5209492': [0.0, 47.0, 1.0], 'GSM5209493': [0.0, 61.0, 0.0], 'GSM5209494': [0.0, 29.0, 0.0], 'GSM5209495': [0.0, 36.0, 0.0], 'GSM5209496': [0.0, 24.0, 0.0], 'GSM5209497': [0.0, 24.0, 0.0], 'GSM5209498': [0.0, 21.0, 0.0], 'GSM5209499': [0.0, 54.0, 0.0], 'GSM5209500': [0.0, 24.0, 0.0], 'GSM5209501': [0.0, 78.0, 0.0], 'GSM5209502': [0.0, 23.0, 1.0], 'GSM5209503': [0.0, 27.0, 0.0], 'GSM5209504': [0.0, 21.0, 1.0], 'GSM5209505': [0.0, 34.0, 1.0], 'GSM5209506': [0.0, 51.0, 1.0], 'GSM5209507': [1.0, 31.0, 0.0], 'GSM5209508': [1.0, 40.0, 0.0], 'GSM5209509': [1.0, 24.0, 0.0], 'GSM5209510': [1.0, 24.0, 1.0], 'GSM5209511': [0.0, 23.0, 0.0], 'GSM5209512': [0.0, 33.0, 1.0], 'GSM5209513': [0.0, 25.0, 0.0], 'GSM5209514': [0.0, 23.0, 0.0], 'GSM5209515': [0.0, 41.0, 1.0], 'GSM5209516': [0.0, 32.0, 1.0], 'GSM5209517': [1.0, 23.0, 0.0], 'GSM5209518': [0.0, 36.0, 1.0], 'GSM5209519': [1.0, 26.0, 1.0], 'GSM5209520': [1.0, 23.0, 0.0], 'GSM5209521': [1.0, 36.0, 1.0], 'GSM5209522': [1.0, 40.0, 0.0], 'GSM5209523': [1.0, 26.0, 0.0], 'GSM5209524': [1.0, 18.0, 0.0], 'GSM5209525': [0.0, 35.0, 0.0], 'GSM5209526': [0.0, 24.0, 0.0], 'GSM5209527': [0.0, 32.0, 1.0], 'GSM5209528': [0.0, 61.0, 0.0], 'GSM5209529': [0.0, 34.0, 0.0], 'GSM5209530': [0.0, 54.0, 0.0], 'GSM5209531': [1.0, 21.0, 0.0], 'GSM5209532': [0.0, 28.0, 1.0], 'GSM5209533': [1.0, 38.0, 0.0], 'GSM5209534': [1.0, 69.0, 1.0], 'GSM5209535': [0.0, 28.0, 0.0], 'GSM5209536': [0.0, 27.0, 1.0], 'GSM5209537': [0.0, 33.0, 1.0], 'GSM5209538': [0.0, 24.0, 1.0], 'GSM5209539': [0.0, 19.0, 1.0], 'GSM5209540': [1.0, 32.0, 1.0], 'GSM5209541': [0.0, 40.0, 1.0], 'GSM5209542': [0.0, 39.0, 0.0], 'GSM5209543': [1.0, 29.0, 0.0], 'GSM5209544': [1.0, 26.0, 1.0], 'GSM5209545': [1.0, 26.0, 1.0], 'GSM5209546': [0.0, 18.0, 0.0], 'GSM5209547': [0.0, 38.0, 1.0], 'GSM5209548': [0.0, 59.0, 1.0], 'GSM5209549': [1.0, 53.0, 1.0], 'GSM5209550': [0.0, 41.0, 1.0], 'GSM5209551': [1.0, 24.0, 0.0], 'GSM5209552': [1.0, 28.0, 0.0], 'GSM5209553': [1.0, 30.0, 1.0], 'GSM5209554': [0.0, 31.0, 1.0], 'GSM5209555': [0.0, 47.0, 0.0], 'GSM5209556': [0.0, 76.0, 0.0], 'GSM5209557': [0.0, 27.0, 1.0], 'GSM5209558': [0.0, 36.0, 1.0], 'GSM5209559': [0.0, 19.0, 0.0], 'GSM5209560': [0.0, 38.0, 1.0], 'GSM5209561': [1.0, 24.0, 1.0], 'GSM5209562': [0.0, 33.0, 1.0], 'GSM5209563': [0.0, 23.0, 0.0], 'GSM5209564': [0.0, 20.0, 0.0], 'GSM5209565': [1.0, 38.0, 1.0], 'GSM5209566': [0.0, 68.0, 0.0], 'GSM5209567': [0.0, 23.0, 1.0], 'GSM5209568': [1.0, 39.0, 1.0], 'GSM5209569': [1.0, 23.0, 1.0], 'GSM5209570': [1.0, 23.0, 0.0], 'GSM5209571': [0.0, 39.0, 1.0], 'GSM5209572': [0.0, 38.0, 0.0], 'GSM5209573': [0.0, 20.0, 0.0], 'GSM5209574': [1.0, 54.0, 1.0], 'GSM5209575': [0.0, 41.0, 1.0], 'GSM5209576': [0.0, 48.0, 0.0], 'GSM5209577': [0.0, 74.0, 1.0], 'GSM5209578': [0.0, 69.0, 0.0], 'GSM5209579': [0.0, 42.0, 0.0], 'GSM5209580': [1.0, 25.0, 1.0], 'GSM5209581': [0.0, 35.0, 1.0], 'GSM5209582': [1.0, 30.0, 1.0], 'GSM5209583': [1.0, 23.0, 0.0], 'GSM5209584': [0.0, 36.0, 0.0], 'GSM5209585': [0.0, 61.0, 1.0], 'GSM5209586': [0.0, 37.0, 1.0], 'GSM5209587': [0.0, 50.0, 1.0], 'GSM5209588': [0.0, 46.0, 0.0], 'GSM5209589': [0.0, 22.0, 1.0], 'GSM5209590': [0.0, 21.0, 0.0], 'GSM5209591': [0.0, 44.0, 0.0], 'GSM5209592': [0.0, 24.0, 0.0], 'GSM5209593': [0.0, 24.0, 1.0], 'GSM5209594': [0.0, 23.0, 0.0], 'GSM5209595': [0.0, 47.0, 0.0], 'GSM5209596': [0.0, 21.0, 0.0], 'GSM5209597': [0.0, 19.0, 0.0], 'GSM5209598': [0.0, 56.0, 0.0], 'GSM5209599': [0.0, 25.0, 1.0], 'GSM5209600': [0.0, 54.0, 1.0], 'GSM5209601': [0.0, 51.0, 1.0], 'GSM5209602': [0.0, 43.0, 0.0], 'GSM5209603': [0.0, 53.0, 0.0], 'GSM5209604': [0.0, 66.0, 1.0], 'GSM5209605': [0.0, 69.0, 1.0], 'GSM5209606': [0.0, 22.0, 0.0], 'GSM5209607': [0.0, 56.0, 0.0], 'GSM5209608': [0.0, 51.0, 1.0], 'GSM5209609': [0.0, 69.0, 1.0], 'GSM5209610': [0.0, 53.0, 0.0], 'GSM5209611': [0.0, 61.0, 1.0], 'GSM5209612': [0.0, 52.0, 1.0], 'GSM5209613': [0.0, 42.0, 0.0], 'GSM5209614': [0.0, 56.0, 1.0], 'GSM5209615': [1.0, 58.0, 0.0], 'GSM5209616': [1.0, 20.0, 0.0], 'GSM5209617': [1.0, 17.0, 1.0], 'GSM5209618': [0.0, 40.0, 0.0], 'GSM5209619': [1.0, 44.0, 1.0], 'GSM5209620': [0.0, 45.0, 0.0], 'GSM5209621': [1.0, 19.0, 1.0], 'GSM5209622': [0.0, 28.0, 0.0], 'GSM5209623': [0.0, 57.0, 0.0], 'GSM5209624': [1.0, 41.0, 0.0], 'GSM5209625': [0.0, 34.0, 0.0], 'GSM5209626': [0.0, 54.0, 0.0], 'GSM5209627': [1.0, 59.0, 1.0], 'GSM5209628': [0.0, 20.0, 1.0]}\n",
"Clinical data shape: (3, 205)\n",
"Clinical features saved to ../../output/preprocess/Crohns_Disease/clinical_data/GSE169568.csv\n",
"Linking clinical and genetic data...\n",
"Linked data shape: (205, 11042)\n",
"Handling missing values...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (205, 11042)\n",
"\n",
"Checking for bias in feature variables:\n",
"For the feature 'Crohns_Disease', the least common label is '1.0' with 52 occurrences. This represents 25.37% of the dataset.\n",
"The distribution of the feature 'Crohns_Disease' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 24.0\n",
" 50% (Median): 34.0\n",
" 75%: 51.0\n",
"Min: 17.0\n",
"Max: 78.0\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '1.0' with 98 occurrences. This represents 47.80% 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/Crohns_Disease/GSE169568.csv\n",
"Final dataset shape: (205, 11042)\n"
]
}
],
"source": [
"# 1. Check if gene data is available after mapping\n",
"if gene_data.shape[0] == 0:\n",
" print(\"Error: Gene expression matrix is empty after mapping.\")\n",
" # Mark the dataset as not usable due to lack of gene expression data\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=False, # No usable gene data\n",
" is_trait_available=True,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"Failed to map probe IDs to gene symbols. The annotation format may not be compatible with the extraction methods.\"\n",
" )\n",
" print(\"Dataset deemed not usable due to lack of gene expression data.\")\n",
"else:\n",
" # Only proceed with normalization if we have gene data\n",
" print(\"Normalizing gene symbols...\")\n",
" gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n",
" print(f\"Gene data shape after normalization: {gene_data_normalized.shape[0]} genes × {gene_data_normalized.shape[1]} samples\")\n",
"\n",
" # Save the normalized gene data\n",
" os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
" gene_data_normalized.to_csv(out_gene_data_file)\n",
" print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
" \n",
" # Extract clinical features from the original data source\n",
" print(\"Extracting clinical features from the original source...\")\n",
" # Get background information and clinical data again\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",
" # Extract clinical features\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(\"Extracted clinical features preview:\")\n",
" print(preview_df(selected_clinical_df))\n",
" print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
" \n",
" # Save the extracted clinical features\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 features saved to {out_clinical_data_file}\")\n",
" \n",
" # Link clinical and genetic data\n",
" print(\"Linking clinical and genetic data...\")\n",
" linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data_normalized)\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
" \n",
" # Check if the linked data has adequate data\n",
" if linked_data.shape[0] == 0 or linked_data.shape[1] <= 4: # 4 is an arbitrary small number\n",
" print(\"Error: Linked data has insufficient samples or features.\")\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,\n",
" df=linked_data,\n",
" note=\"Failed to properly link gene expression data with clinical features.\"\n",
" )\n",
" print(\"Dataset deemed not usable due to linking failure.\")\n",
" else:\n",
" # Handle missing values systematically\n",
" print(\"Handling missing values...\")\n",
" linked_data_clean = handle_missing_values(linked_data, trait_col=trait)\n",
" print(f\"Data shape after handling missing values: {linked_data_clean.shape}\")\n",
" \n",
" # Check if there are still samples after missing value handling\n",
" if linked_data_clean.shape[0] == 0:\n",
" print(\"Error: No samples remain after handling missing values.\")\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,\n",
" df=pd.DataFrame(),\n",
" note=\"All samples were removed during missing value handling.\"\n",
" )\n",
" print(\"Dataset deemed not usable as all samples were filtered out.\")\n",
" else:\n",
" # Check if the dataset is biased\n",
" print(\"\\nChecking for bias in feature variables:\")\n",
" is_biased, linked_data_final = judge_and_remove_biased_features(linked_data_clean, trait)\n",
" \n",
" # Conduct final quality validation\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_final,\n",
" note=\"Dataset contains gene expression data for Crohn's Disease patients, examining response to Infliximab treatment.\"\n",
" )\n",
" \n",
" # Save linked data if usable\n",
" if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data_final.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" print(f\"Final dataset shape: {linked_data_final.shape}\")\n",
" else:\n",
" print(\"Dataset deemed not usable for trait association 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
}
|