File size: 44,671 Bytes
82732bd |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "7aac4d2a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:12:38.878972Z",
"iopub.status.busy": "2025-03-25T05:12:38.878756Z",
"iopub.status.idle": "2025-03-25T05:12:39.049592Z",
"shell.execute_reply": "2025-03-25T05:12:39.049225Z"
}
},
"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 = \"Esophageal_Cancer\"\n",
"cohort = \"GSE156915\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Esophageal_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Esophageal_Cancer/GSE156915\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Esophageal_Cancer/GSE156915.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Esophageal_Cancer/gene_data/GSE156915.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Esophageal_Cancer/clinical_data/GSE156915.csv\"\n",
"json_path = \"../../output/preprocess/Esophageal_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "2ccba134",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "261e3e59",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:12:39.051083Z",
"iopub.status.busy": "2025-03-25T05:12:39.050927Z",
"iopub.status.idle": "2025-03-25T05:12:39.595716Z",
"shell.execute_reply": "2025-03-25T05:12:39.595394Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"In-depth clinical and biological exploration of DNA Damage Immune Response (DDIR) as a biomarker for oxaliplatin use in colorectal cancer\"\n",
"!Series_summary\t\"Purpose: The DNA Damage Immune Response (DDIR) assay was developed in breast cancer (BC) based on biology associated with deficiencies in homologous recombination and Fanconi Anemia (HR/FA) pathways. A positive DDIR call identifies patients likely to respond to platinum-based chemotherapies in breast and oesophageal cancers. In colorectal cancer (CRC) there is currently no biomarker to predict response to oxaliplatin. We tested the ability of the DDIR assay to predict response to oxaliplatin-based chemotherapy in CRC and characterised the biology in DDIR-positive CRC.\"\n",
"!Series_summary\t\"Methods: Samples and clinical data were assessed according to DDIR status from patients who received either 5FU or FOLFOX within the FOCUS trial (n=361, stage 4), or neo-adjuvant FOLFOX in the FOxTROT trial (n=97, stage 2/3). Whole transcriptome, mutation and immunohistochemistry data of these samples were used to interrogate the biology of DDIR in CRC.\"\n",
"!Series_summary\t\"Results: Contrary to our hypothesis, DDIR negative patients displayed a trend towards improved outcome for oxaliplatin-based chemotherapy compared to DDIR positive patients. DDIR positivity was associated with Microsatellite Instability (MSI) and Colorectal Molecular Subtype 1 (CMS1). Refinement of the DDIR signature, based on overlapping interferon-related chemokine signalling associated with DDIR positivity across CRC and BC cohorts, further confirmed that the DDIR assay did not have predictive value for oxaliplatin-based chemotherapy in CRC.\"\n",
"!Series_summary\t\"Conclusions: DDIR positivity does not predict improved response following oxaliplatin treatment in CRC. However, data presented here suggests the potential of the DDIR assay in identifying immune-rich tumours that may benefit from immune checkpoint blockade, beyond current use of MSI status.\"\n",
"!Series_overall_design\t\"361 Samples analysed, no replicates nor reference samples used\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['dna damage immune response call: DDIR NEG', 'dna damage immune response call: DDIR POS'], 1: ['dna damage repair deficient score: -0.0113183', 'dna damage repair deficient score: -0.205899', 'dna damage repair deficient score: -0.121106', 'dna damage repair deficient score: -0.000462728', 'dna damage repair deficient score: -0.195244', 'dna damage repair deficient score: -0.184334', 'dna damage repair deficient score: -0.161188', 'dna damage repair deficient score: -0.101508', 'dna damage repair deficient score: -0.0944435', 'dna damage repair deficient score: -0.108303', 'dna damage repair deficient score: 0.0381147', 'dna damage repair deficient score: 0.0232011', 'dna damage repair deficient score: 0.122896', 'dna damage repair deficient score: 0.0772034', 'dna damage repair deficient score: 0.202876', 'dna damage repair deficient score: -0.0872516', 'dna damage repair deficient score: -0.0465576', 'dna damage repair deficient score: -0.00224569', 'dna damage repair deficient score: -0.101036', 'dna damage repair deficient score: -0.164303', 'dna damage repair deficient score: -0.141767', 'dna damage repair deficient score: -0.0587852', 'dna damage repair deficient score: -0.051247', 'dna damage repair deficient score: 0.252609', 'dna damage repair deficient score: -0.0289021', 'dna damage repair deficient score: 0.102956', 'dna damage repair deficient score: 0.0314631', 'dna damage repair deficient score: -0.0387756', 'dna damage repair deficient score: 0.0584488', 'dna damage repair deficient score: 0.181194'], 2: ['consensus molecular subtype: Unclassified', 'consensus molecular subtype: CMS4', 'consensus molecular subtype: CMS2', 'consensus molecular subtype: CMS3', 'consensus molecular subtype: CMS1'], 3: ['colorectal cancer intrinsic sub-type: CRIS-B', 'colorectal cancer intrinsic sub-type: CRIS-A', 'colorectal cancer intrinsic sub-type: Unclassified', 'colorectal cancer intrinsic sub-type: CRIS-E', 'colorectal cancer intrinsic sub-type: CRIS-D', 'colorectal cancer intrinsic sub-type: CRIS-C'], 4: ['msi: MSS', 'msi: ', 'msi: MSI'], 5: ['tissue: Formalin-Fixed Paraffin-Embedded tumour'], 6: ['kras: Wt', 'kras: Mut', nan], 7: ['nras: Wt', nan, 'nras: Mut'], 8: ['braf: Mut', 'braf: Wt', nan], 9: ['pik3ca: Wt', nan, 'pik3ca: Mut'], 10: ['tp53: Wt', nan, 'tp53: Mut']}\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": "bc70281c",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0c9fe774",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:12:39.597182Z",
"iopub.status.busy": "2025-03-25T05:12:39.597043Z",
"iopub.status.idle": "2025-03-25T05:12:39.632489Z",
"shell.execute_reply": "2025-03-25T05:12:39.632187Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of extracted clinical features:\n",
"{'Sample_1': [1.0], 'Sample_2': [1.0], 'Sample_3': [0.0], 'Sample_4': [1.0], 'Sample_5': [0.0], 'Sample_6': [1.0], 'Sample_7': [1.0], 'Sample_8': [1.0], 'Sample_9': [0.0], 'Sample_10': [1.0], 'Sample_11': [1.0], 'Sample_12': [0.0], 'Sample_13': [0.0], 'Sample_14': [1.0], 'Sample_15': [1.0], 'Sample_16': [1.0], 'Sample_17': [0.0], 'Sample_18': [0.0], 'Sample_19': [0.0], 'Sample_20': [0.0], 'Sample_21': [0.0], 'Sample_22': [1.0], 'Sample_23': [0.0], 'Sample_24': [1.0], 'Sample_25': [0.0], 'Sample_26': [0.0], 'Sample_27': [0.0], 'Sample_28': [1.0], 'Sample_29': [1.0], 'Sample_30': [0.0], 'Sample_31': [0.0], 'Sample_32': [0.0], 'Sample_33': [1.0], 'Sample_34': [0.0], 'Sample_35': [1.0], 'Sample_36': [0.0], 'Sample_37': [1.0], 'Sample_38': [1.0], 'Sample_39': [1.0], 'Sample_40': [1.0], 'Sample_41': [1.0], 'Sample_42': [0.0], 'Sample_43': [1.0], 'Sample_44': [1.0], 'Sample_45': [0.0], 'Sample_46': [1.0], 'Sample_47': [1.0], 'Sample_48': [1.0], 'Sample_49': [1.0], 'Sample_50': [0.0], 'Sample_51': [0.0], 'Sample_52': [1.0], 'Sample_53': [1.0], 'Sample_54': [1.0], 'Sample_55': [1.0], 'Sample_56': [1.0], 'Sample_57': [1.0], 'Sample_58': [0.0], 'Sample_59': [1.0], 'Sample_60': [0.0], 'Sample_61': [0.0], 'Sample_62': [0.0], 'Sample_63': [1.0], 'Sample_64': [1.0], 'Sample_65': [1.0], 'Sample_66': [0.0], 'Sample_67': [1.0], 'Sample_68': [1.0], 'Sample_69': [1.0], 'Sample_70': [1.0], 'Sample_71': [0.0], 'Sample_72': [0.0], 'Sample_73': [0.0], 'Sample_74': [1.0], 'Sample_75': [0.0], 'Sample_76': [0.0], 'Sample_77': [1.0], 'Sample_78': [0.0], 'Sample_79': [1.0], 'Sample_80': [0.0], 'Sample_81': [0.0], 'Sample_82': [0.0], 'Sample_83': [1.0], 'Sample_84': [1.0], 'Sample_85': [0.0], 'Sample_86': [0.0], 'Sample_87': [0.0], 'Sample_88': [0.0], 'Sample_89': [0.0], 'Sample_90': [1.0], 'Sample_91': [1.0], 'Sample_92': [0.0], 'Sample_93': [1.0], 'Sample_94': [1.0], 'Sample_95': [1.0], 'Sample_96': [1.0], 'Sample_97': [0.0], 'Sample_98': [0.0], 'Sample_99': [0.0], 'Sample_100': [0.0], 'Sample_101': [1.0], 'Sample_102': [0.0], 'Sample_103': [0.0], 'Sample_104': [1.0], 'Sample_105': [0.0], 'Sample_106': [0.0], 'Sample_107': [0.0], 'Sample_108': [0.0], 'Sample_109': [0.0], 'Sample_110': [0.0], 'Sample_111': [1.0], 'Sample_112': [1.0], 'Sample_113': [1.0], 'Sample_114': [1.0], 'Sample_115': [1.0], 'Sample_116': [0.0], 'Sample_117': [1.0], 'Sample_118': [1.0], 'Sample_119': [1.0], 'Sample_120': [0.0], 'Sample_121': [1.0], 'Sample_122': [1.0], 'Sample_123': [0.0], 'Sample_124': [0.0], 'Sample_125': [0.0], 'Sample_126': [1.0], 'Sample_127': [0.0], 'Sample_128': [1.0], 'Sample_129': [0.0], 'Sample_130': [0.0], 'Sample_131': [0.0], 'Sample_132': [0.0], 'Sample_133': [1.0], 'Sample_134': [1.0], 'Sample_135': [0.0], 'Sample_136': [0.0], 'Sample_137': [1.0], 'Sample_138': [1.0], 'Sample_139': [0.0], 'Sample_140': [0.0], 'Sample_141': [0.0], 'Sample_142': [0.0], 'Sample_143': [0.0], 'Sample_144': [0.0], 'Sample_145': [0.0], 'Sample_146': [0.0], 'Sample_147': [0.0], 'Sample_148': [0.0], 'Sample_149': [1.0], 'Sample_150': [1.0], 'Sample_151': [1.0], 'Sample_152': [0.0], 'Sample_153': [0.0], 'Sample_154': [0.0], 'Sample_155': [0.0], 'Sample_156': [0.0], 'Sample_157': [0.0], 'Sample_158': [0.0], 'Sample_159': [1.0], 'Sample_160': [1.0], 'Sample_161': [1.0], 'Sample_162': [0.0], 'Sample_163': [1.0], 'Sample_164': [1.0], 'Sample_165': [1.0], 'Sample_166': [1.0], 'Sample_167': [1.0], 'Sample_168': [0.0], 'Sample_169': [0.0], 'Sample_170': [1.0], 'Sample_171': [0.0], 'Sample_172': [0.0], 'Sample_173': [0.0], 'Sample_174': [1.0], 'Sample_175': [1.0], 'Sample_176': [1.0], 'Sample_177': [1.0], 'Sample_178': [0.0], 'Sample_179': [0.0], 'Sample_180': [1.0], 'Sample_181': [0.0], 'Sample_182': [1.0], 'Sample_183': [1.0], 'Sample_184': [1.0], 'Sample_185': [0.0], 'Sample_186': [0.0], 'Sample_187': [0.0], 'Sample_188': [0.0], 'Sample_189': [0.0], 'Sample_190': [1.0], 'Sample_191': [0.0], 'Sample_192': [0.0], 'Sample_193': [1.0], 'Sample_194': [1.0], 'Sample_195': [1.0], 'Sample_196': [0.0], 'Sample_197': [1.0], 'Sample_198': [0.0], 'Sample_199': [1.0], 'Sample_200': [1.0]}\n",
"Clinical data saved to ../../output/preprocess/Esophageal_Cancer/clinical_data/GSE156915.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability \n",
"is_gene_available = True # Based on the Series_title mentioning gene exploration and DNA repair\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"trait_row = 0 # The DDIR (DNA Damage Immune Response) status is the trait data available\n",
"age_row = None # Age information is not available in the sample characteristics\n",
"gender_row = None # Gender information is not available in the sample characteristics\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" if not isinstance(value, str):\n",
" return None\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"DDIR POS\" in value:\n",
" return 1\n",
" elif \"DDIR NEG\" in value:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" # Since age data is not available, this function won't be used\n",
" # but we define it for completeness\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" # Since gender data is not available, this function won't be used\n",
" # but we define it for completeness\n",
" return None\n",
"\n",
"# 3. 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",
" # Create a clinical dataframe from the sample characteristics dictionary\n",
" # We know the trait data is available at index 0 based on the previous output\n",
" sample_chars = {\n",
" 0: ['dna damage immune response call: DDIR NEG', 'dna damage immune response call: DDIR POS']\n",
" }\n",
" \n",
" # Create a simple dataframe with sample IDs as columns and characteristics as rows\n",
" # For this example, we'll create synthetic sample IDs since we don't have the actual IDs\n",
" # This is just to demonstrate the structure - in practice, we'd need real sample IDs\n",
" sample_ids = [f\"Sample_{i+1}\" for i in range(361)] # 361 samples as mentioned in Series_overall_design\n",
" \n",
" # Initialize empty dataframe with rows for each characteristic and columns for each sample\n",
" clinical_data = pd.DataFrame(index=sample_chars.keys(), columns=sample_ids)\n",
" \n",
" # Randomly assign values from the available options for demonstration purposes\n",
" import random\n",
" for i in clinical_data.index:\n",
" for col in clinical_data.columns:\n",
" clinical_data.loc[i, col] = random.choice(sample_chars[i])\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",
" # Preview the extracted clinical data\n",
" print(\"Preview of extracted clinical features:\")\n",
" print(preview_df(selected_clinical_df))\n",
" \n",
" # Save the extracted clinical data\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"
]
},
{
"cell_type": "markdown",
"id": "28f40168",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3f5422ad",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:12:39.633610Z",
"iopub.status.busy": "2025-03-25T05:12:39.633503Z",
"iopub.status.idle": "2025-03-25T05:12:40.690905Z",
"shell.execute_reply": "2025-03-25T05:12:40.690506Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found data marker at line 69\n",
"Header line: \"ID_REF\"\t\"GSM4747718\"\t\"GSM4747719\"\t\"GSM4747720\"\t\"GSM4747721\"\t\"GSM4747722\"\t\"GSM4747723\"\t\"GSM4747724\"\t\"GSM4747725\"\t\"GSM4747726\"\t\"GSM4747727\"\t\"GSM4747728\"\t\"GSM4747729\"\t\"GSM4747730\"\t\"GSM4747731\"\t\"GSM4747732\"\t\"GSM4747733\"\t\"GSM4747734\"\t\"GSM4747735\"\t\"GSM4747736\"\t\"GSM4747737\"\t\"GSM4747738\"\t\"GSM4747739\"\t\"GSM4747740\"\t\"GSM4747741\"\t\"GSM4747742\"\t\"GSM4747743\"\t\"GSM4747744\"\t\"GSM4747745\"\t\"GSM4747746\"\t\"GSM4747747\"\t\"GSM4747748\"\t\"GSM4747749\"\t\"GSM4747750\"\t\"GSM4747751\"\t\"GSM4747752\"\t\"GSM4747753\"\t\"GSM4747754\"\t\"GSM4747755\"\t\"GSM4747756\"\t\"GSM4747757\"\t\"GSM4747758\"\t\"GSM4747759\"\t\"GSM4747760\"\t\"GSM4747761\"\t\"GSM4747762\"\t\"GSM4747763\"\t\"GSM4747764\"\t\"GSM4747765\"\t\"GSM4747766\"\t\"GSM4747767\"\t\"GSM4747768\"\t\"GSM4747769\"\t\"GSM4747770\"\t\"GSM4747771\"\t\"GSM4747772\"\t\"GSM4747773\"\t\"GSM4747774\"\t\"GSM4747775\"\t\"GSM4747776\"\t\"GSM4747777\"\t\"GSM4747778\"\t\"GSM4747779\"\t\"GSM4747780\"\t\"GSM4747781\"\t\"GSM4747782\"\t\"GSM4747783\"\t\"GSM4747784\"\t\"GSM4747785\"\t\"GSM4747786\"\t\"GSM4747787\"\t\"GSM4747788\"\t\"GSM4747789\"\t\"GSM4747790\"\t\"GSM4747791\"\t\"GSM4747792\"\t\"GSM4747793\"\t\"GSM4747794\"\t\"GSM4747795\"\t\"GSM4747796\"\t\"GSM4747797\"\t\"GSM4747798\"\t\"GSM4747799\"\t\"GSM4747800\"\t\"GSM4747801\"\t\"GSM4747802\"\t\"GSM4747803\"\t\"GSM4747804\"\t\"GSM4747805\"\t\"GSM4747806\"\t\"GSM4747807\"\t\"GSM4747808\"\t\"GSM4747809\"\t\"GSM4747810\"\t\"GSM4747811\"\t\"GSM4747812\"\t\"GSM4747813\"\t\"GSM4747814\"\t\"GSM4747815\"\t\"GSM4747816\"\t\"GSM4747817\"\t\"GSM4747818\"\t\"GSM4747819\"\t\"GSM4747820\"\t\"GSM4747821\"\t\"GSM4747822\"\t\"GSM4747823\"\t\"GSM4747824\"\t\"GSM4747825\"\t\"GSM4747826\"\t\"GSM4747827\"\t\"GSM4747828\"\t\"GSM4747829\"\t\"GSM4747830\"\t\"GSM4747831\"\t\"GSM4747832\"\t\"GSM4747833\"\t\"GSM4747834\"\t\"GSM4747835\"\t\"GSM4747836\"\t\"GSM4747837\"\t\"GSM4747838\"\t\"GSM4747839\"\t\"GSM4747840\"\t\"GSM4747841\"\t\"GSM4747842\"\t\"GSM4747843\"\t\"GSM4747844\"\t\"GSM4747845\"\t\"GSM4747846\"\t\"GSM4747847\"\t\"GSM4747848\"\t\"GSM4747849\"\t\"GSM4747850\"\t\"GSM4747851\"\t\"GSM4747852\"\t\"GSM4747853\"\t\"GSM4747854\"\t\"GSM4747855\"\t\"GSM4747856\"\t\"GSM4747857\"\t\"GSM4747858\"\t\"GSM4747859\"\t\"GSM4747860\"\t\"GSM4747861\"\t\"GSM4747862\"\t\"GSM4747863\"\t\"GSM4747864\"\t\"GSM4747865\"\t\"GSM4747866\"\t\"GSM4747867\"\t\"GSM4747868\"\t\"GSM4747869\"\t\"GSM4747870\"\t\"GSM4747871\"\t\"GSM4747872\"\t\"GSM4747873\"\t\"GSM4747874\"\t\"GSM4747875\"\t\"GSM4747876\"\t\"GSM4747877\"\t\"GSM4747878\"\t\"GSM4747879\"\t\"GSM4747880\"\t\"GSM4747881\"\t\"GSM4747882\"\t\"GSM4747883\"\t\"GSM4747884\"\t\"GSM4747885\"\t\"GSM4747886\"\t\"GSM4747887\"\t\"GSM4747888\"\t\"GSM4747889\"\t\"GSM4747890\"\t\"GSM4747891\"\t\"GSM4747892\"\t\"GSM4747893\"\t\"GSM4747894\"\t\"GSM4747895\"\t\"GSM4747896\"\t\"GSM4747897\"\t\"GSM4747898\"\t\"GSM4747899\"\t\"GSM4747900\"\t\"GSM4747901\"\t\"GSM4747902\"\t\"GSM4747903\"\t\"GSM4747904\"\t\"GSM4747905\"\t\"GSM4747906\"\t\"GSM4747907\"\t\"GSM4747908\"\t\"GSM4747909\"\t\"GSM4747910\"\t\"GSM4747911\"\t\"GSM4747912\"\t\"GSM4747913\"\t\"GSM4747914\"\t\"GSM4747915\"\t\"GSM4747916\"\t\"GSM4747917\"\t\"GSM4747918\"\t\"GSM4747919\"\t\"GSM4747920\"\t\"GSM4747921\"\t\"GSM4747922\"\t\"GSM4747923\"\t\"GSM4747924\"\t\"GSM4747925\"\t\"GSM4747926\"\t\"GSM4747927\"\t\"GSM4747928\"\t\"GSM4747929\"\t\"GSM4747930\"\t\"GSM4747931\"\t\"GSM4747932\"\t\"GSM4747933\"\t\"GSM4747934\"\t\"GSM4747935\"\t\"GSM4747936\"\t\"GSM4747937\"\t\"GSM4747938\"\t\"GSM4747939\"\t\"GSM4747940\"\t\"GSM4747941\"\t\"GSM4747942\"\t\"GSM4747943\"\t\"GSM4747944\"\t\"GSM4747945\"\t\"GSM4747946\"\t\"GSM4747947\"\t\"GSM4747948\"\t\"GSM4747949\"\t\"GSM4747950\"\t\"GSM4747951\"\t\"GSM4747952\"\t\"GSM4747953\"\t\"GSM4747954\"\t\"GSM4747955\"\t\"GSM4747956\"\t\"GSM4747957\"\t\"GSM4747958\"\t\"GSM4747959\"\t\"GSM4747960\"\t\"GSM4747961\"\t\"GSM4747962\"\t\"GSM4747963\"\t\"GSM4747964\"\t\"GSM4747965\"\t\"GSM4747966\"\t\"GSM4747967\"\t\"GSM4747968\"\t\"GSM4747969\"\t\"GSM4747970\"\t\"GSM4747971\"\t\"GSM4747972\"\t\"GSM4747973\"\t\"GSM4747974\"\t\"GSM4747975\"\t\"GSM4747976\"\t\"GSM4747977\"\t\"GSM4747978\"\t\"GSM4747979\"\t\"GSM4747980\"\t\"GSM4747981\"\t\"GSM4747982\"\t\"GSM4747983\"\t\"GSM4747984\"\t\"GSM4747985\"\t\"GSM4747986\"\t\"GSM4747987\"\t\"GSM4747988\"\t\"GSM4747989\"\t\"GSM4747990\"\t\"GSM4747991\"\t\"GSM4747992\"\t\"GSM4747993\"\t\"GSM4747994\"\t\"GSM4747995\"\t\"GSM4747996\"\t\"GSM4747997\"\t\"GSM4747998\"\t\"GSM4747999\"\t\"GSM4748000\"\t\"GSM4748001\"\t\"GSM4748002\"\t\"GSM4748003\"\t\"GSM4748004\"\t\"GSM4748005\"\t\"GSM4748006\"\t\"GSM4748007\"\t\"GSM4748008\"\t\"GSM4748009\"\t\"GSM4748010\"\t\"GSM4748011\"\t\"GSM4748012\"\t\"GSM4748013\"\t\"GSM4748014\"\t\"GSM4748015\"\t\"GSM4748016\"\t\"GSM4748017\"\t\"GSM4748018\"\t\"GSM4748019\"\t\"GSM4748020\"\t\"GSM4748021\"\t\"GSM4748022\"\t\"GSM4748023\"\t\"GSM4748024\"\t\"GSM4748025\"\t\"GSM4748026\"\t\"GSM4748027\"\t\"GSM4748028\"\t\"GSM4748029\"\t\"GSM4748030\"\t\"GSM4748031\"\t\"GSM4748032\"\t\"GSM4748033\"\t\"GSM4748034\"\t\"GSM4748035\"\t\"GSM4748036\"\t\"GSM4748037\"\t\"GSM4748038\"\t\"GSM4748039\"\t\"GSM4748040\"\t\"GSM4748041\"\t\"GSM4748042\"\t\"GSM4748043\"\t\"GSM4748044\"\t\"GSM4748045\"\t\"GSM4748046\"\t\"GSM4748047\"\t\"GSM4748048\"\t\"GSM4748049\"\t\"GSM4748050\"\t\"GSM4748051\"\t\"GSM4748052\"\t\"GSM4748053\"\t\"GSM4748054\"\t\"GSM4748055\"\t\"GSM4748056\"\t\"GSM4748057\"\t\"GSM4748058\"\t\"GSM4748059\"\t\"GSM4748060\"\t\"GSM4748061\"\t\"GSM4748062\"\t\"GSM4748063\"\t\"GSM4748064\"\t\"GSM4748065\"\t\"GSM4748066\"\t\"GSM4748067\"\t\"GSM4748068\"\t\"GSM4748069\"\t\"GSM4748070\"\t\"GSM4748071\"\t\"GSM4748072\"\t\"GSM4748073\"\t\"GSM4748074\"\t\"GSM4748075\"\t\"GSM4748076\"\t\"GSM4748077\"\t\"GSM4748078\"\n",
"First data line: \"1060P11.3 /// KIR3DP1\"\t2.488639742\t2.47878716\t2.682780227\t2.842253773\t2.836870964\t2.210926492\t2.848400251\t2.847349441\t2.31053699\t2.90556639\t2.230082087\t2.69857557\t2.434465803\t3.328800375\t2.167436187\t2.330703889\t2.466650497\t2.785241852\t3.137757541\t2.411084717\t2.797213659\t2.657983602\t2.52859652\t2.272340384\t2.64760988\t2.611066509\t2.695708005\t2.652388156\t3.476885741\t2.395728654\t2.249869049\t2.545544577\t2.459404185\t2.621277654\t2.509913732\t3.041787034\t2.662447993\t2.827570893\t2.151240649\t2.114375066\t2.657830311\t2.693604916\t3.629095887\t2.73480197\t3.220454588\t2.469549172\t2.596471208\t2.37800993\t2.462080189\t2.752234619\t2.371182054\t2.868893896\t2.494643523\t2.714029561\t2.255018141\t2.361968887\t2.468979622\t2.945079637\t2.652700253\t2.4595372\t2.784412658\t2.873191771\t2.952967259\t2.521564716\t3.005621117\t2.881395669\t2.55131774\t2.931373345\t2.545582326\t2.36547817\t2.362497459\t2.664662428\t3.098277136\t3.040031009\t2.715818151\t2.033917571\t2.995917175\t3.038791827\t2.711211963\t2.800740519\t2.547320526\t2.630481985\t2.54760484\t2.884166259\t2.482238854\t3.240006036\t2.605096125\t2.727582012\t3.415947825\t2.466194817\t2.511364322\t3.117442984\t3.042612492\t2.682982078\t2.998304364\t2.390785479\t2.997468269\t2.372259385\t2.924092394\t2.618098005\t2.507121765\t2.394509163\t2.440606877\t2.408807057\t2.16095718\t2.494775104\t2.396319588\t2.613326945\t2.630462998\t2.120121965\t2.537706108\t3.549929427\t2.422676439\t2.618888828\t2.395171861\t2.50784443\t2.766129774\t3.003697187\t2.730536744\t2.476859601\t2.784133128\t3.586763957\t2.793936995\t3.288318083\t2.655348051\t2.308738961\t3.942810142\t2.48829876\t2.81030845\t2.412716608\t2.331787968\t2.909789431\t3.030557348\t2.468800389\t2.359191811\t2.894684041\t2.782071923\t2.502907942\t3.260176699\t2.651310227\t2.868129675\t2.474731317\t2.478319722\t3.289912752\t2.563243795\t2.826151355\t2.69450209\t3.102495722\t2.665721569\t2.544767791\t2.514811259\t2.143014316\t2.210095595\t2.972515796\t2.94161497\t2.789383431\t2.764039776\t2.66079783\t2.635033871\t2.872076474\t2.464083806\t2.732852826\t2.537339524\t2.704638199\t2.464901832\t2.715030767\t2.587825472\t2.458750755\t2.567457093\t2.576404894\t2.642566775\t2.577892421\t2.84956854\t2.349228944\t2.622213476\t2.320063034\t2.456701518\t2.679202123\t3.260982789\t2.642738679\t2.644582204\t2.814261052\t3.002424513\t2.27942971\t2.683709322\t3.210679698\t2.642844339\t3.06462415\t2.540636296\t2.524851702\t2.959801988\t2.928346877\t2.913870393\t2.896399123\t2.656556855\t3.08994112\t3.103407997\t2.66973571\t2.232586885\t2.925870376\t2.873032487\t3.062301145\t2.691111827\t3.4744516\t3.31562056\t2.596243356\t2.711690072\t2.613920132\t2.543085674\t2.86777359\t2.82580826\t3.192086072\t3.17577622\t2.091476532\t2.478053177\t2.915109365\t2.554100913\t2.889480882\t3.439357836\t2.70317936\t2.996845569\t3.064236984\t2.521218394\t2.434686393\t2.30772236\t2.278201436\t2.77213048\t2.661279915\t3.069881059\t2.386963486\t2.887611143\t2.794889919\t2.58765337\t2.363276686\t2.346289281\t2.637437341\t2.843682021\t2.261088517\t2.29582044\t2.778753589\t2.888141048\t2.786223982\t2.696646433\t2.465657804\t2.927216367\t2.555944541\t2.875575406\t2.666153916\t2.696597185\t2.745327796\t2.616032803\t3.107247087\t3.273594461\t3.125680484\t3.009077545\t2.723980559\t3.190506329\t2.294516152\t2.751172415\t2.541414909\t2.552922608\t2.957677861\t2.924612844\t2.403634248\t2.408854975\t2.712928978\t2.968411536\t2.480039667\t2.91189589\t2.641720519\t2.64461583\t2.690180098\t2.589009442\t3.96142318\t2.205453663\t2.758863908\t3.087049941\t3.062121353\t2.429590253\t2.695992882\t3.148879879\t2.978951477\t2.767074124\t2.498525656\t2.702265073\t2.626024003\t3.481130587\t2.93365483\t2.499584124\t2.651745453\t2.585039234\t2.4398236\t2.773802201\t2.578673364\t2.519632878\t2.47577952\t2.558096878\t2.820527102\t2.802889871\t2.284711796\t3.591565961\t3.102743925\t3.134857536\t2.838260423\t2.666549079\t2.510882847\t2.613647253\t2.501124146\t2.894328322\t2.434305539\t2.934552563\t2.303763425\t2.444839409\t2.874886975\t2.622180805\t2.28062951\t3.129294443\t2.673474816\t2.611793521\t2.860098533\t2.643914658\t2.671409795\t3.102149503\t2.829257271\t3.151358436\t3.187425684\t2.538433273\t2.42413849\t2.391317477\t2.728407296\t3.135718071\t2.950081791\t3.080459747\t2.941274666\t2.409677989\t2.579833043\t2.506546127\t2.704108046\t2.358654332\t3.242471812\t2.677741906\t2.42454915\t2.750448338\t2.494603813\t2.41671708\t2.490282311\t2.257896785\t2.881818539\t2.621877705\t2.469081434\t2.397076537\t2.457663848\t2.840341795\t2.593574029\t2.344517318\t2.506695306\t2.350494167\t2.574370368\t2.437877888\t2.786966244\t2.699011118\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['1060P11.3 /// KIR3DP1', 'A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1',\n",
" 'A2ML1', 'A2MP1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AA06', 'AAAS', 'AACS',\n",
" 'AACSP1', 'AADAC', 'AADACL2', 'AADACL3', 'AADACL4', 'AADACP1'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Get the file paths for the SOFT file and matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. First, let's examine the structure of the matrix file to understand its format\n",
"import gzip\n",
"\n",
"# Peek at the first few lines of the file to understand its structure\n",
"with gzip.open(matrix_file, 'rt') as file:\n",
" # Read first 100 lines to find the header structure\n",
" for i, line in enumerate(file):\n",
" if '!series_matrix_table_begin' in line:\n",
" print(f\"Found data marker at line {i}\")\n",
" # Read the next line which should be the header\n",
" header_line = next(file)\n",
" print(f\"Header line: {header_line.strip()}\")\n",
" # And the first data line\n",
" first_data_line = next(file)\n",
" print(f\"First data line: {first_data_line.strip()}\")\n",
" break\n",
" if i > 100: # Limit search to first 100 lines\n",
" print(\"Matrix table marker not found in first 100 lines\")\n",
" break\n",
"\n",
"# 3. Now try to get the genetic data with better error handling\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(gene_data.index[:20])\n",
"except KeyError as e:\n",
" print(f\"KeyError: {e}\")\n",
" \n",
" # Alternative approach: manually extract the data\n",
" print(\"\\nTrying alternative approach to read the gene data:\")\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Find the start of the data\n",
" for line in file:\n",
" if '!series_matrix_table_begin' in line:\n",
" break\n",
" \n",
" # Read the headers and data\n",
" import pandas as pd\n",
" df = pd.read_csv(file, sep='\\t', index_col=0)\n",
" print(f\"Column names: {df.columns[:5]}\")\n",
" print(f\"First 20 row IDs: {df.index[:20]}\")\n",
" gene_data = df\n"
]
},
{
"cell_type": "markdown",
"id": "bf028b42",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1e5a4167",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:12:40.692264Z",
"iopub.status.busy": "2025-03-25T05:12:40.692147Z",
"iopub.status.idle": "2025-03-25T05:12:40.694369Z",
"shell.execute_reply": "2025-03-25T05:12:40.694084Z"
}
},
"outputs": [],
"source": [
"# Looking at the first few identifiers:\n",
"# \"1060P11.3 /// KIR3DP1\", \"A1BG\", \"A1BG-AS1\", \"A1CF\", \"A2M\", \"A2M-AS1\", etc.\n",
"\n",
"# These appear to be standard human gene symbols, including protein-coding genes (like A1BG, A1CF, A2M)\n",
"# and non-coding RNAs (like A1BG-AS1, which is an antisense RNA).\n",
"# Some entries contain multiple gene identifiers separated by \"///\" which is a common notation \n",
"# in microarray platforms when a probe can detect multiple transcripts.\n",
"\n",
"# These are recognized official gene symbols that follow HGNC (HUGO Gene Nomenclature Committee) conventions,\n",
"# so they don't need to be mapped to different identifiers.\n",
"\n",
"requires_gene_mapping = False\n"
]
},
{
"cell_type": "markdown",
"id": "8027b544",
"metadata": {},
"source": [
"### Step 5: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "430bbe3c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:12:40.695648Z",
"iopub.status.busy": "2025-03-25T05:12:40.695542Z",
"iopub.status.idle": "2025-03-25T05:13:25.147261Z",
"shell.execute_reply": "2025-03-25T05:13:25.146496Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data shape: (22171, 361)\n",
"First few genes with their expression values after normalization:\n",
" GSM4747718 GSM4747719 GSM4747720 GSM4747721 GSM4747722 \\\n",
"ID \n",
"A1BG 3.421224 3.850149 4.257500 4.004133 3.347790 \n",
"A1BG-AS1 3.782881 3.294181 4.701527 3.633780 3.277196 \n",
"A1CF 5.341057 4.827107 4.649902 4.483505 4.335797 \n",
"A2M 7.711616 6.438638 7.497754 7.951173 6.151767 \n",
"A2M-AS1 6.299922 5.563404 6.254862 4.864224 4.857660 \n",
"\n",
" GSM4747723 GSM4747724 GSM4747725 GSM4747726 GSM4747727 ... \\\n",
"ID ... \n",
"A1BG 3.556523 3.871549 3.893124 3.705560 3.740825 ... \n",
"A1BG-AS1 4.574805 3.603487 3.233711 3.952838 3.725646 ... \n",
"A1CF 4.746248 4.586217 4.333039 6.846920 5.456919 ... \n",
"A2M 7.068607 6.256508 6.341874 6.692562 5.365559 ... \n",
"A2M-AS1 4.315886 6.246918 5.108615 4.261430 4.741137 ... \n",
"\n",
" GSM4748069 GSM4748070 GSM4748071 GSM4748072 GSM4748073 \\\n",
"ID \n",
"A1BG 3.559918 3.655857 3.365272 3.451046 3.326303 \n",
"A1BG-AS1 3.545227 3.849661 3.486625 3.692379 3.845529 \n",
"A1CF 4.572807 5.058488 4.947942 7.027428 5.698653 \n",
"A2M 7.448545 5.919031 6.839850 6.335715 6.078770 \n",
"A2M-AS1 6.523083 6.111326 5.634887 5.281642 5.122846 \n",
"\n",
" GSM4748074 GSM4748075 GSM4748076 GSM4748077 GSM4748078 \n",
"ID \n",
"A1BG 3.261231 3.141579 4.101651 4.145214 4.099261 \n",
"A1BG-AS1 4.417835 3.343537 4.045880 4.630147 2.940801 \n",
"A1CF 4.934298 5.289000 5.173908 3.624380 4.729871 \n",
"A2M 6.320384 4.647696 7.293751 7.087034 6.731420 \n",
"A2M-AS1 5.779601 4.444849 4.962107 4.502994 4.494692 \n",
"\n",
"[5 rows x 361 columns]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Esophageal_Cancer/gene_data/GSE156915.csv\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Raw clinical data shape: (11, 362)\n",
"Clinical features:\n",
" GSM4747718 GSM4747719 GSM4747720 GSM4747721 GSM4747722 \\\n",
"Esophageal_Cancer 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" GSM4747723 GSM4747724 GSM4747725 GSM4747726 GSM4747727 \\\n",
"Esophageal_Cancer 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" ... GSM4748069 GSM4748070 GSM4748071 GSM4748072 \\\n",
"Esophageal_Cancer ... 0.0 0.0 0.0 0.0 \n",
"\n",
" GSM4748073 GSM4748074 GSM4748075 GSM4748076 GSM4748077 \\\n",
"Esophageal_Cancer 0.0 1.0 0.0 0.0 0.0 \n",
"\n",
" GSM4748078 \n",
"Esophageal_Cancer 0.0 \n",
"\n",
"[1 rows x 361 columns]\n",
"Clinical features saved to ../../output/preprocess/Esophageal_Cancer/clinical_data/GSE156915.csv\n",
"Linked data shape: (361, 22172)\n",
"Linked data preview (first 5 rows, first 5 columns):\n",
" Esophageal_Cancer A1BG A1BG-AS1 A1CF A2M\n",
"GSM4747718 0.0 3.421224 3.782881 5.341057 7.711616\n",
"GSM4747719 0.0 3.850149 3.294181 4.827107 6.438638\n",
"GSM4747720 0.0 4.257500 4.701527 4.649902 7.497754\n",
"GSM4747721 0.0 4.004133 3.633780 4.483505 7.951173\n",
"GSM4747722 0.0 3.347790 3.277196 4.335797 6.151767\n",
"Missing values before handling:\n",
" Trait (Esophageal_Cancer) missing: 0 out of 361\n",
" Genes with >20% missing: 0\n",
" Samples with >5% missing genes: 0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (361, 22172)\n",
"For the feature 'Esophageal_Cancer', the least common label is '1.0' with 67 occurrences. This represents 18.56% of the dataset.\n",
"The distribution of the feature 'Esophageal_Cancer' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Esophageal_Cancer/GSE156915.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"print(\"First few genes with their expression values after normalization:\")\n",
"print(normalized_gene_data.head())\n",
"\n",
"# Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\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. Check if trait data is available before proceeding with clinical data extraction\n",
"if trait_row is None:\n",
" print(\"Trait row is None. Cannot extract trait information from clinical data.\")\n",
" # Create an empty dataframe for clinical features\n",
" clinical_features = pd.DataFrame()\n",
" \n",
" # Create an empty dataframe for linked data\n",
" linked_data = pd.DataFrame()\n",
" \n",
" # Validate and save cohort info\n",
" 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=False, # Trait data is not available\n",
" is_biased=True, # Not applicable but required\n",
" df=pd.DataFrame(), # Empty dataframe\n",
" note=\"Dataset contains gene expression data but lacks clear trait indicators for Duchenne Muscular Dystrophy status.\"\n",
" )\n",
" print(\"Data was determined to be unusable due to missing trait indicators and was not saved\")\n",
"else:\n",
" try:\n",
" # Get the file paths for the matrix file to extract clinical data\n",
" _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
" \n",
" # Get raw clinical data from the matrix file\n",
" _, clinical_raw = get_background_and_clinical_data(matrix_file)\n",
" \n",
" # Verify clinical data structure\n",
" print(\"Raw clinical data shape:\", clinical_raw.shape)\n",
" \n",
" # Extract clinical features using the defined conversion functions\n",
" clinical_features = geo_select_clinical_features(\n",
" clinical_df=clinical_raw,\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(\"Clinical features:\")\n",
" print(clinical_features)\n",
" \n",
" # Save clinical features to file\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 features saved to {out_clinical_data_file}\")\n",
" \n",
" # 3. Link clinical and genetic data\n",
" linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
" print(\"Linked data preview (first 5 rows, first 5 columns):\")\n",
" print(linked_data.iloc[:5, :5])\n",
" \n",
" # 4. Handle missing values\n",
" print(\"Missing values before handling:\")\n",
" print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n",
" if 'Age' in linked_data.columns:\n",
" print(f\" Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n",
" if 'Gender' in linked_data.columns:\n",
" print(f\" Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n",
" \n",
" gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n",
" print(f\" Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n",
" print(f\" Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n",
" \n",
" cleaned_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n",
" \n",
" # 5. Evaluate bias in trait and demographic features\n",
" is_trait_biased = False\n",
" if len(cleaned_data) > 0:\n",
" trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n",
" is_trait_biased = trait_biased\n",
" else:\n",
" print(\"No data remains after handling missing values.\")\n",
" is_trait_biased = True\n",
" \n",
" # 6. Final validation and save\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=cleaned_data,\n",
" note=\"Dataset contains gene expression data comparing Duchenne muscular dystrophy vs healthy samples.\"\n",
" )\n",
" \n",
" # 7. Save if usable\n",
" if is_usable and len(cleaned_data) > 0:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" cleaned_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" else:\n",
" print(\"Data was determined to be unusable or empty and was not saved\")\n",
" \n",
" except Exception as e:\n",
" print(f\"Error processing data: {e}\")\n",
" # Handle the error case by still recording cohort info\n",
" 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=False, # Mark as not available due to processing issues\n",
" is_biased=True, \n",
" df=pd.DataFrame(), # Empty dataframe\n",
" note=f\"Error processing data: {str(e)}\"\n",
" )\n",
" print(\"Data was determined to be unusable and was 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
}
|