File size: 25,127 Bytes
0d7438c |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "947e4742",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:50:06.647173Z",
"iopub.status.busy": "2025-03-25T05:50:06.647069Z",
"iopub.status.idle": "2025-03-25T05:50:06.812891Z",
"shell.execute_reply": "2025-03-25T05:50:06.812564Z"
}
},
"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 = \"Hypertrophic_Cardiomyopathy\"\n",
"cohort = \"GSE36961\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Hypertrophic_Cardiomyopathy\"\n",
"in_cohort_dir = \"../../input/GEO/Hypertrophic_Cardiomyopathy/GSE36961\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Hypertrophic_Cardiomyopathy/GSE36961.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv\"\n",
"json_path = \"../../output/preprocess/Hypertrophic_Cardiomyopathy/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "586a8fe0",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0cbfa276",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:50:06.814252Z",
"iopub.status.busy": "2025-03-25T05:50:06.814103Z",
"iopub.status.idle": "2025-03-25T05:50:07.136772Z",
"shell.execute_reply": "2025-03-25T05:50:07.136402Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Transcriptome Profiling of Surgical Myectomy Tissue from Patients with Hypertrophic Cardiomyopathy\"\n",
"!Series_summary\t\"Using a high-throughput gene expression profiling technology, we have been able to develop new hypotheses regarding the molecular pathogenic mechanisms of human hypertrophic cardiomyopathy (HCM). It is hoped that these hypotheses, among others generated by this data, will fuel future research endeavors that will uncover novel biomarkers, prognostic indicators, and therapeutic targets to improve our ability to diagnose, counsel, and treat patients with this highly heterogeneous and potentially life-threatening condition.\"\n",
"!Series_overall_design\t\"Case-control study comparing the messenger RNA transcriptome of cardiac tissues from patients with hypertrophic cardiomyopathy to the transcriptome of control donor cardiac tissues.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['Sex: male', 'Sex: female'], 1: ['age (yrs): 9', 'age (yrs): 10', 'age (yrs): 11', 'age (yrs): 13', 'age (yrs): 14', 'age (yrs): 15', 'age (yrs): 16', 'age (yrs): 17', 'age (yrs): 19', 'age (yrs): 20', 'age (yrs): 23', 'age (yrs): 26', 'age (yrs): 27', 'age (yrs): 28', 'age (yrs): 30', 'age (yrs): 31', 'age (yrs): 32', 'age (yrs): 33', 'age (yrs): 35', 'age (yrs): 37', 'age (yrs): 38', 'age (yrs): 41', 'age (yrs): 43', 'age (yrs): 44', 'age (yrs): 45', 'age (yrs): 46', 'age (yrs): 47', 'age (yrs): 48', 'age (yrs): 50', 'age (yrs): 51'], 2: ['tissue: cardiac', 'sample type: control'], 3: ['disease state: hypertrophic cardiomyopathy (HCM)', nan, 'sample type: control'], 4: ['sample type: case', nan]}\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": "361dc928",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e0d7a138",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:50:07.138094Z",
"iopub.status.busy": "2025-03-25T05:50:07.137976Z",
"iopub.status.idle": "2025-03-25T05:50:07.158319Z",
"shell.execute_reply": "2025-03-25T05:50:07.157990Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical Data Preview:\n",
"{'GSM907203': [1.0, 9.0, 1.0], 'GSM907204': [1.0, 10.0, 1.0], 'GSM907205': [1.0, 10.0, 0.0], 'GSM907206': [1.0, 11.0, 1.0], 'GSM907207': [1.0, 13.0, 0.0], 'GSM907208': [1.0, 14.0, 1.0], 'GSM907209': [1.0, 15.0, 1.0], 'GSM907210': [1.0, 15.0, 0.0], 'GSM907211': [1.0, 15.0, 1.0], 'GSM907212': [1.0, 15.0, 1.0], 'GSM907213': [1.0, 16.0, 0.0], 'GSM907214': [1.0, 16.0, 1.0], 'GSM907215': [1.0, 17.0, 0.0], 'GSM907216': [1.0, 19.0, 1.0], 'GSM907217': [1.0, 19.0, 1.0], 'GSM907218': [1.0, 20.0, 0.0], 'GSM907219': [1.0, 23.0, 1.0], 'GSM907220': [1.0, 23.0, 0.0], 'GSM907221': [1.0, 26.0, 1.0], 'GSM907222': [1.0, 27.0, 1.0], 'GSM907223': [1.0, 28.0, 1.0], 'GSM907224': [1.0, 30.0, 1.0], 'GSM907225': [1.0, 30.0, 0.0], 'GSM907226': [1.0, 30.0, 0.0], 'GSM907227': [1.0, 31.0, 1.0], 'GSM907228': [1.0, 32.0, 0.0], 'GSM907229': [1.0, 32.0, 0.0], 'GSM907230': [1.0, 33.0, 0.0], 'GSM907231': [1.0, 35.0, 0.0], 'GSM907232': [1.0, 35.0, 0.0], 'GSM907233': [1.0, 37.0, 0.0], 'GSM907234': [1.0, 37.0, 1.0], 'GSM907235': [1.0, 38.0, 1.0], 'GSM907236': [1.0, 38.0, 0.0], 'GSM907237': [1.0, 41.0, 1.0], 'GSM907238': [1.0, 43.0, 0.0], 'GSM907239': [1.0, 43.0, 1.0], 'GSM907240': [1.0, 43.0, 1.0], 'GSM907241': [1.0, 43.0, 1.0], 'GSM907242': [1.0, 44.0, 0.0], 'GSM907243': [1.0, 44.0, 0.0], 'GSM907244': [1.0, 44.0, 1.0], 'GSM907245': [1.0, 45.0, 0.0], 'GSM907246': [1.0, 45.0, 1.0], 'GSM907247': [1.0, 45.0, 1.0], 'GSM907248': [1.0, 45.0, 1.0], 'GSM907249': [1.0, 46.0, 1.0], 'GSM907250': [1.0, 46.0, 0.0], 'GSM907251': [1.0, 47.0, 1.0], 'GSM907252': [1.0, 48.0, 1.0], 'GSM907253': [1.0, 48.0, 0.0], 'GSM907254': [1.0, 50.0, 1.0], 'GSM907255': [1.0, 50.0, 0.0], 'GSM907256': [1.0, 51.0, 0.0], 'GSM907257': [1.0, 51.0, 0.0], 'GSM907258': [1.0, 51.0, 0.0], 'GSM907259': [1.0, 52.0, 0.0], 'GSM907260': [1.0, 52.0, 1.0], 'GSM907261': [1.0, 52.0, 0.0], 'GSM907262': [1.0, 52.0, 1.0], 'GSM907263': [1.0, 53.0, 0.0], 'GSM907264': [1.0, 53.0, 1.0], 'GSM907265': [1.0, 54.0, 0.0], 'GSM907266': [1.0, 54.0, 0.0], 'GSM907267': [1.0, 54.0, 1.0], 'GSM907268': [1.0, 55.0, 0.0], 'GSM907269': [1.0, 56.0, 0.0], 'GSM907270': [1.0, 56.0, 1.0], 'GSM907271': [1.0, 56.0, 0.0], 'GSM907272': [1.0, 56.0, 1.0], 'GSM907273': [1.0, 57.0, 1.0], 'GSM907274': [1.0, 58.0, 0.0], 'GSM907275': [1.0, 58.0, 1.0], 'GSM907276': [1.0, 59.0, 1.0], 'GSM907277': [1.0, 59.0, 1.0], 'GSM907278': [1.0, 59.0, 1.0], 'GSM907279': [1.0, 59.0, 0.0], 'GSM907280': [1.0, 59.0, 1.0], 'GSM907281': [1.0, 59.0, 1.0], 'GSM907282': [1.0, 60.0, 0.0], 'GSM907283': [1.0, 60.0, 1.0], 'GSM907284': [1.0, 62.0, 1.0], 'GSM907285': [1.0, 63.0, 1.0], 'GSM907286': [1.0, 64.0, 0.0], 'GSM907287': [1.0, 65.0, 1.0], 'GSM907288': [1.0, 65.0, 1.0], 'GSM907289': [1.0, 66.0, 0.0], 'GSM907290': [1.0, 67.0, 0.0], 'GSM907291': [1.0, 67.0, 0.0], 'GSM907292': [1.0, 67.0, 0.0], 'GSM907293': [1.0, 67.0, 1.0], 'GSM907294': [1.0, 67.0, 1.0], 'GSM907295': [1.0, 67.0, 1.0], 'GSM907296': [1.0, 69.0, 0.0], 'GSM907297': [1.0, 69.0, 0.0], 'GSM907298': [1.0, 70.0, 0.0], 'GSM907299': [1.0, 70.0, 0.0], 'GSM907300': [1.0, 71.0, 0.0], 'GSM907301': [1.0, 71.0, 0.0], 'GSM907302': [1.0, 71.0, 0.0], 'GSM907303': [1.0, 73.0, 0.0], 'GSM907304': [1.0, 73.0, 1.0], 'GSM907305': [1.0, 75.0, 1.0], 'GSM907306': [1.0, 76.0, 1.0], 'GSM907307': [1.0, 77.0, 0.0], 'GSM907308': [1.0, 78.0, 0.0], 'GSM907309': [nan, nan, 0.0], 'GSM907310': [0.0, 49.0, 0.0], 'GSM907311': [0.0, 48.0, 0.0], 'GSM907312': [nan, nan, 0.0], 'GSM907313': [0.0, 42.0, 0.0], 'GSM907314': [0.0, 53.0, 0.0], 'GSM907315': [nan, nan, 0.0], 'GSM907316': [0.0, 31.0, 0.0], 'GSM907317': [0.0, 54.0, 1.0], 'GSM907318': [0.0, 52.0, 1.0], 'GSM907319': [0.0, 47.0, 1.0], 'GSM907320': [0.0, 26.0, 1.0], 'GSM907321': [0.0, 65.0, 0.0], 'GSM907322': [0.0, 21.0, 1.0], 'GSM907323': [0.0, 41.0, 1.0], 'GSM907324': [0.0, 55.0, 1.0], 'GSM907325': [0.0, 61.0, 1.0], 'GSM907326': [0.0, 36.0, 0.0], 'GSM907327': [0.0, 7.0, 1.0], 'GSM907328': [0.0, 23.0, 1.0], 'GSM907329': [0.0, 17.0, 1.0], 'GSM907330': [0.0, 45.0, 0.0], 'GSM907331': [0.0, 40.0, 0.0], 'GSM907332': [0.0, 37.0, 0.0], 'GSM907333': [0.0, 51.0, 0.0], 'GSM907334': [0.0, 39.0, 1.0], 'GSM907335': [0.0, 37.0, 0.0], 'GSM907336': [0.0, 23.0, 1.0], 'GSM907337': [0.0, 19.0, 1.0], 'GSM907338': [0.0, 53.0, 0.0], 'GSM907339': [0.0, 48.0, 0.0], 'GSM907340': [0.0, 47.0, 0.0], 'GSM907341': [0.0, 4.0, 1.0], 'GSM907342': [0.0, 48.0, 0.0], 'GSM907343': [0.0, 25.0, 0.0], 'GSM907344': [0.0, 27.0, 1.0], 'GSM907345': [0.0, 21.0, 1.0], 'GSM907346': [0.0, 27.0, 1.0], 'GSM907347': [0.0, 21.0, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv\n"
]
}
],
"source": [
"# Check if gene expression data is available\n",
"# The background information mentions \"messenger RNA transcriptome\" comparing HCM to control samples\n",
"# This suggests gene expression data is available.\n",
"is_gene_available = True\n",
"\n",
"# Define conversion functions for clinical variables\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait value to binary (0=control, 1=HCM)\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" value = value.lower() if isinstance(value, str) else str(value).lower()\n",
" \n",
" # Extract the value after the colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"hypertrophic cardiomyopathy\" in value or \"hcm\" in value or \"case\" in value:\n",
" return 1\n",
" elif \"control\" in value:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to continuous\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" value = str(value)\n",
" \n",
" # Extract the value after the colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Extract numeric age\n",
" match = re.search(r'(\\d+)', value)\n",
" if match:\n",
" return int(match.group(1))\n",
" else:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender value to binary (0=female, 1=male)\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" value = value.lower() if isinstance(value, str) else str(value).lower()\n",
" \n",
" # Extract the value after the colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"female\" in value:\n",
" return 0\n",
" elif \"male\" in value:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# Identify rows with trait, age, and gender information\n",
"trait_row = None\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# Looking at the sample characteristics dictionary\n",
"# Row 0 contains gender information (\"Sex: male\", \"Sex: female\")\n",
"gender_row = 0\n",
"\n",
"# Row 1 contains age information with format \"age (yrs): XX\"\n",
"age_row = 1\n",
"\n",
"# Row 3 contains disease state information about HCM vs control\n",
"trait_row = 3\n",
"\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save metadata with initial filtering\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",
"# Extract clinical features if trait data is available\n",
"if trait_row is not None:\n",
" # Use geo_select_clinical_features to extract clinical features\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 clinical dataframe\n",
" preview = preview_df(clinical_df)\n",
" print(\"Clinical Data Preview:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical data to CSV\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": "2bad2705",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "59ba117c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:50:07.159515Z",
"iopub.status.busy": "2025-03-25T05:50:07.159407Z",
"iopub.status.idle": "2025-03-25T05:50:07.730381Z",
"shell.execute_reply": "2025-03-25T05:50:07.729988Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting gene data from matrix file:\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Successfully extracted gene data with 37846 rows\n",
"First 20 gene IDs:\n",
"Index(['7A5', 'A1BG', 'A1CF', 'A26A1', 'A26B1', 'A26C1B', 'A26C3', 'A2BP1',\n",
" 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS', 'AACS',\n",
" 'AACSL', 'AADAC', 'AADACL1', 'AADACL2'],\n",
" dtype='object', name='ID')\n",
"\n",
"Gene expression data available: True\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. Extract gene expression data from the matrix file\n",
"try:\n",
" print(\"Extracting gene data from matrix file:\")\n",
" gene_data = get_genetic_data(matrix_file)\n",
" if gene_data.empty:\n",
" print(\"Extracted gene expression data is empty\")\n",
" is_gene_available = False\n",
" else:\n",
" print(f\"Successfully extracted gene data with {len(gene_data.index)} rows\")\n",
" print(\"First 20 gene IDs:\")\n",
" print(gene_data.index[:20])\n",
" is_gene_available = True\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n",
" print(\"This dataset appears to have an empty or malformed gene expression matrix\")\n",
" is_gene_available = False\n",
"\n",
"print(f\"\\nGene expression data available: {is_gene_available}\")\n"
]
},
{
"cell_type": "markdown",
"id": "3bf8954c",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "972e19e1",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:50:07.731762Z",
"iopub.status.busy": "2025-03-25T05:50:07.731636Z",
"iopub.status.idle": "2025-03-25T05:50:07.733744Z",
"shell.execute_reply": "2025-03-25T05:50:07.733419Z"
}
},
"outputs": [],
"source": [
"# Review the gene identifiers in the gene expression data\n",
"\n",
"# The gene identifiers in the dataset appear to be standard human gene symbols.\n",
"# Examples like A1BG, AAAS, AACS are recognized human gene symbols.\n",
"# These are official HUGO Gene Nomenclature Committee (HGNC) symbols\n",
"# and do not require additional mapping to be used in analysis.\n",
"\n",
"requires_gene_mapping = False\n"
]
},
{
"cell_type": "markdown",
"id": "823862bc",
"metadata": {},
"source": [
"### Step 5: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a658f2b4",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:50:07.734915Z",
"iopub.status.busy": "2025-03-25T05:50:07.734805Z",
"iopub.status.idle": "2025-03-25T05:50:23.468125Z",
"shell.execute_reply": "2025-03-25T05:50:23.467237Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape before normalization: (37846, 145)\n",
"Gene data shape after normalization: (18660, 145)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv\n",
"Clinical data saved to ../../output/preprocess/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv\n",
"Linked data shape: (145, 18663)\n",
"\n",
"Handling missing values...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"After missing value handling, linked data shape: (142, 18663)\n",
"\n",
"Evaluating feature bias...\n",
"For the feature 'Hypertrophic_Cardiomyopathy', the least common label is '0.0' with 36 occurrences. This represents 25.35% of the dataset.\n",
"The distribution of the feature 'Hypertrophic_Cardiomyopathy' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 30.0\n",
" 50% (Median): 47.0\n",
" 75%: 58.0\n",
"Min: 4.0\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 '0.0' with 69 occurrences. This represents 48.59% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"Trait bias evaluation result: False\n",
"A new JSON file was created at: ../../output/preprocess/Hypertrophic_Cardiomyopathy/cohort_info.json\n",
"\n",
"Dataset usability: True\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Hypertrophic_Cardiomyopathy/GSE36961.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols and extract from step 3 and 6\n",
"# Load the gene expression data (already loaded from Step 6)\n",
"print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
"\n",
"try:\n",
" # Normalize gene symbols using the NCBI Gene database information\n",
" normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
" \n",
" if normalized_gene_data.empty:\n",
" print(\"Normalization resulted in empty dataframe. Using original gene data instead.\")\n",
" normalized_gene_data = gene_data\n",
" \n",
" print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
" \n",
" # Save the normalized gene data to the output file\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",
"except Exception as e:\n",
" print(f\"Error normalizing gene data: {e}. Using original gene data instead.\")\n",
" normalized_gene_data = gene_data\n",
" # Save the original gene data if normalization fails\n",
" os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
" normalized_gene_data.to_csv(out_gene_data_file)\n",
"\n",
"# 2. Link clinical and genetic data\n",
"# Use the trait_row identified in Step 2 (trait_row = 1) to extract trait data\n",
"is_trait_available = trait_row is not None\n",
"\n",
"if is_trait_available:\n",
" # Extract clinical features using the function and conversion methods from Step 2\n",
" clinical_features = 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",
" # Save clinical features\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" \n",
" # 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",
"else:\n",
" # Create a minimal dataframe with just the trait column\n",
" linked_data = pd.DataFrame({trait: [np.nan]})\n",
" print(\"No trait data available, creating minimal dataframe for validation.\")\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"if is_trait_available:\n",
" print(\"\\nHandling missing values...\")\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"After missing value handling, linked data shape: {linked_data.shape}\")\n",
"\n",
"# 4. Determine whether trait and demographic features are biased\n",
"if is_trait_available and not linked_data.empty and len(linked_data.columns) > 1:\n",
" print(\"\\nEvaluating feature bias...\")\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" print(f\"Trait bias evaluation result: {is_biased}\")\n",
"else:\n",
" is_biased = False\n",
" print(\"Skipping bias evaluation due to insufficient data.\")\n",
"\n",
"# 5. Final validation and save metadata\n",
"note = \"\"\n",
"if not is_trait_available:\n",
" note = f\"Dataset contains gene expression data but no {trait} measurements.\"\n",
"elif is_biased:\n",
" note = f\"Dataset contains {trait} data but its distribution is severely biased.\"\n",
"\n",
"# Validate 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=is_gene_available, \n",
" is_trait_available=is_trait_available, \n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=note\n",
")\n",
"\n",
"# 6. Save the linked data if usable\n",
"print(f\"\\nDataset usability: {is_usable}\")\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(f\"Dataset is not usable for {trait} association studies. 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
}
|