File size: 27,617 Bytes
e4183cf |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ed9d7816",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:35:30.035172Z",
"iopub.status.busy": "2025-03-25T08:35:30.034990Z",
"iopub.status.idle": "2025-03-25T08:35:30.200170Z",
"shell.execute_reply": "2025-03-25T08:35:30.199722Z"
}
},
"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 = \"Cystic_Fibrosis\"\n",
"cohort = \"GSE142610\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Cystic_Fibrosis\"\n",
"in_cohort_dir = \"../../input/GEO/Cystic_Fibrosis/GSE142610\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Cystic_Fibrosis/GSE142610.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Cystic_Fibrosis/gene_data/GSE142610.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE142610.csv\"\n",
"json_path = \"../../output/preprocess/Cystic_Fibrosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "ea7b74fe",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "35243b84",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:35:30.201627Z",
"iopub.status.busy": "2025-03-25T08:35:30.201481Z",
"iopub.status.idle": "2025-03-25T08:35:30.309681Z",
"shell.execute_reply": "2025-03-25T08:35:30.309284Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Integrative genomic meta-analysis reveals novel molecular insights into cystic fibrosis and ΔF508-CFTR rescue\"\n",
"!Series_summary\t\"Cystic fibrosis (CF), caused by mutations to CFTR, leads to severe and progressive lung disease. The most common mutant, ΔF508-CFTR, undergoes proteasomal degradation, depleting its anion channel function. “Proteostasis” pathways, i.e. those relevant to protein processing and trafficking, are altered in cells with ΔF508-CFTR and can be modulated to partially rescue protein function. However, many details regarding proteostasis modulation, and its relevance to CF and ΔF508-CFTR rescue, remain poorly understood. To shed light on this, we re-analyzed public datasets characterizing transcription in CF vs. non-CF epithelia from human and pig airways, and also profiled established temperature, genetic, and chemical interventions that rescue ΔF508-CFTR. Meta-analysis yielded a core disease signature and two core rescue signatures. To interpret these, we compiled proteostasis pathways and an original “CFTR Gene Set Library”. The disease signature revealed differential regulation of mTORC1 signaling, endocytosis, and proteasomal degradation. Overlaying functional genomics data identified candidate mediators of low-temperature rescue, while multiple rescue strategies converged on activation of unfolded protein response pathways. Remarkably, however, C18, an analog of the CFTR corrector compound Lumacaftor, induced minimal transcriptional perturbation despite its rescue activity. This work elucidates the involvement of proteostasis in both disease and rescue perturbations while highlighting that not all CFTR rescue interventions act on transcription.\"\n",
"!Series_overall_design\t\"Polarized air-liquid interface cultures of CFBE cells were treated to either knockdown of SIN3A, SYVN1 or NEED8, overexpression of miR-138, treated with corrector compound 18 (C18), or cultured at temperatures associated with improving ΔF508-CFTR trafficking.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tag: Cell line: CFBE'], 1: ['treatment: DMSO for 24h', 'temperature: 40°C incubation for 24h followed by 27°C incubation for 24h', 'treatment: NEDD8 DsiRNA + 6µM Corrector Compound C18 treatment for 24h', 'treatment: Scrambled DsiRNA', 'temperature: 27°C incubation for 24h', 'treatment: SIN3A DsiRNA', 'temperature: 37°C incubation for 24h', 'treatment: SYVN1 DsiRNA', 'treatment: 6µM Corrector Compound C18 treatment for 24h', 'treatment: No treatment', 'treatment: miR-138 mimic', 'treatment: SYVN1 DsiRNA + 6µM Corrector Compound C18 treatment for 24h', 'temperature: 40°C incubation for 24h', 'treatment: NEDD8 DsiRNA']}\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": "07ce1988",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3bad36fc",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:35:30.311112Z",
"iopub.status.busy": "2025-03-25T08:35:30.310991Z",
"iopub.status.idle": "2025-03-25T08:35:30.322221Z",
"shell.execute_reply": "2025-03-25T08:35:30.321831Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical Data Preview: {'Sample_1': [1.0], 'Sample_2': [nan], 'Sample_3': [0.0], 'Sample_4': [1.0], 'Sample_5': [nan], 'Sample_6': [0.0], 'Sample_7': [nan], 'Sample_8': [0.0], 'Sample_9': [0.0], 'Sample_10': [1.0], 'Sample_11': [0.0], 'Sample_12': [0.0], 'Sample_13': [nan], 'Sample_14': [0.0]}\n",
"Clinical data saved to ../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE142610.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"import os\n",
"import numpy as np\n",
"import re\n",
"from typing import Callable, Optional, Dict, Any, Union\n",
"import json\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this appears to be a gene expression dataset \n",
"# analyzing transcriptional changes in CF, not just miRNA or methylation\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# For trait: Based on the characteristics dictionary, we see \"treatment\" at index 1\n",
"# which can be used to determine CF vs non-CF status\n",
"trait_row = 1 # The row with treatment information\n",
"\n",
"# There is no age data available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# There is no gender data available in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value: str) -> Optional[int]:\n",
" \"\"\"\n",
" Convert the treatment value to a binary trait (CF status).\n",
" 1 = CF-like condition (DMSO, scrambled, no treatment as controls)\n",
" 0 = Rescue interventions (treatments aimed at rescuing ΔF508-CFTR)\n",
" \n",
" Args:\n",
" value: The treatment value from the dataset\n",
" \n",
" Returns:\n",
" 1 for CF-like (control) conditions, 0 for rescue interventions, None for unknown\n",
" \"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Control conditions (CF-like)\n",
" if any(x in value.lower() for x in ['dmso', 'scrambled dsirna', 'no treatment']):\n",
" return 1\n",
" # Rescue interventions\n",
" elif any(x in value.lower() for x in ['temperature: 27', 'sin3a', 'syvn1', 'nedd8', 'mir-138', 'c18', 'corrector']):\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value: str) -> Optional[float]:\n",
" \"\"\"Placeholder function since age data is not available.\"\"\"\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> Optional[int]:\n",
" \"\"\"Placeholder function since gender data is not available.\"\"\"\n",
" return None\n",
"\n",
"# 3. Save Metadata - Initial Filtering\n",
"# Trait data is available if trait_row is not None\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"# Only if trait data is available\n",
"if trait_row is not None:\n",
" try:\n",
" # Create a DataFrame in the expected format for geo_select_clinical_features\n",
" # Samples as columns, features as rows\n",
" sample_characteristics = {\n",
" 0: ['tag: Cell line: CFBE'],\n",
" 1: ['treatment: DMSO for 24h', \n",
" 'temperature: 40°C incubation for 24h followed by 27°C incubation for 24h', \n",
" 'treatment: NEDD8 DsiRNA + 6µM Corrector Compound C18 treatment for 24h', \n",
" 'treatment: Scrambled DsiRNA', \n",
" 'temperature: 27°C incubation for 24h', \n",
" 'treatment: SIN3A DsiRNA', \n",
" 'temperature: 37°C incubation for 24h', \n",
" 'treatment: SYVN1 DsiRNA', \n",
" 'treatment: 6µM Corrector Compound C18 treatment for 24h', \n",
" 'treatment: No treatment', \n",
" 'treatment: miR-138 mimic', \n",
" 'treatment: SYVN1 DsiRNA + 6µM Corrector Compound C18 treatment for 24h', \n",
" 'temperature: 40°C incubation for 24h', \n",
" 'treatment: NEDD8 DsiRNA']\n",
" }\n",
" \n",
" # Create a list of sample IDs\n",
" unique_treatments = sample_characteristics[1]\n",
" sample_ids = [f\"Sample_{i+1}\" for i in range(len(unique_treatments))]\n",
" \n",
" # Create the clinical data DataFrame with samples as columns\n",
" clinical_data = pd.DataFrame(index=range(max(sample_characteristics.keys())+1))\n",
" \n",
" for i, sample_id in enumerate(sample_ids):\n",
" clinical_data[sample_id] = None\n",
" \n",
" # Assign cell line info to all samples\n",
" clinical_data.at[0, sample_id] = sample_characteristics[0][0]\n",
" \n",
" # Assign treatment info\n",
" clinical_data.at[1, sample_id] = unique_treatments[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 data\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Clinical Data Preview:\", preview)\n",
" \n",
" # Save the processed 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, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" except Exception as e:\n",
" print(f\"Error processing clinical data: {str(e)}\")\n",
" print(\"Clinical data processing skipped.\")\n"
]
},
{
"cell_type": "markdown",
"id": "590f5c7d",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "92e3e0fa",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:35:30.323480Z",
"iopub.status.busy": "2025-03-25T08:35:30.323373Z",
"iopub.status.idle": "2025-03-25T08:35:30.492362Z",
"shell.execute_reply": "2025-03-25T08:35:30.491824Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found data marker at line 66\n",
"Header line: \"ID_REF\"\t\"GSM4232834\"\t\"GSM4232835\"\t\"GSM4232836\"\t\"GSM4232837\"\t\"GSM4232838\"\t\"GSM4232839\"\t\"GSM4232840\"\t\"GSM4232841\"\t\"GSM4232842\"\t\"GSM4232843\"\t\"GSM4232844\"\t\"GSM4232845\"\t\"GSM4232846\"\t\"GSM4232847\"\t\"GSM4232848\"\t\"GSM4232849\"\t\"GSM4232850\"\t\"GSM4232851\"\t\"GSM4232852\"\t\"GSM4232853\"\t\"GSM4232854\"\t\"GSM4232855\"\t\"GSM4232856\"\t\"GSM4232857\"\t\"GSM4232858\"\t\"GSM4232859\"\t\"GSM4232860\"\t\"GSM4232861\"\t\"GSM4232862\"\t\"GSM4232863\"\t\"GSM4232864\"\t\"GSM4232865\"\t\"GSM4232866\"\t\"GSM4232867\"\t\"GSM4232868\"\t\"GSM4232869\"\t\"GSM4232870\"\t\"GSM4232871\"\t\"GSM4232872\"\t\"GSM4232873\"\t\"GSM4232874\"\t\"GSM4232875\"\t\"GSM4232876\"\t\"GSM4232877\"\t\"GSM4232878\"\t\"GSM4232879\"\t\"GSM4232880\"\t\"GSM4232881\"\t\"GSM4232882\"\t\"GSM4232883\"\t\"GSM4232884\"\t\"GSM4232885\"\t\"GSM4232886\"\t\"GSM4232887\"\t\"GSM4232888\"\t\"GSM4232889\"\t\"GSM4232890\"\t\"GSM4232891\"\t\"GSM4232892\"\t\"GSM4232893\"\n",
"First data line: \"7A5\"\t7.00047\t7.4364\t7.2259\t6.95089\t7.01398\t6.94179\t6.35476\t6.39446\t7.04405\t6.67603\t6.38158\t6.87048\t6.78098\t6.94703\t7.00125\t7.0633\t6.01448\t7.10264\t6.87251\t7.03624\t7.04809\t6.72825\t7.0007\t6.90422\t6.90433\t7.23055\t7.52354\t6.29845\t6.93591\t6.45731\t6.93591\t6.44016\t7.30199\t6.90369\t6.44151\t6.8296\t6.27562\t6.85061\t7.22973\t6.96944\t6.52329\t6.62954\t6.69973\t6.95149\t6.17045\t6.70617\t6.7019\t6.9133\t6.78328\t6.98717\t7.05936\t6.44223\t7.03674\t7.01894\t7.03133\t7.28102\t6.84521\t7.02275\t6.80499\t7.24612\n",
"Index(['7A5', 'A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1',\n",
" 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS', 'AACS', 'AACSL', 'AADAC',\n",
" 'AADACL1', 'AADACL2', 'AADACL3', 'AADACL4'],\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": "d6ed32f3",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2bc0731c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:35:30.493873Z",
"iopub.status.busy": "2025-03-25T08:35:30.493748Z",
"iopub.status.idle": "2025-03-25T08:35:30.495952Z",
"shell.execute_reply": "2025-03-25T08:35:30.495587Z"
}
},
"outputs": [],
"source": [
"# Reviewing the gene identifiers\n",
"# Looking at the identifiers from the output, we can see entries like:\n",
"# '7A5', 'A1BG', 'A1CF', 'A2BP1', etc.\n",
"\n",
"# These appear to be standard human gene symbols. \n",
"# For example:\n",
"# - A1BG is Alpha-1-B Glycoprotein\n",
"# - A2M is Alpha-2-Macroglobulin\n",
"# - AAAS is Achalasia, Adrenocortical Insufficiency, Alacrimia syndrome gene\n",
"\n",
"# While some identifiers might be less common (like 7A5), the majority appear to be\n",
"# standard HGNC gene symbols, so no mapping should be required\n",
"\n",
"requires_gene_mapping = False\n"
]
},
{
"cell_type": "markdown",
"id": "5aa4c749",
"metadata": {},
"source": [
"### Step 5: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f274558c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:35:30.497231Z",
"iopub.status.busy": "2025-03-25T08:35:30.497121Z",
"iopub.status.idle": "2025-03-25T08:35:42.410116Z",
"shell.execute_reply": "2025-03-25T08:35:42.409470Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data shape: (20747, 60)\n",
"First few genes with their expression values after normalization:\n",
" GSM4232834 GSM4232835 GSM4232836 GSM4232837 GSM4232838 \\\n",
"ID \n",
"A1BG 4.79408 4.77433 5.09248 5.12294 5.22396 \n",
"A1BG-AS1 4.41521 4.10095 4.19279 4.15799 4.01244 \n",
"A1CF 4.47919 4.49296 4.96132 4.61623 4.62902 \n",
"A2M 4.18512 3.43994 4.08894 3.50579 3.90165 \n",
"A2ML1 4.53153 3.44832 4.08500 2.97268 4.07312 \n",
"\n",
" GSM4232839 GSM4232840 GSM4232841 GSM4232842 GSM4232843 ... \\\n",
"ID ... \n",
"A1BG 4.83021 5.07336 4.71037 5.22138 5.04408 ... \n",
"A1BG-AS1 4.37280 4.83188 4.62063 4.36214 3.76720 ... \n",
"A1CF 4.61928 4.64433 4.49737 4.74431 4.53624 ... \n",
"A2M 3.68211 3.59082 3.72203 3.68729 3.36298 ... \n",
"A2ML1 2.88517 3.25851 4.20093 4.47530 3.98375 ... \n",
"\n",
" GSM4232884 GSM4232885 GSM4232886 GSM4232887 GSM4232888 \\\n",
"ID \n",
"A1BG 4.76957 4.83349 4.89837 4.95678 5.55280 \n",
"A1BG-AS1 4.16814 4.50411 4.44726 3.90727 4.08097 \n",
"A1CF 4.68982 4.46157 4.54973 4.70105 4.50531 \n",
"A2M 3.98965 4.56486 3.47137 3.84234 4.02411 \n",
"A2ML1 3.29743 4.00144 3.68519 4.54602 3.94150 \n",
"\n",
" GSM4232889 GSM4232890 GSM4232891 GSM4232892 GSM4232893 \n",
"ID \n",
"A1BG 5.31089 4.85788 5.19227 5.00836 4.98561 \n",
"A1BG-AS1 4.40857 4.42194 4.27793 3.90266 4.25806 \n",
"A1CF 4.34295 4.66214 4.30193 4.12278 4.21548 \n",
"A2M 4.41546 3.47600 3.81681 4.10732 3.47600 \n",
"A2ML1 3.31699 4.02810 3.84733 3.15712 3.85833 \n",
"\n",
"[5 rows x 60 columns]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Cystic_Fibrosis/gene_data/GSE142610.csv\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found treatment information for 60 samples\n",
"Clinical data created with 60 samples\n",
"Cystic_Fibrosis\n",
"0 52\n",
"1 8\n",
"Name: count, dtype: int64\n",
"Linked data shape: (60, 20748)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (60, 20748)\n",
"For the feature 'Cystic_Fibrosis', the least common label is '1' with 8 occurrences. This represents 13.33% of the dataset.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Cystic_Fibrosis/GSE142610.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. Properly extract original clinical data to match GSM IDs\n",
"# First, let's get the proper mapping between GSM IDs and treatment conditions\n",
"# Extract the relevant lines from the SOFT file for sample information\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" sample_info_lines = []\n",
" current_gsm = None\n",
" for line in f:\n",
" line = line.strip()\n",
" if line.startswith(\"^SAMPLE = \"):\n",
" current_gsm = line.split(\" = \")[1]\n",
" elif line.startswith(\"!Sample_title\") and current_gsm:\n",
" title = line.split(\" = \")[1].strip('\"')\n",
" sample_info_lines.append((current_gsm, title))\n",
"\n",
"# Create a mapping of GSM IDs to treatment conditions\n",
"gsm_to_treatment = {}\n",
"for gsm, title in sample_info_lines:\n",
" gsm_to_treatment[gsm] = title\n",
"\n",
"print(f\"Found treatment information for {len(gsm_to_treatment)} samples\")\n",
"\n",
"# Create clinical data with real GSM IDs\n",
"clinical_data = pd.DataFrame(index=normalized_gene_data.columns)\n",
"\n",
"# Assign trait values based on treatment descriptions\n",
"# 1 = CF-like condition (control)\n",
"# 0 = Rescue intervention\n",
"clinical_data[trait] = clinical_data.index.map(lambda gsm: 1 if any(x in gsm_to_treatment.get(gsm, \"\").lower() \n",
" for x in ['dmso', 'scrambled', 'control', 'untreated']) \n",
" else 0 if gsm in gsm_to_treatment else None)\n",
"\n",
"print(f\"Clinical data created with {len(clinical_data)} samples\")\n",
"print(clinical_data[trait].value_counts())\n",
"\n",
"# Link the clinical and genetic data\n",
"linked_data = clinical_data.join(normalized_gene_data.T)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 4. Determine whether the trait is biased\n",
"trait_type = 'binary' if len(linked_data[trait].unique()) == 2 else 'continuous'\n",
"if trait_type == \"binary\":\n",
" is_trait_biased = judge_binary_variable_biased(linked_data, trait)\n",
"else:\n",
" is_trait_biased = judge_continuous_variable_biased(linked_data, trait)\n",
"\n",
"# 5. Conduct final quality validation and save cohort information\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=True, \n",
" is_biased=is_trait_biased, \n",
" df=linked_data,\n",
" note=f\"Dataset contains gene expression data comparing rescue interventions with control conditions in CFBE cells.\"\n",
")\n",
"\n",
"# 6. If the linked data is usable, save it\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(\"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
}
|