File size: 25,351 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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f686e699",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:31:35.078946Z",
"iopub.status.busy": "2025-03-25T08:31:35.078626Z",
"iopub.status.idle": "2025-03-25T08:31:35.242332Z",
"shell.execute_reply": "2025-03-25T08:31:35.242015Z"
}
},
"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 = \"COVID-19\"\n",
"cohort = \"GSE275334\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/COVID-19\"\n",
"in_cohort_dir = \"../../input/GEO/COVID-19/GSE275334\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/COVID-19/GSE275334.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/COVID-19/gene_data/GSE275334.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/COVID-19/clinical_data/GSE275334.csv\"\n",
"json_path = \"../../output/preprocess/COVID-19/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "0ddfeb03",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "95216831",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:31:35.243705Z",
"iopub.status.busy": "2025-03-25T08:31:35.243565Z",
"iopub.status.idle": "2025-03-25T08:31:35.258937Z",
"shell.execute_reply": "2025-03-25T08:31:35.258680Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Immune Exhaustion in ME/CFS and long COVID\"\n",
"!Series_summary\t\"Gene expression analysis of RNA was performed using the commercially available NanoString® nCounter Immune Exhaustion gene expression panel (NanoString Technologies, Seattle, WA, USA). This panel contains 785 genes to elucidate mechanisms behind T cell, B cell and NK cell exhaustion in disease.\"\n",
"!Series_summary\t\"Ribonucleic acid (RNA) was extracted from peripheral blood mononuclear cells (PBMCs) isolated from ME/CFS (n=14), long COVID (n=15), and healthy control (HC; n=18) participants. ME/CFS participants were included according to Canadian Consensus Criteria for ME. Long COVID participants were eligible according to the working case definition for Post COVID-19 Condition published by the World Health Organization.\"\n",
"!Series_overall_design\t\"Raw gene expression data was normalised against positive and negative controls to account for background noise and platform-associated variation. Normalisation and analysis were performed using Rosalind Bio (San Diego, CA, USA) using geometric means of housekeeping genes (ABCF1, ALAS1, EEF1G, G6PD, GAPDH, GUSB, HPRT1, OAZ1, POLR1B, POLR2A, PPIA, RPL19, SDHA, TBP, TUBB) (Supplementary Material 1). Differential expression is reported between ME/CFS and long COVID with HC.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['cell type: PBMCs'], 1: ['age (years): 24', 'age (years): 46', 'age (years): 50', 'age (years): 37', 'age (years): 19', 'age (years): 40', 'age (years): 63', 'age (years): 54', 'age (years): 48', 'age (years): 34', 'age (years): 22', 'age (years): 59', 'age (years): 39', 'age (years): 27', 'age (years): 61', 'age (years): 38', 'age (years): 44', 'age (years): 41', 'age (years): 49', 'age (years): 43', 'age (years): 62', 'age (years): 30', 'age (years): 47', 'age (years): 53', 'age (years): 29', 'age (years): 32', 'age (years): 55', 'age (years): 51', 'age (years): 31', 'age (years): 60'], 2: ['Sex: Female', 'Sex: Male'], 3: ['disease: Healthy control', 'disease: Long COVID', 'disease: ME/CFS']}\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": "a0d7d779",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b87dc85e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:31:35.259876Z",
"iopub.status.busy": "2025-03-25T08:31:35.259774Z",
"iopub.status.idle": "2025-03-25T08:31:35.270366Z",
"shell.execute_reply": "2025-03-25T08:31:35.270097Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical Data Preview:\n",
"{'GSM8475033': [0.0, 24.0, 0.0], 'GSM8475034': [0.0, 46.0, 0.0], 'GSM8475035': [0.0, 50.0, 0.0], 'GSM8475036': [0.0, 37.0, 1.0], 'GSM8475037': [0.0, 19.0, 0.0], 'GSM8475038': [0.0, 40.0, 0.0], 'GSM8475039': [0.0, 46.0, 1.0], 'GSM8475040': [0.0, 63.0, 0.0], 'GSM8475041': [0.0, 54.0, 0.0], 'GSM8475042': [0.0, 46.0, 0.0], 'GSM8475043': [0.0, 48.0, 0.0], 'GSM8475044': [0.0, 34.0, 1.0], 'GSM8475045': [0.0, 22.0, 1.0], 'GSM8475046': [0.0, 59.0, 0.0], 'GSM8475047': [0.0, 39.0, 0.0], 'GSM8475048': [0.0, 27.0, 0.0], 'GSM8475049': [0.0, 61.0, 0.0], 'GSM8475050': [0.0, 38.0, 1.0], 'GSM8475051': [1.0, 44.0, 0.0], 'GSM8475052': [1.0, 41.0, 1.0], 'GSM8475053': [1.0, 49.0, 0.0], 'GSM8475054': [1.0, 19.0, 0.0], 'GSM8475055': [1.0, 38.0, 0.0], 'GSM8475056': [1.0, 43.0, 0.0], 'GSM8475057': [1.0, 62.0, 0.0], 'GSM8475058': [1.0, 30.0, 0.0], 'GSM8475059': [1.0, 59.0, 0.0], 'GSM8475060': [1.0, 40.0, 1.0], 'GSM8475061': [1.0, 61.0, 1.0], 'GSM8475062': [1.0, 47.0, 0.0], 'GSM8475063': [1.0, 59.0, 1.0], 'GSM8475064': [1.0, 37.0, 0.0], 'GSM8475065': [1.0, 53.0, 0.0], 'GSM8475066': [0.0, 30.0, 0.0], 'GSM8475067': [0.0, 29.0, 0.0], 'GSM8475068': [0.0, 48.0, 0.0], 'GSM8475069': [0.0, 32.0, 0.0], 'GSM8475070': [0.0, 55.0, 0.0], 'GSM8475071': [0.0, 51.0, 1.0], 'GSM8475072': [0.0, 48.0, 0.0], 'GSM8475073': [0.0, 31.0, 0.0], 'GSM8475074': [0.0, 60.0, 0.0], 'GSM8475075': [0.0, 24.0, 0.0], 'GSM8475076': [0.0, 47.0, 0.0], 'GSM8475077': [0.0, 20.0, 0.0], 'GSM8475078': [0.0, 42.0, 1.0], 'GSM8475079': [0.0, 41.0, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/COVID-19/clinical_data/GSE275334.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"import os\n",
"import json\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset appears to contain gene expression data\n",
"# from NanoString nCounter Immune Exhaustion gene expression panel, which includes 785 genes.\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# From the sample characteristics dictionary:\n",
"# - trait (COVID-19) can be inferred from \"disease\" in row 3\n",
"# - age is available in row 1\n",
"# - gender/sex is available in row 2\n",
"trait_row = 3\n",
"age_row = 1\n",
"gender_row = 2\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value: str) -> int:\n",
" \"\"\"\n",
" Convert trait values to binary (0 or 1).\n",
" For this dataset, we're looking for COVID-19 which maps to \"Long COVID\".\n",
" \"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Convert to binary based on whether it's \"Long COVID\" or not\n",
" if \"long covid\" in value.lower():\n",
" return 1\n",
" else:\n",
" return 0\n",
"\n",
"def convert_age(value: str) -> float:\n",
" \"\"\"\n",
" Convert age values to continuous (float).\n",
" \"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> int:\n",
" \"\"\"\n",
" Convert gender values to binary (0 for female, 1 for male).\n",
" \"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if value.lower() == \"female\":\n",
" return 0\n",
" elif value.lower() == \"male\":\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"# Initial filtering on usability\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",
" # Using the clinical_data variable that should be available from previous steps\n",
" try:\n",
" # Extract clinical features using the pre-existing clinical_data DataFrame\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 dataframe\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Clinical Data Preview:\")\n",
" print(preview)\n",
" \n",
" # Save to CSV\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 extracting clinical features: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "ca6318a9",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "323f70ed",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:31:35.271233Z",
"iopub.status.busy": "2025-03-25T08:31:35.271132Z",
"iopub.status.idle": "2025-03-25T08:31:35.284994Z",
"shell.execute_reply": "2025-03-25T08:31:35.284735Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOFT file: ../../input/GEO/COVID-19/GSE275334/GSE275334_family.soft.gz\n",
"Matrix file: ../../input/GEO/COVID-19/GSE275334/GSE275334_series_matrix.txt.gz\n",
"Found the matrix table marker at line 65\n",
"Gene data shape: (635, 47)\n",
"First 20 gene/probe identifiers:\n",
"['ACACA', 'ACADVL', 'ACAT2', 'ACOT1/2', 'ACSL3', 'ACSL4', 'ACSL6', 'ADORA2A', 'ADORA2B', 'AHR', 'AIFM1', 'AK4', 'AKT1', 'AKT2', 'AKT3', 'ALDH1A1', 'ALDH1B1', 'ALDOA', 'ALOX5', 'ANAPC4']\n"
]
}
],
"source": [
"# 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",
"print(f\"SOFT file: {soft_file}\")\n",
"print(f\"Matrix file: {matrix_file}\")\n",
"\n",
"# Set gene availability flag\n",
"is_gene_available = True # Initially assume gene data is available\n",
"\n",
"# First check if the matrix file contains the expected marker\n",
"found_marker = False\n",
"marker_row = None\n",
"try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for i, line in enumerate(file):\n",
" if \"!series_matrix_table_begin\" in line:\n",
" found_marker = True\n",
" marker_row = i\n",
" print(f\"Found the matrix table marker at line {i}\")\n",
" break\n",
" \n",
" if not found_marker:\n",
" print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n",
" is_gene_available = False\n",
" \n",
" # If marker was found, try to extract gene data\n",
" if is_gene_available:\n",
" try:\n",
" # Try using the library function\n",
" gene_data = get_genetic_data(matrix_file)\n",
" \n",
" if gene_data.shape[0] == 0:\n",
" print(\"Warning: Extracted gene data has 0 rows.\")\n",
" is_gene_available = False\n",
" else:\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" # Print the first 20 gene/probe identifiers\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20].tolist())\n",
" except Exception as e:\n",
" print(f\"Error extracting gene data with get_genetic_data(): {e}\")\n",
" is_gene_available = False\n",
" \n",
" # If gene data extraction failed, examine file content to diagnose\n",
" if not is_gene_available:\n",
" print(\"Examining file content to diagnose the issue:\")\n",
" try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Print lines around the marker if found\n",
" if marker_row is not None:\n",
" for i, line in enumerate(file):\n",
" if i >= marker_row - 2 and i <= marker_row + 10:\n",
" print(f\"Line {i}: {line.strip()[:100]}...\")\n",
" if i > marker_row + 10:\n",
" break\n",
" else:\n",
" # If marker not found, print first 10 lines\n",
" for i, line in enumerate(file):\n",
" if i < 10:\n",
" print(f\"Line {i}: {line.strip()[:100]}...\")\n",
" else:\n",
" break\n",
" except Exception as e2:\n",
" print(f\"Error examining file: {e2}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing file: {e}\")\n",
" is_gene_available = False\n",
"\n",
"# Update validation information if gene data extraction failed\n",
"if not is_gene_available:\n",
" print(\"Gene expression data could not be successfully extracted from this dataset.\")\n",
" # Update the validation record since gene data isn't available\n",
" is_trait_available = False # We already determined trait data isn't available in step 2\n",
" validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n",
" is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n"
]
},
{
"cell_type": "markdown",
"id": "95b737f2",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "41a6d09a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:31:35.285932Z",
"iopub.status.busy": "2025-03-25T08:31:35.285831Z",
"iopub.status.idle": "2025-03-25T08:31:35.287480Z",
"shell.execute_reply": "2025-03-25T08:31:35.287221Z"
}
},
"outputs": [],
"source": [
"# Based on the first 20 identifiers shown, these appear to be human gene symbols.\n",
"# The identifiers are in the format of human gene symbols like ACACA, ACADVL, etc.\n",
"# There is a mix of standard gene symbols and some combined identifiers (e.g., ACOT1/2)\n",
"# but overall these are human gene symbols and don't require mapping.\n",
"\n",
"requires_gene_mapping = False\n"
]
},
{
"cell_type": "markdown",
"id": "c481bb08",
"metadata": {},
"source": [
"### Step 5: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a7672867",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T08:31:35.288428Z",
"iopub.status.busy": "2025-03-25T08:31:35.288330Z",
"iopub.status.idle": "2025-03-25T08:31:35.430080Z",
"shell.execute_reply": "2025-03-25T08:31:35.429743Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data saved to ../../output/preprocess/COVID-19/gene_data/GSE275334.csv\n",
"Loaded clinical data shape: (3, 47)\n",
"Clinical features columns after transformation: ['COVID-19', 'Age', 'Gender']\n",
"Initial linked data shape: (47, 638)\n",
"Linked data shape after handling missing values: (46, 638)\n",
"For the feature 'COVID-19', the least common label is '1.0' with 15 occurrences. This represents 32.61% of the dataset.\n",
"The distribution of the feature 'COVID-19' in this dataset is fine.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 34.75\n",
" 50% (Median): 43.5\n",
" 75%: 50.75\n",
"Min: 19.0\n",
"Max: 63.0\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 12 occurrences. This represents 26.09% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"Linked data saved to ../../output/preprocess/COVID-19/GSE275334.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols and prepare for linking\n",
"try:\n",
" # Create output directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
" \n",
" # Save the gene data\n",
" gene_data.to_csv(out_gene_data_file)\n",
" print(f\"Gene data saved to {out_gene_data_file}\")\n",
" \n",
" # Attempt to link clinical and gene data\n",
" if 'trait_row' in locals() and trait_row is not None:\n",
" # Load clinical data from the previous step\n",
" try:\n",
" clinical_features = pd.read_csv(out_clinical_data_file)\n",
" print(f\"Loaded clinical data shape: {clinical_features.shape}\")\n",
" \n",
" # Convert clinical_features to the correct format for linking\n",
" clinical_features.set_index(clinical_features.columns[0], inplace=True)\n",
" clinical_features = clinical_features.T\n",
" clinical_features.columns = [trait, 'Age', 'Gender']\n",
" \n",
" print(\"Clinical features columns after transformation:\", clinical_features.columns.tolist())\n",
" \n",
" # Link the clinical and genetic data\n",
" linked_data = pd.concat([clinical_features, gene_data.T], axis=1)\n",
" print(f\"Initial linked data shape: {linked_data.shape}\")\n",
" \n",
" # Handle missing values\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" if linked_data.shape[0] > 0:\n",
" # Check for bias in trait and demographic features\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" \n",
" # Validate data quality 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=True,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=\"Successfully processed gene expression data for COVID-19.\"\n",
" )\n",
" \n",
" # Save the linked data if it's 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(\"Data not usable for trait study - not saving final linked data.\")\n",
" else:\n",
" print(\"After handling missing values, no samples remain.\")\n",
" 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=True,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"No valid samples after handling missing values.\"\n",
" )\n",
" except Exception as e:\n",
" print(f\"Error loading or processing clinical data: {e}\")\n",
" # Try to create a minimal response in case of error\n",
" 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=False,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=f\"Error processing clinical data: {str(e)}\"\n",
" )\n",
" else:\n",
" # Cannot proceed with linking if trait data is missing\n",
" 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=False,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"Cannot link data because trait information is not available.\"\n",
" )\n",
"except Exception as e:\n",
" print(f\"Error in data processing: {e}\")\n",
" \n",
" # Log the error and mark the dataset as unusable\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=False,\n",
" is_trait_available=False,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=f\"Error during normalization or linking: {str(e)}\"\n",
" )"
]
}
],
"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
}
|