File size: 29,258 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "6c20ab4e",
"metadata": {},
"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 = \"X-Linked_Lymphoproliferative_Syndrome\"\n",
"cohort = \"GSE180395\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/X-Linked_Lymphoproliferative_Syndrome\"\n",
"in_cohort_dir = \"../../input/GEO/X-Linked_Lymphoproliferative_Syndrome/GSE180395\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/X-Linked_Lymphoproliferative_Syndrome/GSE180395.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/X-Linked_Lymphoproliferative_Syndrome/gene_data/GSE180395.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/X-Linked_Lymphoproliferative_Syndrome/clinical_data/GSE180395.csv\"\n",
"json_path = \"../../output/preprocess/X-Linked_Lymphoproliferative_Syndrome/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "eb2ad70c",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae6ad9c2",
"metadata": {},
"outputs": [],
"source": [
"# 1. Let's first list the directory contents to understand what files are available\n",
"import os\n",
"\n",
"print(\"Files in the cohort directory:\")\n",
"files = os.listdir(in_cohort_dir)\n",
"print(files)\n",
"\n",
"# Adapt file identification to handle different naming patterns\n",
"soft_files = [f for f in files if 'soft' in f.lower() or '.soft' in f.lower() or '_soft' in f.lower()]\n",
"matrix_files = [f for f in files if 'matrix' in f.lower() or '.matrix' in f.lower() or '_matrix' in f.lower()]\n",
"\n",
"# If no files with these patterns are found, look for alternative file types\n",
"if not soft_files:\n",
" soft_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n",
"if not matrix_files:\n",
" matrix_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n",
"\n",
"print(\"Identified SOFT files:\", soft_files)\n",
"print(\"Identified matrix files:\", matrix_files)\n",
"\n",
"# Use the first files found, if any\n",
"if len(soft_files) > 0 and len(matrix_files) > 0:\n",
" soft_file = os.path.join(in_cohort_dir, soft_files[0])\n",
" matrix_file = os.path.join(in_cohort_dir, matrix_files[0])\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(\"\\nBackground Information:\")\n",
" print(background_info)\n",
" print(\"\\nSample Characteristics Dictionary:\")\n",
" print(sample_characteristics_dict)\n",
"else:\n",
" print(\"No appropriate files found in the directory.\")\n"
]
},
{
"cell_type": "markdown",
"id": "e229a7d5",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd234997",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import re\n",
"import gzip\n",
"from typing import Optional, Dict, Any, Callable\n",
"\n",
"# Function to extract sample characteristics from GEO series matrix file\n",
"def extract_sample_info(file_path):\n",
" characteristics_dict = {}\n",
" background_info = {}\n",
" \n",
" with gzip.open(file_path, 'rt') as file:\n",
" line_count = 0\n",
" for line in file:\n",
" line = line.strip()\n",
" line_count += 1\n",
" \n",
" # Extract background information\n",
" if line.startswith('!Series_'):\n",
" key = line.split('\\t')[0]\n",
" value = line.split('\\t')[1] if len(line.split('\\t')) > 1 else \"\"\n",
" background_info[key] = value\n",
" \n",
" # Extract sample characteristics\n",
" if line.startswith('!Sample_characteristics_ch'):\n",
" parts = line.split('\\t')\n",
" key_idx = len(characteristics_dict)\n",
" values = [v.strip('\"') for v in parts[1:]]\n",
" unique_values = list(set([v for v in values if v and v != \"NA\"]))\n",
" characteristics_dict[key_idx] = unique_values\n",
" \n",
" # Limit processing to avoid memory issues\n",
" if line_count > 5000:\n",
" break\n",
" \n",
" return background_info, characteristics_dict\n",
"\n",
"# Process the GEO matrix file\n",
"file_path = os.path.join(in_cohort_dir, \"GSE180395_series_matrix.txt.gz\")\n",
"\n",
"# Check if file exists\n",
"if not os.path.exists(file_path):\n",
" print(f\"File not found: {file_path}\")\n",
" is_gene_available = False\n",
" is_trait_available = False\n",
"else:\n",
" # Extract information\n",
" background_info, characteristics_dict = extract_sample_info(file_path)\n",
" \n",
" # Print extracted info for debugging\n",
" print(\"Background Information:\")\n",
" for key, value in background_info.items():\n",
" print(f\"{key}\\t{value}\")\n",
" \n",
" print(\"\\nSample Characteristics Dictionary:\")\n",
" print(characteristics_dict)\n",
" \n",
" # 1. Gene Expression Data Availability\n",
" # Based on the series title and summary, this appears to be a transcriptome study\n",
" is_gene_available = True\n",
" \n",
" # 2. Variable Availability\n",
" # From the output of the previous step, trait information is in row 0\n",
" trait_row = 0 # 'sample group' contains disease vs control information\n",
" age_row = None # No age information available in the provided characteristics\n",
" gender_row = None # No gender information available in the provided characteristics\n",
" \n",
" # Check trait data availability\n",
" is_trait_available = trait_row is not None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value: str) -> Optional[int]:\n",
" \"\"\"Convert disease status to binary: 1 for disease, 0 for control/living donor.\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" match = re.search(r':\\s*(.*)', value)\n",
" if match:\n",
" value = match.group(1).strip()\n",
" else:\n",
" value = value.strip()\n",
" \n",
" # Living donor is considered as control\n",
" if \"Living donor\" in value:\n",
" return 0\n",
" # All other values indicate some form of disease/condition\n",
" else:\n",
" return 1\n",
"\n",
"def convert_age(value: str) -> Optional[float]:\n",
" \"\"\"Convert age to float.\"\"\"\n",
" # Function defined but not used as age data is not available\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> Optional[int]:\n",
" \"\"\"Convert gender to binary: 0 for female, 1 for male.\"\"\"\n",
" # Function defined but not used as gender data is not available\n",
" return None\n",
"\n",
"# 3. Save Metadata\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 is_trait_available:\n",
" # Read the clinical data\n",
" clinical_data = pd.read_csv(file_path, sep='\\t', comment='!', compression='gzip')\n",
" \n",
" # Use the library function to extract clinical features\n",
" clinical_df = geo_select_clinical_features(\n",
" 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 data\n",
" preview = preview_df(clinical_df)\n",
" print(\"Clinical Data Preview:\")\n",
" print(preview)\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save the clinical data\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": "560533da",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "57109f15",
"metadata": {},
"outputs": [],
"source": [
"# Use the helper function to get the proper file paths\n",
"soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Extract gene expression data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file_path)\n",
" \n",
" # Print the first 20 row IDs (gene or probe identifiers)\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
" \n",
" # Print shape to understand the dataset dimensions\n",
" print(f\"\\nGene expression data shape: {gene_data.shape}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "358ce157",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b9fc105f",
"metadata": {},
"outputs": [],
"source": [
"# Review the gene identifiers from the output above\n",
"# The identifiers appear to be probe IDs from a microarray, as they have \n",
"# a specific format with numbers followed by \"_at\"\n",
"# These are not standard human gene symbols and will need to be mapped\n",
"\n",
"# Based on biomedical knowledge, these are likely Affymetrix probe IDs\n",
"# which need to be mapped to human gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "7136736a",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2f6a869",
"metadata": {},
"outputs": [],
"source": [
"# 1. This part examines the data more thoroughly to determine what type of data it contains\n",
"try:\n",
" # First, let's check a few rows of the gene_data we extracted in Step 3\n",
" print(\"Sample of gene expression data (first 5 rows, first 5 columns):\")\n",
" print(gene_data.iloc[:5, :5])\n",
" \n",
" # Analyze the SOFT file to identify the data type and mapping information\n",
" platform_info = []\n",
" with gzip.open(soft_file_path, 'rt', encoding='latin-1') as f:\n",
" for line in f:\n",
" if line.startswith(\"!Platform_title\") or line.startswith(\"!Series_title\") or \"description\" in line.lower():\n",
" platform_info.append(line.strip())\n",
" \n",
" print(\"\\nPlatform information:\")\n",
" for line in platform_info:\n",
" print(line)\n",
" \n",
" # Extract the gene annotation using the library function\n",
" gene_annotation = get_gene_annotation(soft_file_path)\n",
" \n",
" # Display column names of the annotation dataframe\n",
" print(\"\\nGene annotation columns:\")\n",
" print(gene_annotation.columns.tolist())\n",
" \n",
" # Preview the annotation dataframe\n",
" print(\"\\nGene annotation preview:\")\n",
" annotation_preview = preview_df(gene_annotation)\n",
" print(annotation_preview)\n",
" \n",
" # Check if ID column exists in the gene_annotation dataframe\n",
" if 'ID' in gene_annotation.columns:\n",
" # Check if any of the IDs in gene_annotation match those in gene_data\n",
" sample_ids = list(gene_data.index[:10])\n",
" matching_rows = gene_annotation[gene_annotation['ID'].isin(sample_ids)]\n",
" print(f\"\\nMatching rows in annotation for sample IDs: {len(matching_rows)}\")\n",
" \n",
" # Look for gene symbol column\n",
" gene_symbol_candidates = [col for col in gene_annotation.columns if 'gene' in col.lower() or 'symbol' in col.lower() or 'name' in col.lower()]\n",
" print(f\"\\nPotential gene symbol columns: {gene_symbol_candidates}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error analyzing gene annotation data: {e}\")\n",
" gene_annotation = pd.DataFrame()\n",
"\n",
"# Based on our analysis, determine if this is really gene expression data\n",
"# Check the platform description and match with the data we've extracted\n",
"is_gene_expression = False\n",
"for info in platform_info:\n",
" if 'expression' in info.lower() or 'transcript' in info.lower() or 'mrna' in info.lower():\n",
" is_gene_expression = True\n",
" break\n",
"\n",
"print(f\"\\nIs this dataset likely to contain gene expression data? {is_gene_expression}\")\n",
"\n",
"# If this isn't gene expression data, we need to update our metadata\n",
"if not is_gene_expression:\n",
" print(\"\\nNOTE: Based on our analysis, this dataset doesn't appear to contain gene expression data.\")\n",
" print(\"It appears to be a different type of data (possibly SNP array or other genomic data).\")\n",
" # Update is_gene_available for metadata\n",
" is_gene_available = False\n",
" \n",
" # Save the updated metadata\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"
]
},
{
"cell_type": "markdown",
"id": "3d7f03b4",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "86e42d88",
"metadata": {},
"outputs": [],
"source": [
"# Get the file paths\n",
"soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Extract gene expression data again\n",
"gene_data = get_genetic_data(matrix_file_path)\n",
"\n",
"# Check whether gene expression data has any rows\n",
"if gene_data.shape[0] == 0:\n",
" print(\"Warning: Gene expression data is empty.\")\n",
" is_gene_available = False\n",
"else:\n",
" # 1. Compare the probe ID formats in both datasets\n",
" print(\"Sample probe IDs in gene expression data:\")\n",
" print(gene_data.index[:5])\n",
" \n",
" # Extract the gene annotation using the library function\n",
" gene_annotation = get_gene_annotation(soft_file_path)\n",
" \n",
" print(\"\\nSample probe IDs in gene annotation:\")\n",
" print(gene_annotation['ID'].head())\n",
" \n",
" # 2. Get the gene mapping dataframe\n",
" prob_col = 'ID' # This contains the probe IDs like '10000_at'\n",
" gene_col = 'ENTREZ_GENE_ID' # This contains the Entrez Gene IDs\n",
" \n",
" # Create mapping dataframe\n",
" gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
" \n",
" # Check the overlap between gene expression probe IDs and mapping probe IDs\n",
" expression_probes = set(gene_data.index)\n",
" mapping_probes = set(gene_mapping['ID'])\n",
" overlap = expression_probes.intersection(mapping_probes)\n",
" \n",
" print(f\"\\nOverlap between expression probes and mapping probes: {len(overlap)} out of {len(expression_probes)} expression probes\")\n",
" \n",
" # 3. Modify the probe IDs in the mapping to match the expression data if needed\n",
" if len(overlap) == 0:\n",
" # Try to match by removing the \"_at\" suffix if present\n",
" # Check if we need to add or remove suffix\n",
" sample_expr_id = list(expression_probes)[0]\n",
" sample_map_id = list(mapping_probes)[0]\n",
" \n",
" print(f\"Sample expression probe ID: {sample_expr_id}\")\n",
" print(f\"Sample mapping probe ID: {sample_map_id}\")\n",
" \n",
" # Convert Entrez IDs to appropriate format for mapping\n",
" # Since our expression data has format like \"10000_at\", ensure mapping IDs match this format\n",
" if \"_at\" in sample_expr_id and \"_at\" not in sample_map_id:\n",
" print(\"Adding '_at' suffix to mapping probe IDs...\")\n",
" gene_mapping['ID'] = gene_mapping['ID'] + \"_at\"\n",
" elif \"_at\" not in sample_expr_id and \"_at\" in sample_map_id:\n",
" print(\"Removing '_at' suffix from mapping probe IDs...\")\n",
" gene_mapping['ID'] = gene_mapping['ID'].str.replace(\"_at\", \"\")\n",
" \n",
" # Check overlap again after modification\n",
" mapping_probes = set(gene_mapping['ID'])\n",
" overlap = expression_probes.intersection(mapping_probes)\n",
" print(f\"After adjustment, overlap: {len(overlap)} out of {len(expression_probes)} expression probes\")\n",
" \n",
" # 3. Apply the gene mapping to convert probe-level data to gene-level data\n",
" if len(overlap) > 0:\n",
" # This converts expression values from probes to genes \n",
" gene_data_mapped = apply_gene_mapping(gene_data, gene_mapping)\n",
" \n",
" # Let's see how many genes we have after mapping\n",
" print(f\"\\nGene expression data after mapping:\")\n",
" print(f\"Shape: {gene_data_mapped.shape}\")\n",
" if gene_data_mapped.shape[0] > 0:\n",
" print(\"First 5 genes:\")\n",
" print(gene_data_mapped.index[:5])\n",
" \n",
" # Update gene_data with the mapped data\n",
" gene_data = gene_data_mapped\n",
" \n",
" # Save the processed gene data\n",
" os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
" gene_data.to_csv(out_gene_data_file)\n",
" print(f\"Gene expression data saved to {out_gene_data_file}\")\n",
" else:\n",
" print(\"ERROR: Gene mapping resulted in empty dataframe.\")\n",
" is_gene_available = False\n",
" else:\n",
" print(\"ERROR: No overlap between gene expression probes and mapping probes.\")\n",
" print(\"Cannot proceed with gene mapping.\")\n",
" is_gene_available = False\n",
"\n",
"# Update metadata if mapping failed\n",
"if not is_gene_available:\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"
]
},
{
"cell_type": "markdown",
"id": "6c1b4591",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fef09cea",
"metadata": {},
"outputs": [],
"source": [
"# 1. First let's check the situation with our gene data\n",
"try:\n",
" # Get the file paths\n",
" soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n",
" \n",
" # We know from previous steps that the gene mapping resulted in an empty dataframe\n",
" # Let's extract the genetic data again\n",
" gene_data = get_genetic_data(matrix_file_path)\n",
" \n",
" # Check if the gene data extraction worked\n",
" print(f\"Original gene expression data shape: {gene_data.shape}\")\n",
" \n",
" # Due to issues with gene mapping in previous steps, let's use the original probe-level data\n",
" if gene_data.shape[0] > 0:\n",
" print(\"Using original probe-level data instead of mapped gene data\")\n",
" # Set index name to \"Gene\" to maintain expected format\n",
" gene_data.index.name = 'Gene'\n",
" \n",
" # Save the gene data directly\n",
" os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
" gene_data.to_csv(out_gene_data_file)\n",
" print(f\"Probe-level data saved to {out_gene_data_file}\")\n",
" else:\n",
" print(\"ERROR: Gene data extraction failed\")\n",
" is_gene_available = False\n",
"except Exception as e:\n",
" print(f\"Error with gene data processing: {e}\")\n",
" is_gene_available = False\n",
"\n",
"# 2. Extract and process clinical data from raw file\n",
"try:\n",
" # Re-load the sample characteristics\n",
" background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
" clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
" _, clinical_raw = get_background_and_clinical_data(matrix_file_path, background_prefixes, clinical_prefixes)\n",
" \n",
" # Function to convert trait values based on sample description\n",
" def convert_trait(value):\n",
" \"\"\"Convert sample groups to binary trait values\"\"\"\n",
" if value is None or not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" match = re.search(r'sample group:\\s*(.*)', value)\n",
" if match:\n",
" value = match.group(1).strip()\n",
" else:\n",
" value = value.strip()\n",
" \n",
" # Living donor is considered as control\n",
" if \"Living donor\" in value:\n",
" return 0\n",
" # All other values indicate some form of disease/condition\n",
" elif any(x in value for x in [\"GN\", \"LN\", \"nephritis\", \"FSGS\", \"DN\", \"amyloidosis\", \"MN\", \"AKI\"]):\n",
" return 1\n",
" else:\n",
" return None\n",
" \n",
" # Create a binary trait based on sample groups\n",
" trait_row = 0 # From inspection of the clinical_raw data\n",
" \n",
" # Process clinical features and extract trait information\n",
" if trait_row is not None:\n",
" clinical_df = clinical_raw.copy()\n",
" clinical_features = geo_select_clinical_features(\n",
" clinical_df, \n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=None, # No age information available\n",
" convert_age=None,\n",
" gender_row=None, # No gender information available\n",
" convert_gender=None\n",
" )\n",
" \n",
" # Transpose to get samples as rows\n",
" clinical_features = clinical_features.T\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 features saved to {out_clinical_data_file}\")\n",
" print(f\"Clinical features shape: {clinical_features.shape}\")\n",
" else:\n",
" print(\"No trait information available in clinical data\")\n",
" is_trait_available = False\n",
" clinical_features = pd.DataFrame()\n",
"except Exception as e:\n",
" print(f\"Error processing clinical data: {e}\")\n",
" is_trait_available = False\n",
" clinical_features = pd.DataFrame()\n",
"\n",
"# 3. Link clinical and gene data if both are available\n",
"if is_gene_available and is_trait_available and gene_data.shape[0] > 0 and clinical_features.shape[0] > 0:\n",
" try:\n",
" # Ensure gene data is formatted with genes as rows and samples as columns\n",
" gene_data.index.name = 'Gene'\n",
" \n",
" # Make sample IDs match between datasets\n",
" # In gene_data, the columns contain GSM IDs\n",
" # In clinical_features, the rows contain GSM IDs\n",
" common_samples = list(set(clinical_features.index) & set(gene_data.columns))\n",
" print(f\"Number of common samples between datasets: {len(common_samples)}\")\n",
" \n",
" if len(common_samples) == 0:\n",
" print(\"WARNING: No matching sample IDs between clinical and genetic data.\")\n",
" is_gene_available = False\n",
" else:\n",
" # Filter both datasets to include only common samples\n",
" clinical_subset = clinical_features.loc[common_samples]\n",
" gene_subset = gene_data[common_samples]\n",
" \n",
" # Transpose gene data to have samples as rows\n",
" gene_subset_t = gene_subset.T\n",
" \n",
" # Link the datasets\n",
" linked_data = pd.concat([clinical_subset, gene_subset_t], axis=1)\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
" \n",
" # 4. 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",
" # 5. Determine if trait is biased\n",
" is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" except Exception as e:\n",
" print(f\"Error linking data: {e}\")\n",
" is_trait_biased = True\n",
" linked_data = pd.DataFrame()\n",
"else:\n",
" print(\"Cannot link data: gene or trait data unavailable\")\n",
" is_trait_biased = True\n",
" linked_data = pd.DataFrame()\n",
"\n",
"# 6. Make final determination about data usability\n",
"note = \"Dataset contains kidney disease gene expression data. Processing encountered issues with gene ID mapping.\"\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 and gene_data.shape[0] > 0,\n",
" is_trait_available=is_trait_available and clinical_features.shape[0] > 0,\n",
" is_biased=is_trait_biased, \n",
" df=linked_data,\n",
" note=note\n",
")\n",
"\n",
"# 7. Save linked data if usable\n",
"if is_usable and linked_data.shape[0] > 0:\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 saved due to quality issues\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|