File size: 30,412 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "b7d0cb3d",
"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 = \"Endometrioid_Cancer\"\n",
"cohort = \"GSE40785\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Endometrioid_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Endometrioid_Cancer/GSE40785\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Endometrioid_Cancer/GSE40785.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Endometrioid_Cancer/gene_data/GSE40785.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Endometrioid_Cancer/clinical_data/GSE40785.csv\"\n",
"json_path = \"../../output/preprocess/Endometrioid_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "9eae4e0a",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e708dab",
"metadata": {},
"outputs": [],
"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": "fbd437df",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c0aebfa3",
"metadata": {},
"outputs": [],
"source": [
"# Define data availability flags\n",
"is_gene_available = True # Dataset likely contains gene expression data based on background info\n",
"\n",
"# Define which rows in sample characteristics contain our features of interest\n",
"trait_row = 1 # The histology information \n",
"age_row = None # Age data is not available in sample characteristics\n",
"gender_row = None # Gender data is not available in sample characteristics\n",
"\n",
"# Define conversion functions for each variable\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert histology data to a binary indicating Endometrioid_Cancer (1) or not (0).\n",
" \"\"\"\n",
" if pd.isna(value):\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",
" # Convert to binary based on histology\n",
" if \"Endometrioid\" in value:\n",
" return 1 # Presence of Endometrioid cancer\n",
" else:\n",
" return 0 # Other histology types\n",
" \n",
"# Since age and gender are not available, we define placeholder functions\n",
"def convert_age(value):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" return None\n",
"\n",
"# Save metadata for initial filtering\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(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",
"# If trait data is available, extract clinical features using the sample characteristics dict\n",
"if trait_row is not None:\n",
" # Use the sample characteristics dictionary from the previous step output\n",
" sample_char_dict = {0: ['sample origin: Primary', 'sample origin: Xenograft', 'sample origin: Ascites', \n",
" 'histology: Adenocarcinoma NOS', 'sample origin: ascites', 'sample origin: primary'], \n",
" 1: ['histology: Mucinous', 'histology: Clear cell', 'histology: Papillary serous', \n",
" 'histology: Endometrioid', 'histology: Mullerian NOS', \n",
" 'histology: Mixed Endometrioid and Pap. serous', 'histology: Dysgerminoma', \n",
" 'histology: Carcinosarcoma', 'medium: RPMI', 'medium: OCMI', 'histology: Adenocarcinoma NOS'], \n",
" 2: ['sample type: fresh', 'sample type: frozen', 'medium: OCMI', None, 'medium: DMEM/F12', \n",
" \"medium: McCoy's 5A\", 'medium: MCDB105/M199', \"medium: Ham's F12\"], \n",
" 3: ['medium: OCMI', None]}\n",
" \n",
" # Create a mock clinical DataFrame with histology information\n",
" # Assume each unique value represents one sample\n",
" samples = []\n",
" trait_values = []\n",
" \n",
" # Extract values from row 1 (trait_row)\n",
" for value in sample_char_dict[trait_row]:\n",
" if pd.isna(value):\n",
" continue\n",
" samples.append(f\"Sample_{len(samples) + 1}\")\n",
" trait_values.append(value)\n",
" \n",
" # Create a DataFrame with samples as columns\n",
" data = {samples[i]: [trait_values[i]] for i in range(len(samples))}\n",
" clinical_data = pd.DataFrame(data, index=[trait_row])\n",
" \n",
" # Extract and process clinical features\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",
" # Preview the processed clinical features\n",
" preview = preview_df(clinical_features)\n",
" print(\"Preview of clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save the processed clinical data\n",
" # Ensure output directory exists\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "822b6cb6",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "65300647",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Set up paths for input files\n",
"clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
"gene_data_path = os.path.join(in_cohort_dir, \"gene_data.csv\")\n",
"\n",
"# Check if gene expression data is available\n",
"is_gene_available = os.path.exists(gene_data_path)\n",
"\n",
"# Define conversion functions for trait, age, and gender data\n",
"def convert_trait(value):\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" if isinstance(value, str):\n",
" value_lower = value.lower().strip()\n",
" # Extract the value part if in \"label: value\" format\n",
" if ':' in value_lower:\n",
" value_lower = value_lower.split(':', 1)[1].strip()\n",
" \n",
" # For endometrioid cancer studies, look for relevant keywords\n",
" if any(term in value_lower for term in ['cancer', 'tumor', 'malignant', 'carcinoma', 'endometrioid']):\n",
" return 1\n",
" elif any(term in value_lower for term in ['normal', 'control', 'healthy', 'non-cancer']):\n",
" return 0\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" if isinstance(value, str):\n",
" value_lower = value.lower().strip()\n",
" # Extract the value part if in \"label: value\" format\n",
" if ':' in value_lower:\n",
" value_lower = value_lower.split(':', 1)[1].strip()\n",
" \n",
" # Extract numeric age value\n",
" import re\n",
" matches = re.search(r'(\\d+)(?:\\s*years?)?', value_lower)\n",
" if matches:\n",
" try:\n",
" age = int(matches.group(1))\n",
" return age\n",
" except ValueError:\n",
" pass\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" if isinstance(value, str):\n",
" value_lower = value.lower().strip()\n",
" # Extract the value part if in \"label: value\" format\n",
" if ':' in value_lower:\n",
" value_lower = value_lower.split(':', 1)[1].strip()\n",
" \n",
" if any(term in value_lower for term in ['female', 'woman', 'women', 'f']):\n",
" return 0\n",
" elif any(term in value_lower for term in ['male', 'man', 'men', 'm']):\n",
" return 1\n",
" return None\n",
"\n",
"# Initialize variables\n",
"trait_row = None\n",
"age_row = None\n",
"gender_row = None\n",
"is_trait_available = False\n",
"\n",
"# Check if clinical data file exists and process it\n",
"if os.path.exists(clinical_data_path):\n",
" clinical_data = pd.read_csv(clinical_data_path)\n",
" \n",
" print(\"Clinical data shape:\", clinical_data.shape)\n",
" print(\"Clinical data columns:\", clinical_data.columns.tolist())\n",
" \n",
" # Examine the sample characteristics to find trait, age, and gender information\n",
" sample_characteristics = {}\n",
" for i in range(len(clinical_data)):\n",
" row_values = clinical_data.iloc[i].dropna().tolist()\n",
" unique_values = set(row_values)\n",
" sample_characteristics[i] = list(unique_values)\n",
" print(f\"Row {i} unique values: {sample_characteristics[i]}\")\n",
" \n",
" # Identify rows containing trait, age, and gender information\n",
" for row_idx, values in sample_characteristics.items():\n",
" for value in values:\n",
" if isinstance(value, str):\n",
" value_lower = value.lower()\n",
" \n",
" # Check for trait indicators (cancer/normal status)\n",
" if any(term in value_lower for term in ['cancer', 'tumor', 'carcinoma', 'normal', 'control', 'endometrioid']):\n",
" trait_row = row_idx\n",
" \n",
" # Check for age indicators\n",
" if 'age' in value_lower and any(char.isdigit() for char in value_lower):\n",
" age_row = row_idx\n",
" \n",
" # Check for gender indicators\n",
" if any(term in value_lower for term in ['gender', 'sex', 'female', 'male']):\n",
" gender_row = row_idx\n",
" \n",
" print(f\"Identified rows - Trait: {trait_row}, Age: {age_row}, Gender: {gender_row}\")\n",
" \n",
" # Check if trait data is available\n",
" is_trait_available = trait_row is not None\n",
"else:\n",
" print(\"Clinical data file not found.\")\n",
" clinical_data = pd.DataFrame() # Empty dataframe\n",
"\n",
"# Validate and save initial cohort info\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",
"# If trait data is available, extract and save clinical features\n",
"if is_trait_available:\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 if age_row is not None else None,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender if gender_row is not None else None\n",
" )\n",
" \n",
" # Preview the extracted clinical data\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"\\nPreview of extracted clinical data:\")\n",
" print(preview)\n",
" \n",
" # Create output directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save extracted clinical data\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to: {out_clinical_data_file}\")\n",
"else:\n",
" print(\"Trait data is not available.\")\n"
]
},
{
"cell_type": "markdown",
"id": "b35e4944",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d88814e6",
"metadata": {},
"outputs": [],
"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": "76c2538a",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "50168542",
"metadata": {},
"outputs": [],
"source": [
"# The identifiers start with \"ILMN_\" which indicates these are Illumina microarray probe IDs\n",
"# These are not standard human gene symbols, but rather probe identifiers from the Illumina platform\n",
"# We need to map these probe IDs to standard gene symbols for proper analysis\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "33a6ead7",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5c290972",
"metadata": {},
"outputs": [],
"source": [
"# 1. Let's first examine the structure of the SOFT file before trying to parse it\n",
"import gzip\n",
"\n",
"# Look at the first few lines of the SOFT file to understand its structure\n",
"print(\"Examining SOFT file structure:\")\n",
"try:\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" # Read first 20 lines to understand the file structure\n",
" for i, line in enumerate(file):\n",
" if i < 20:\n",
" print(f\"Line {i}: {line.strip()}\")\n",
" else:\n",
" break\n",
"except Exception as e:\n",
" print(f\"Error reading SOFT file: {e}\")\n",
"\n",
"# 2. Now let's try a more robust approach to extract the gene annotation\n",
"# Instead of using the library function which failed, we'll implement a custom approach\n",
"try:\n",
" # First, look for the platform section which contains gene annotation\n",
" platform_data = []\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" in_platform_section = False\n",
" for line in file:\n",
" if line.startswith('^PLATFORM'):\n",
" in_platform_section = True\n",
" continue\n",
" if in_platform_section and line.startswith('!platform_table_begin'):\n",
" # Next line should be the header\n",
" header = next(file).strip()\n",
" platform_data.append(header)\n",
" # Read until the end of the platform table\n",
" for table_line in file:\n",
" if table_line.startswith('!platform_table_end'):\n",
" break\n",
" platform_data.append(table_line.strip())\n",
" break\n",
" \n",
" # If we found platform data, convert it to a DataFrame\n",
" if platform_data:\n",
" import pandas as pd\n",
" import io\n",
" platform_text = '\\n'.join(platform_data)\n",
" gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n",
" low_memory=False, on_bad_lines='skip')\n",
" print(\"\\nGene annotation preview:\")\n",
" print(preview_df(gene_annotation))\n",
" else:\n",
" print(\"Could not find platform table in SOFT file\")\n",
" \n",
" # Try an alternative approach - extract mapping from other sections\n",
" with gzip.open(soft_file, 'rt') as file:\n",
" for line in file:\n",
" if 'ANNOTATION information' in line or 'annotation information' in line:\n",
" print(f\"Found annotation information: {line.strip()}\")\n",
" if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n",
" print(f\"Platform title: {line.strip()}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing gene annotation: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "134ecad3",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5c278c79",
"metadata": {},
"outputs": [],
"source": [
"# Extract the gene annotation from the SOFT file using the helper function\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# Identifying the relevant columns for mapping\n",
"# Based on the preview, we can see:\n",
"# - 'ID' contains the probe IDs (ILMN_*)\n",
"# - 'Symbol' contains gene symbols\n",
"\n",
"# Create the gene mapping dataframe\n",
"gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Symbol')\n",
"\n",
"# Print the first few rows of the mapping to verify\n",
"print(\"Gene mapping preview (first 5 rows):\")\n",
"print(gene_mapping.head())\n",
"\n",
"# Apply the gene mapping to convert probe-level data to gene-level data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Print the first few gene symbols and dimensions of the gene expression data\n",
"print(\"\\nGene data converted to gene symbols:\")\n",
"print(f\"Shape: {gene_data.shape}\")\n",
"print(f\"First 10 gene symbols: {gene_data.index[:10].tolist()}\")\n",
"\n",
"# Check if the mapping was successful by counting non-null gene entries\n",
"num_genes = len(gene_data.index)\n",
"print(f\"Number of genes after mapping: {num_genes}\")\n",
"\n",
"# Save the gene expression 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"
]
},
{
"cell_type": "markdown",
"id": "1f485a2c",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4afe2824",
"metadata": {},
"outputs": [],
"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",
"# Re-extract the gene expression data since it wasn't persisted from the previous step\n",
"gene_data = get_genetic_data(matrix_file)\n",
"print(f\"Re-extracted gene data shape: {gene_data.shape}\")\n",
"\n",
"# 1. Normalize gene symbols in the gene expression data\n",
"# First create the gene mapping again\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Symbol')\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Now normalize the mapped gene symbols\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 the clinical data from the matrix file\n",
"background_info, clinical_raw = get_background_and_clinical_data(matrix_file)\n",
"\n",
"# Define trait row based on the sample characteristics from Step 1\n",
"trait_row = 1 # Row containing histology information including \"Endometrioid\"\n",
"\n",
"# Define the conversion functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert histology data to binary for Endometrioid_Cancer\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Convert to binary based on histology\n",
" if isinstance(value, str) and \"Endometrioid\" in value:\n",
" return 1 # Presence of Endometrioid cancer\n",
" else:\n",
" return 0 # Other histology types\n",
"\n",
"# Age and gender not available in this dataset\n",
"age_row = None\n",
"gender_row = None\n",
"convert_age = None\n",
"convert_gender = None\n",
"\n",
"try:\n",
" # Extract clinical features\n",
" clinical_features = geo_select_clinical_features(\n",
" clinical_df=clinical_raw,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" print(\"Clinical features:\")\n",
" print(clinical_features)\n",
" \n",
" # Save clinical features to file\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
" \n",
" # 3. Link clinical and genetic data\n",
" linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
" print(f\"Linked data shape: {linked_data.shape}\")\n",
" print(\"Linked data preview (first 5 rows, first 5 columns):\")\n",
" print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Empty DataFrame\")\n",
" \n",
" # 4. Handle missing values\n",
" if not linked_data.empty:\n",
" print(\"Missing values before handling:\")\n",
" print(f\" Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n",
" if 'Age' in linked_data.columns:\n",
" print(f\" Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n",
" if 'Gender' in linked_data.columns:\n",
" print(f\" Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n",
" \n",
" gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n",
" print(f\" Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n",
" print(f\" Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n",
" \n",
" cleaned_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n",
" \n",
" # 5. Evaluate bias in trait and demographic features\n",
" trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n",
" \n",
" # 6. Final validation and save\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=True, \n",
" is_biased=trait_biased, \n",
" df=cleaned_data,\n",
" note=\"Dataset contains gene expression data with histology information, including endometrioid cancer samples.\"\n",
" )\n",
" \n",
" # 7. Save if usable\n",
" if is_usable and not cleaned_data.empty:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" cleaned_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" else:\n",
" print(\"Data was determined to be unusable or empty and was not saved\")\n",
" else:\n",
" print(\"No linked data could be created - either clinical or gene 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=True, \n",
" is_trait_available=False, \n",
" is_biased=True, \n",
" df=pd.DataFrame(),\n",
" note=\"Dataset contains gene expression data but clinical-genetic data linking failed.\"\n",
" )\n",
" \n",
"except Exception as e:\n",
" print(f\"Error in clinical data processing: {e}\")\n",
" import traceback\n",
" traceback.print_exc()\n",
" \n",
" # Still save the cohort info even if processing failed\n",
" validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=False, \n",
" is_biased=True, \n",
" df=pd.DataFrame(),\n",
" note=f\"Error during clinical data processing: {str(e)}\"\n",
" )\n",
" print(\"Data was determined to be unusable due to processing errors and was not saved\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|