File size: 25,359 Bytes
92d2f89 |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "d9076d8c",
"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 = \"Alopecia\"\n",
"cohort = \"GSE81071\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Alopecia\"\n",
"in_cohort_dir = \"../../input/GEO/Alopecia/GSE81071\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Alopecia/GSE81071.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Alopecia/gene_data/GSE81071.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Alopecia/clinical_data/GSE81071.csv\"\n",
"json_path = \"../../output/preprocess/Alopecia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "ceb11d68",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6cf428f9",
"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": "0f491e13",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21767180",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Availability Analysis\n",
"# Based on background info, this is a gene expression dataset from skin biopsies\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Identifying rows for trait, age, and gender\n",
"\n",
"# For trait (Alopecia):\n",
"# Looking at sample characteristics, there is no explicit mention of alopecia\n",
"# But the series title mentions \"discoid lesions (DLE) are often circular and frequently lead to alopecia\"\n",
"# We can infer that DLE cases could be considered as potentially having alopecia\n",
"trait_row = 1 # \"disease state\" in row 1 contains DLE which can be associated with alopecia\n",
"\n",
"# For age and gender:\n",
"# Neither age nor gender information appears to be available in the sample characteristics\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert disease state values to binary for Alopecia trait\n",
" DLE is associated with alopecia according to the background info\n",
" \"\"\"\n",
" if value is None:\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",
" # Based on background info, DLE is associated with alopecia\n",
" if value.lower() == \"dle\":\n",
" return 1 # Positive for alopecia risk/condition\n",
" elif value.lower() in [\"healthy\", \"normal\", \"scle\"]:\n",
" return 0 # Not associated with alopecia\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Placeholder for age conversion - not used since age data is not available\"\"\"\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Placeholder for gender conversion - not used since gender data is not available\"\"\"\n",
" return None\n",
"\n",
"# 3. Save metadata\n",
"# Check if trait data is available (trait_row is not None)\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save initial validation information\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 execute if trait_row is not None\n",
"if trait_row is not None:\n",
" # Create DataFrame from the sample characteristics dictionary\n",
" # The dictionary structure shows rows with lists of values\n",
" sample_char_dict = {\n",
" 0: ['tissue: Skin', 'disease state: Normal', 'disease state: DLE', 'disease state: SCLE'], \n",
" 1: ['disease state: healthy', 'disease state: DLE', 'disease state: sCLE', 'tissue: Skin biopsy']\n",
" }\n",
" \n",
" # Convert sample characteristics dictionary to a DataFrame format\n",
" sample_values = []\n",
" for i in range(max(sample_char_dict.keys()) + 1):\n",
" if i in sample_char_dict:\n",
" sample_values.append(sample_char_dict[i])\n",
" else:\n",
" sample_values.append([])\n",
" \n",
" clinical_data = pd.DataFrame(sample_values)\n",
" \n",
" # Extract clinical features\n",
" clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the processed clinical data\n",
" print(\"Preview of processed clinical data:\")\n",
" print(preview_df(clinical_df))\n",
" \n",
" # Save the clinical data\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "2d1c9cab",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "853431e6",
"metadata": {},
"outputs": [],
"source": [
"# 1. First get the file paths again to access the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data from the matrix_file\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 3. Print the first 20 row IDs (gene or probe identifiers) for future observation\n",
"print(\"First 20 gene/probe identifiers:\")\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "36c762f7",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b0919f3",
"metadata": {},
"outputs": [],
"source": [
"# These identifiers appear to be probe IDs from a microarray platform rather than standard human gene symbols.\n",
"# They follow a pattern of numbers followed by \"_at\" which is typical of microarray probe IDs\n",
"# (like Affymetrix arrays).\n",
"# These would need to be mapped to gene symbols for standardized analysis.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "0c678032",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ec80ab35",
"metadata": {},
"outputs": [],
"source": [
"# 1. First get the file paths using geo_get_relevant_filepaths function\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 3. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "d4b096d8",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a7eaa21",
"metadata": {},
"outputs": [],
"source": [
"Analyzing the current step and previous outputs, I'll correct the gene identifier mapping code to address the format mismatch:\n",
"\n",
"```python\n",
"# 1. First examine the structure of the SOFT file more thoroughly to find gene symbols\n",
"# Re-read the SOFT file to look for gene symbol information\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Let's read a portion of the SOFT file to understand its structure better\n",
"import gzip\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" # Read first 100 lines to identify the structure\n",
" lines = [next(f).strip() for _ in range(100) if f]\n",
"\n",
"# Look for lines that might contain gene symbol information\n",
"gene_symbol_lines = [line for line in lines if \"symbol\" in line.lower() or \"gene_symbol\" in line.lower()]\n",
"print(\"Sample lines containing gene symbol information:\")\n",
"for i, line in enumerate(gene_symbol_lines[:5]):\n",
" print(f\"{i}: {line}\")\n",
"\n",
"# Examine the structure of the gene expression data more closely\n",
"print(\"\\nStructure of gene expression data:\")\n",
"print(f\"Gene data shape: {gene_data.shape}\")\n",
"print(f\"Gene data columns (first 5): {list(gene_data.columns)[:5]}\")\n",
"print(f\"Gene data index format (first 5): {list(gene_data.index[:5])}\")\n",
"\n",
"# Let's try a different approach - use platform information from the SOFT file\n",
"# Read platform information to find probe-to-gene mapping\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" platform_id = None\n",
" for line in f:\n",
" if line.startswith('^PLATFORM'):\n",
" platform_id = line.strip().split('=')[1]\n",
" break\n",
"\n",
"print(f\"\\nPlatform ID: {platform_id}\")\n",
"\n",
"# Instead of relying on the limited annotation, let's try to extract gene symbols from the SOFT file\n",
"# Read the platform details to find gene symbol mappings\n",
"probe_gene_dict = {}\n",
"gene_symbol_column = None\n",
"probe_id_column = None\n",
"current_section = None\n",
"\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" for line in f:\n",
" if line.startswith('!platform_table_begin'):\n",
" current_section = 'platform_table'\n",
" # Read the header line to find relevant columns\n",
" header_line = next(f).strip()\n",
" headers = header_line.split('\\t')\n",
" \n",
" # Find columns for probe ID and gene symbol\n",
" for i, header in enumerate(headers):\n",
" if header.lower() in ['id', 'id_ref', 'probe_id', 'probeid']:\n",
" probe_id_column = i\n",
" if header.lower() in ['gene_symbol', 'symbol', 'genesymbol']:\n",
" gene_symbol_column = i\n",
" \n",
" if probe_id_column is not None and gene_symbol_column is not None:\n",
" print(f\"Found probe ID column ({headers[probe_id_column]}) and gene symbol column ({headers[gene_symbol_column]})\")\n",
" # Read the mapping\n",
" for data_line in f:\n",
" if data_line.startswith('!platform_table_end'):\n",
" break\n",
" fields = data_line.strip().split('\\t')\n",
" if len(fields) > max(probe_id_column, gene_symbol_column):\n",
" probe_id = fields[probe_id_column]\n",
" gene_symbol = fields[gene_symbol_column]\n",
" if gene_symbol: # Only add if gene symbol is not empty\n",
" probe_gene_dict[probe_id] = gene_symbol\n",
" break\n",
"\n",
"# If we found mappings, create a mapping DataFrame\n",
"if probe_gene_dict:\n",
" print(f\"\\nFound {len(probe_gene_dict)} probe-to-gene mappings\")\n",
" # Create mapping DataFrame\n",
" mapping_data = pd.DataFrame({\n",
" 'ID': list(probe_gene_dict.keys()),\n",
" 'Gene': list(probe_gene_dict.values())\n",
" })\n",
" print(\"Mapping dataframe preview:\")\n",
" print(preview_df(mapping_data))\n",
" \n",
" # Apply gene mapping with the new mapping dataframe\n",
" try:\n",
" gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
" print(\"\\nGene expression data after mapping:\")\n",
" print(f\"Shape: {gene_data.shape}\")\n",
" print(preview_df(gene_data))\n",
" \n",
" # Normalize gene symbols\n",
" gene_data = normalize_gene_symbols_in_index(gene_data)\n",
" print(\"\\nGene expression data after normalization:\")\n",
" print(f\"Shape: {gene_data.shape}\")\n",
" print(preview_df(gene_data))\n",
" \n",
" # Save the 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\"\\nGene data saved to {out_gene_data_file}\")\n",
" except Exception as e:\n",
" print(f\"Error applying gene mapping: {e}\")\n",
"else:\n",
" # If we couldn't find the mapping in the SOFT file, try a fallback approach\n",
" print(\"\\nCouldn't find proper gene symbol mapping in the SOFT file.\")\n",
" print(\"Using Entrez Gene IDs instead...\")\n",
" \n",
" # Try to fix the format mismatch between gene_data index and gene_annotation ID\n",
" # Create a mapping between probe IDs in gene_data and gene annotation\n",
" gene_data_ids = set(gene_data.index)\n",
" annotation_ids = set(gene_annotation['ID'])\n",
" \n",
" # Check for any exact matches\n",
" common_ids = gene_data_ids.intersection(annotation_ids)\n",
" print(f\"Number of exact ID matches: {len(common_ids)}\")\n",
" \n",
" # If few exact matches, try to match by removing suffixes\n",
" if len(common_ids) < 100:\n",
" print(\"Trying to match IDs by removing suffixes...\")\n",
" # Create a mapping that ignores suffixes like '_at'\n",
" cleaned_gene_data_ids = {id.split('_')[0]: id for id in gene_data_ids}\n",
" cleaned_annotation_ids = {id.split('_')[0]: id for id in annotation_ids}\n",
" \n",
" # Find common base IDs\n",
" common_base_ids = set(cleaned_gene_data_ids.keys()).intersection(set(cleaned_annotation_ids.keys()))\n",
" print(f\"Number of matches after removing suffixes: {len(common_base_ids)}\")\n",
" \n",
" # Create a mapping from gene_data IDs to annotation IDs\n",
" id_mapping = {cleaned_gene_data_ids[base_id]: cleaned_annotation_ids[base_id] \n",
" for base_id in common_base_ids if base_id in cleaned_gene_data_ids and base_id in cleaned_annotation_ids}\n",
" \n",
" if id_mapping:\n",
" # Create a new mapping dataframe based on this ID mapping\n",
" mapping_rows = []\n",
" for gene_data_id, annotation_id in id_mapping.items():\n",
" gene_symbol = gene_annotation.loc[gene_annotation['ID'] == annotation_id, 'ENTREZ_GENE_ID'].values\n",
" if len(gene_symbol) > 0:\n",
" mapping_rows.append({'ID': gene_data_id, 'Gene': gene_symbol[0]})\n",
" \n",
" if mapping_rows:\n",
" mapping_df = pd.DataFrame(mapping_rows)\n",
" print(\"\\nCreated mapping dataframe with fixed ID format:\")\n",
" print(preview_df(mapping_df))\n",
" \n",
" # Apply gene mapping with the fixed mapping dataframe\n",
" gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
" print(\"\\nGene expression data after mapping:\")\n",
" print(f\"Shape: {gene_data.shape}\")\n",
" print(preview_df(gene_data))\n",
" \n",
" # Normalize gene symbols\n",
" gene_data = normalize_gene_symbols_in_index(gene_data)\n",
" print(\"\\nGene expression data after normalization:\")\n",
" print(f\"Shape: {gene_data.shape}\")\n",
" print(preview_df(gene_data))\n",
" \n",
" # Save the gene data\n",
" os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
" gene_data.\n"
]
},
{
"cell_type": "markdown",
"id": "af0aa29a",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1b1f1bc",
"metadata": {},
"outputs": [],
"source": [
"# 1. Reload necessary data\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"gene_data = get_genetic_data(matrix_file)\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Analyze gene identifiers in gene expression data and gene annotation data\n",
"print(\"Gene expression data - first 10 probe IDs:\")\n",
"print(list(gene_data.index[:10]))\n",
"print(\"Gene annotation data - first 10 probe IDs:\")\n",
"print(list(gene_annotation['ID'][:10]))\n",
"\n",
"# Check if there's any direct overlap between the two sets of IDs\n",
"gene_data_ids = set(gene_data.index)\n",
"annotation_ids = set(gene_annotation['ID'])\n",
"common_ids = gene_data_ids.intersection(annotation_ids)\n",
"print(f\"Number of directly matching IDs: {len(common_ids)}\")\n",
"\n",
"# Try to extract the platform information from the SOFT file\n",
"platform_info = {}\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" for line in f:\n",
" line = line.strip()\n",
" if line.startswith(\"!Platform_title\"):\n",
" platform_info['title'] = line.split(\"=\", 1)[1].strip().strip('\"')\n",
" elif line.startswith(\"!Platform_geo_accession\"):\n",
" platform_info['accession'] = line.split(\"=\", 1)[1].strip().strip('\"')\n",
"\n",
"print(\"Platform information:\")\n",
"print(platform_info)\n",
"\n",
"# Create a mapping by cleaning probe IDs\n",
"def clean_probe_id(probe_id):\n",
" # Remove common suffixes\n",
" for suffix in ['_at', '_st', '_a_at', '_s_at', '_x_at']:\n",
" if probe_id.endswith(suffix):\n",
" return probe_id[:-len(suffix)]\n",
" return probe_id\n",
"\n",
"# Clean and map the IDs\n",
"cleaned_gene_data_ids = {clean_probe_id(id): id for id in gene_data_ids}\n",
"cleaned_annotation_ids = {clean_probe_id(id): id for id in annotation_ids}\n",
"\n",
"# Find potential matches based on cleaned IDs\n",
"potential_matches = {}\n",
"for clean_id, orig_id in cleaned_gene_data_ids.items():\n",
" if clean_id in cleaned_annotation_ids:\n",
" potential_matches[orig_id] = cleaned_annotation_ids[clean_id]\n",
"\n",
"print(f\"Found {len(potential_matches)} potential matches after cleaning IDs\")\n",
"\n",
"# Try numeric matching if needed\n",
"if len(potential_matches) < 100:\n",
" def extract_numeric(probe_id):\n",
" import re\n",
" match = re.search(r'(\\d+)', probe_id)\n",
" if match:\n",
" return match.group(1)\n",
" return None\n",
"\n",
" numeric_gene_data_ids = {extract_numeric(id): id for id in gene_data_ids if extract_numeric(id)}\n",
" numeric_annotation_ids = {extract_numeric(id): id for id in annotation_ids if extract_numeric(id)}\n",
" \n",
" numeric_matches = {}\n",
" for num_id, orig_id in numeric_gene_data_ids.items():\n",
" if num_id in numeric_annotation_ids:\n",
" numeric_matches[orig_id] = numeric_annotation_ids[num_id]\n",
" \n",
" print(f\"Found {len(numeric_matches)} matches based on numeric part\")\n",
" \n",
" if len(numeric_matches) > len(potential_matches):\n",
" potential_matches = numeric_matches\n",
"\n",
"# Create a mapping dataframe\n",
"if potential_matches:\n",
" mapping_rows = []\n",
" for gene_data_id, annotation_id in potential_matches.items():\n",
" gene_symbols = gene_annotation.loc[gene_annotation['ID'] == annotation_id, 'ENTREZ_GENE_ID']\n",
" if not gene_symbols.empty:\n",
" mapping_rows.append({'ID': gene_data_id, 'Gene': gene_symbols.iloc[0]})\n",
" \n",
" mapping_df = pd.DataFrame(mapping_rows)\n",
" print(\"Created custom mapping dataframe. Preview:\")\n",
" print(preview_df(mapping_df))\n",
"else:\n",
" # Fallback to original mapping\n",
" mapping_df = get_gene_mapping(gene_annotation, 'ID', 'ENTREZ_GENE_ID')\n",
" print(\"Using original mapping dataframe. Preview:\")\n",
" print(preview_df(mapping_df))\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"try:\n",
" gene_data_mapped = apply_gene_mapping(gene_data, mapping_df)\n",
" print(\"Gene mapping applied. New gene data shape:\", gene_data_mapped.shape)\n",
" print(\"Gene data preview after mapping:\")\n",
" print(preview_df(gene_data_mapped))\n",
" \n",
" # If mapping produced results, use it\n",
" if gene_data_mapped.shape[0] > 0:\n",
" gene_data = gene_data_mapped\n",
" else:\n",
" # Use a direct approach if mapping failed\n",
" print(\"Mapping resulted in empty dataframe. Using a different approach...\")\n",
" simple_mapping = pd.DataFrame({\n",
" 'ID': gene_data.index,\n",
" 'Gene': [str(idx).split('_')[0] for idx in gene_data.index]\n",
" })\n",
" gene_data = apply_gene_mapping(gene_data, simple_mapping)\n",
" print(\"Alternative mapping applied. New gene data shape:\", gene_data.shape)\n",
"except Exception as e:\n",
" print(f\"Error during gene mapping: {e}\")\n",
" # Fallback to a simpler approach\n",
" simple_mapping = pd.DataFrame({\n",
" 'ID': gene_data.index,\n",
" 'Gene': [str(idx).split('_')[0] for idx in gene_data.index]\n",
" })\n",
" gene_data = apply_gene_mapping(gene_data, simple_mapping)\n",
" print(\"Fallback mapping applied. New gene data shape:\", gene_data.shape)\n",
"\n",
"# 4. Normalize gene symbols to ensure consistency\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(\"Gene symbols normalized. Final gene data shape:\", gene_data.shape)\n",
"print(\"Gene data preview after normalization:\")\n",
"print(preview_df(gene_data))\n",
"\n",
"# 5. Save the processed gene data to a file\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 data saved to {out_gene_data_file}\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|