File size: 25,124 Bytes
53eb596 |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "05a8536f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:54:33.654831Z",
"iopub.status.busy": "2025-03-25T07:54:33.654673Z",
"iopub.status.idle": "2025-03-25T07:54:33.820347Z",
"shell.execute_reply": "2025-03-25T07:54:33.820024Z"
}
},
"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 = \"Lupus_(Systemic_Lupus_Erythematosus)\"\n",
"cohort = \"GSE112943\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Lupus_(Systemic_Lupus_Erythematosus)\"\n",
"in_cohort_dir = \"../../input/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE112943\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/GSE112943.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE112943.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE112943.csv\"\n",
"json_path = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "1256620e",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ac43afb7",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:54:33.821799Z",
"iopub.status.busy": "2025-03-25T07:54:33.821652Z",
"iopub.status.idle": "2025-03-25T07:54:33.988480Z",
"shell.execute_reply": "2025-03-25T07:54:33.988070Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Genome-wide analysis of gene expression of cutaneous lupus skin samples and lupus nephritis kidney samples\"\n",
"!Series_summary\t\"Microarray gene expression analyses were performed on human skin samples from cutaneous lupus subtypes (SCLE and CCLE) and normal patients along with human kidney samples from lupus nephritis and normal patients\"\n",
"!Series_overall_design\t\"47 deidentified human samples from formalin fixed, paraffin-embedded skin (6 chronic cutaneous lupus, 10 subacute cutaneous lupus, 10 control skin) and formalin fixed paraffin-embedded kidney (14 lupus nephritis, 7 control kidney)\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: CCLE', 'tissue: SCLE', 'tissue: Skin Control', 'tissue: Kidney Control', 'tissue: Lupus Nephritis']}\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": "83c27e7e",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fbbb9d5e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:54:33.989802Z",
"iopub.status.busy": "2025-03-25T07:54:33.989687Z",
"iopub.status.idle": "2025-03-25T07:54:33.998075Z",
"shell.execute_reply": "2025-03-25T07:54:33.997762Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical Data Preview:\n",
"{'GSM3091745': [1.0], 'GSM3091746': [1.0], 'GSM3091747': [1.0], 'GSM3091748': [1.0], 'GSM3091749': [1.0], 'GSM3091750': [1.0], 'GSM3091751': [1.0], 'GSM3091752': [1.0], 'GSM3091753': [1.0], 'GSM3091754': [1.0], 'GSM3091755': [1.0], 'GSM3091756': [1.0], 'GSM3091757': [1.0], 'GSM3091758': [1.0], 'GSM3091759': [1.0], 'GSM3091760': [1.0], 'GSM3091765': [0.0], 'GSM3091766': [0.0], 'GSM3091767': [0.0], 'GSM3091768': [0.0], 'GSM3091769': [0.0], 'GSM3091770': [0.0], 'GSM3091771': [0.0], 'GSM3091772': [0.0], 'GSM3091773': [0.0], 'GSM3091774': [0.0], 'GSM3091775': [0.0], 'GSM3091776': [0.0], 'GSM3091777': [0.0], 'GSM3091778': [0.0], 'GSM3091779': [0.0], 'GSM3091780': [0.0], 'GSM3091781': [0.0], 'GSM3091782': [1.0], 'GSM3091783': [1.0], 'GSM3091784': [1.0], 'GSM3091785': [1.0], 'GSM3091786': [1.0], 'GSM3091787': [1.0], 'GSM3091788': [1.0], 'GSM3091789': [1.0], 'GSM3091790': [1.0], 'GSM3091791': [1.0], 'GSM3091792': [1.0], 'GSM3091793': [1.0], 'GSM3091794': [1.0], 'GSM3091795': [1.0]}\n",
"Clinical data saved to ../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE112943.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains gene expression data from microarray analysis\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# Looking at the sample characteristics dictionary, row 0 contains tissue information\n",
"# This can be used to determine lupus status (trait)\n",
"trait_row = 0\n",
"# Age and gender information are not available in the sample characteristics\n",
"age_row = None\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value_str):\n",
" \"\"\"Convert tissue information to binary trait (Lupus vs Control)\"\"\"\n",
" if not isinstance(value_str, str):\n",
" return None\n",
" \n",
" if ':' in value_str:\n",
" value = value_str.split(':', 1)[1].strip()\n",
" else:\n",
" value = value_str.strip()\n",
" \n",
" # CCLE = Chronic Cutaneous Lupus Erythematosus\n",
" # SCLE = Subacute Cutaneous Lupus Erythematosus\n",
" # Lupus Nephritis is also a form of lupus\n",
" if 'CCLE' in value or 'SCLE' in value or 'Lupus Nephritis' in value:\n",
" return 1 # Lupus\n",
" elif 'Control' in value:\n",
" return 0 # Control\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value_str):\n",
" \"\"\"Convert age string to numeric value (not used in this dataset)\"\"\"\n",
" return None # Age data not available\n",
"\n",
"def convert_gender(value_str):\n",
" \"\"\"Convert gender string to binary (not used in this dataset)\"\"\"\n",
" return None # Gender data not available\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",
"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",
" # Use the function to extract clinical features\n",
" try:\n",
" # Assuming clinical_data is already available from previous step\n",
" # If clinical_data is not available or loaded, we need to handle it\n",
" if 'clinical_data' in locals() or 'clinical_data' in globals():\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 extracted clinical features\n",
" preview = preview_df(selected_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 to CSV\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(\"Clinical data not available from previous step\")\n",
" except Exception as e:\n",
" print(f\"Error in clinical feature extraction: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "8858dc02",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bb71d593",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:54:33.999070Z",
"iopub.status.busy": "2025-03-25T07:54:33.998958Z",
"iopub.status.idle": "2025-03-25T07:54:34.266577Z",
"shell.execute_reply": "2025-03-25T07:54:34.266187Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\n",
"No subseries references found in the first 1000 lines of the SOFT file.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene data extraction result:\n",
"Number of rows: 47303\n",
"First 20 gene/probe identifiers:\n",
"Index(['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',\n",
" 'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',\n",
" 'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',\n",
" 'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',\n",
" 'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. First get the path to the soft and matrix files\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Looking more carefully at the background information\n",
"# This is a SuperSeries which doesn't contain direct gene expression data\n",
"# Need to investigate the soft file to find the subseries\n",
"print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n",
"\n",
"# Open the SOFT file to try to identify subseries\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" subseries_lines = []\n",
" for i, line in enumerate(f):\n",
" if 'Series_relation' in line and 'SuperSeries of' in line:\n",
" subseries_lines.append(line.strip())\n",
" if i > 1000: # Limit search to first 1000 lines\n",
" break\n",
"\n",
"# Display the subseries found\n",
"if subseries_lines:\n",
" print(\"Found potential subseries references:\")\n",
" for line in subseries_lines:\n",
" print(line)\n",
"else:\n",
" print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n",
"\n",
"# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(\"\\nGene data extraction result:\")\n",
" print(\"Number of rows:\", len(gene_data))\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n",
" print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")\n"
]
},
{
"cell_type": "markdown",
"id": "cdda2f72",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c0f4621f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:54:34.267923Z",
"iopub.status.busy": "2025-03-25T07:54:34.267803Z",
"iopub.status.idle": "2025-03-25T07:54:34.269759Z",
"shell.execute_reply": "2025-03-25T07:54:34.269472Z"
}
},
"outputs": [],
"source": [
"# Review of gene identifiers in the gene expression data\n",
"# The identifiers start with \"ILMN_\" which indicates they're Illumina BeadChip probe IDs\n",
"# These are microarray probe identifiers, not human gene symbols\n",
"# They need to be mapped to official gene symbols for better interpretability and cross-platform analysis\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "004eaed5",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9d0d2afd",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:54:34.270881Z",
"iopub.status.busy": "2025-03-25T07:54:34.270769Z",
"iopub.status.idle": "2025-03-25T07:54:40.022748Z",
"shell.execute_reply": "2025-03-25T07:54:40.022343Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Species': [nan, nan, nan, nan, nan], 'Source': [nan, nan, nan, nan, nan], 'Search_Key': [nan, nan, nan, nan, nan], 'Transcript': [nan, nan, nan, nan, nan], 'ILMN_Gene': [nan, nan, nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan, nan, nan], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Unigene_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': [nan, nan, nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB'], 'Protein_Product': [nan, nan, nan, nan, 'thrB'], 'Probe_Id': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5090180.0, 6510136.0, 7560739.0, 1450438.0, 1240647.0], 'Probe_Type': [nan, nan, nan, nan, nan], 'Probe_Start': [nan, nan, nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT', 'TCTGTCACTGTCAGGAAAGTGGTAAAACTGCAACTCAATTACTGCAATGC', 'CTTGTGCCTGAGCTGTCAAAAGTAGAGCACGTCGCCGAGATGAAGGGCGC'], 'Chromosome': [nan, nan, nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan, nan, nan], 'Cytoband': [nan, nan, nan, nan, nan], 'Definition': [nan, nan, nan, nan, nan], 'Ontology_Component': [nan, nan, nan, nan, nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, nan, nan], 'Synonyms': [nan, nan, nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan, nan, nan], 'GB_ACC': [nan, nan, nan, nan, nan]}\n"
]
}
],
"source": [
"# 1. 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",
"# 2. 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": "bfd5e378",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "62e857df",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:54:40.024062Z",
"iopub.status.busy": "2025-03-25T07:54:40.023924Z",
"iopub.status.idle": "2025-03-25T07:54:40.918389Z",
"shell.execute_reply": "2025-03-25T07:54:40.917985Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping preview:\n",
"{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Gene': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB']}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene-level expression data preview:\n",
"Shape: (20254, 47)\n",
"Number of genes: 20254\n",
"First few gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data saved to ../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE112943.csv\n"
]
}
],
"source": [
"# 1. After observing the gene annotation data and gene expression data:\n",
"# - The gene identifiers in gene expression data start with \"ILMN_\"\n",
"# - In the annotation data, these are stored in the 'ID' column\n",
"# - The gene symbols are stored in the 'Symbol' column\n",
"\n",
"# 2. Create a gene mapping dataframe with the probe ID and gene symbol columns\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
"\n",
"# Let's examine the mapping dataframe\n",
"print(\"Gene mapping preview:\")\n",
"print(preview_df(gene_mapping))\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Normalize gene symbols (handle synonyms)\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"\n",
"# Preview the converted gene expression data\n",
"print(\"\\nGene-level expression data preview:\")\n",
"print(f\"Shape: {gene_data.shape}\")\n",
"print(f\"Number of genes: {len(gene_data)}\")\n",
"print(f\"First few gene symbols: {list(gene_data.index[:10])}\")\n",
"\n",
"# Save the gene data to the specified output 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}\")\n"
]
},
{
"cell_type": "markdown",
"id": "9b9cee93",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "65aadb77",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:54:40.919810Z",
"iopub.status.busy": "2025-03-25T07:54:40.919681Z",
"iopub.status.idle": "2025-03-25T07:54:51.591760Z",
"shell.execute_reply": "2025-03-25T07:54:51.591333Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 10 gene indices before normalization: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS']\n",
"Top 10 gene indices after normalization: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS']\n",
"Shape of normalized gene data: (20254, 47)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved normalized gene data to ../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE112943.csv\n",
"Loaded clinical data with shape: (1, 47)\n",
"Shape of linked data: (47, 20255)\n",
"Column names in linked data: [0, 'A1BG', 'A1BG-AS1', 'A1CF', 'A2M']\n",
"Using '0' as the trait column for handling missing values\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of linked data after handling missing values: (47, 20255)\n",
"For the feature '0', the least common label is '0.0' with 17 occurrences. This represents 36.17% of the dataset.\n",
"The distribution of the feature '0' in this dataset is fine.\n",
"\n",
"A new JSON file was created at: ../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved processed linked data to ../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/GSE112943.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"print(f\"Top 10 gene indices before normalization: {gene_data.index[:10].tolist()}\")\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Top 10 gene indices after normalization: {normalized_gene_data.index[:10].tolist()}\")\n",
"print(f\"Shape of normalized gene data: {normalized_gene_data.shape}\")\n",
"\n",
"# Create directory for gene data file if it doesn't exist\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"# Save the normalized gene data\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Saved normalized gene data to {out_gene_data_file}\")\n",
"\n",
"# 2. Load the clinical data \n",
"# Load the clinical data that was already processed in step 2\n",
"selected_clinical_df = pd.read_csv(out_clinical_data_file)\n",
"print(f\"Loaded clinical data with shape: {selected_clinical_df.shape}\")\n",
"\n",
"# 3. Link clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
"print(f\"Shape of linked data: {linked_data.shape}\")\n",
"\n",
"# Inspect the column names to find the trait column name\n",
"print(f\"Column names in linked data: {linked_data.columns[:5].tolist()}\")\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"# Since we're dealing with a trait that was saved from a previous step,\n",
"# we need to find the actual column name used for the trait in the linked data\n",
"# The first column (index 0) of clinical data should be our trait\n",
"trait_col = linked_data.columns[0]\n",
"print(f\"Using '{trait_col}' as the trait column for handling missing values\")\n",
"linked_data = handle_missing_values(linked_data, trait_col)\n",
"print(f\"Shape of linked data after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 5. Determine if the trait and demographic features are biased\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait_col)\n",
"\n",
"# 6. Validate the dataset and save cohort information\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=True,\n",
" is_biased=is_trait_biased,\n",
" df=unbiased_linked_data,\n",
" note=\"Dataset contains gene expression data from skin and kidney samples of lupus patients and controls.\"\n",
")\n",
"\n",
"# 7. 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",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Saved processed linked data to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset validation failed. Final linked data not saved.\")"
]
}
],
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|