File size: 36,019 Bytes
736e4a0 |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "1e249621",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:54.571183Z",
"iopub.status.busy": "2025-03-25T06:52:54.571075Z",
"iopub.status.idle": "2025-03-25T06:52:54.734208Z",
"shell.execute_reply": "2025-03-25T06:52:54.733865Z"
}
},
"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 = \"Autism_spectrum_disorder_(ASD)\"\n",
"cohort = \"GSE57802\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Autism_spectrum_disorder_(ASD)\"\n",
"in_cohort_dir = \"../../input/GEO/Autism_spectrum_disorder_(ASD)/GSE57802\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/GSE57802.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE57802.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE57802.csv\"\n",
"json_path = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "4a785e43",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b06f2a9d",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:54.735622Z",
"iopub.status.busy": "2025-03-25T06:52:54.735481Z",
"iopub.status.idle": "2025-03-25T06:52:54.942564Z",
"shell.execute_reply": "2025-03-25T06:52:54.942207Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Transcriptome Profiling of patients with 16p11.2 rearrangements\"\n",
"!Series_summary\t\"The 600kb BP4-BP5 16p11.2 CNV (copy number variant) is associated with neuroanatomical, neurocognitive and metabolic disorders. These recurrent rearrangements are associated with reciprocal phenotypes such as obesity and underweight, macro- and microcephaly, as well as autism spectrum disorder (ASD) and schizophrenia. Here we interrogated the transcriptome of individuals carrying reciprocal CNVs in 16p11.2.\"\n",
"!Series_summary\t\"The genome-wide transcript perturbations correlated with clinical endophenotypes of the CNV and were enriched for genes associated with ASD. We uncovered a significant correlation between copy number changes and expression levels of genes mutated in ciliopathies.\"\n",
"!Series_overall_design\t\"Transcriptome profiles of lymphoblastoid cell lines of 50 16p11.2 deletion carriers, 31 16p11.2 duplication carriers and 17 controls.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['cell type: lymphoblastoid'], 1: ['gender: M', 'gender: F'], 2: ['age: 46', 'age: 33', 'age: NA', 'age: 22', 'age: 52', 'age: 25', 'age: 31', 'age: 60', 'age: 40', 'age: 50', 'age: 51', 'age: 39', 'age: 6', 'age: 56', 'age: 16', 'age: 41', 'age: 35', 'age: 4', 'age: 10', 'age: 12', 'age: 7', 'age: 1.4', 'age: 38', 'age: 14.7', 'age: 11', 'age: 12.8', 'age: 11.9', 'age: 7.7', 'age: 3.3', 'age: 1.5'], 3: ['copy number 16p11.2: 2', 'copy number 16p11.2: 1', 'copy number 16p11.2: 3'], 4: ['genotype: Control', 'genotype: 600kbdel', 'genotype: 600kbdup'], 5: ['family identifier: 201', 'family identifier: 202', 'family identifier: 203', 'family identifier: 204', 'family identifier: 205', 'family identifier: 206', 'family identifier: 207', 'family identifier: 208', 'family identifier: 209', 'family identifier: 210', 'family identifier: 211', 'family identifier: 212', 'family identifier: 213', 'family identifier: 84', 'family identifier: 63', 'family identifier: 1', 'family identifier: 4', 'family identifier: 5', 'family identifier: 8', 'family identifier: 11', 'family identifier: 12', 'family identifier: 13', 'family identifier: 14', 'family identifier: 15', 'family identifier: 17', 'family identifier: 20', 'family identifier: 23', 'family identifier: 24', 'family identifier: 26', 'family identifier: 28'], 6: ['kinship: unrelated', 'kinship: father', 'kinship: sibling', 'kinship: mother', 'kinship: proband', 'kinship: pat grandfather']}\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": "e7dc37fe",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "530ea6ea",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:54.943794Z",
"iopub.status.busy": "2025-03-25T06:52:54.943691Z",
"iopub.status.idle": "2025-03-25T06:52:54.958510Z",
"shell.execute_reply": "2025-03-25T06:52:54.958225Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical Data Preview:\n",
"{'GSM1389621': [0.0, 46.0, 1.0], 'GSM1389622': [0.0, 33.0, 0.0], 'GSM1389623': [0.0, nan, 1.0], 'GSM1389624': [0.0, nan, 0.0], 'GSM1389625': [0.0, 22.0, 1.0], 'GSM1389626': [0.0, 52.0, 1.0], 'GSM1389627': [0.0, 25.0, 1.0], 'GSM1389628': [0.0, 31.0, 0.0], 'GSM1389629': [0.0, 60.0, 1.0], 'GSM1389630': [0.0, nan, 1.0], 'GSM1389631': [0.0, 40.0, 1.0], 'GSM1389632': [0.0, 50.0, 1.0], 'GSM1389633': [0.0, 51.0, 1.0], 'GSM1389634': [0.0, 39.0, 1.0], 'GSM1389635': [0.0, 6.0, 1.0], 'GSM1389636': [0.0, 51.0, 1.0], 'GSM1389637': [0.0, 56.0, 0.0], 'GSM1389638': [1.0, 16.0, 1.0], 'GSM1389639': [1.0, 41.0, 1.0], 'GSM1389640': [1.0, 31.0, 0.0], 'GSM1389641': [1.0, 35.0, 1.0], 'GSM1389642': [1.0, 4.0, 1.0], 'GSM1389643': [1.0, 10.0, 0.0], 'GSM1389644': [1.0, 12.0, 0.0], 'GSM1389645': [1.0, 7.0, 1.0], 'GSM1389646': [1.0, 6.0, 1.0], 'GSM1389647': [1.0, 1.4, 1.0], 'GSM1389648': [1.0, 10.0, 0.0], 'GSM1389649': [1.0, 6.0, 1.0], 'GSM1389650': [1.0, 38.0, 1.0], 'GSM1389651': [1.0, 14.7, 1.0], 'GSM1389652': [1.0, 11.0, 0.0], 'GSM1389653': [1.0, 7.0, 0.0], 'GSM1389654': [1.0, 12.8, 1.0], 'GSM1389655': [1.0, 11.9, 0.0], 'GSM1389656': [1.0, 7.7, 0.0], 'GSM1389657': [1.0, 3.3, 1.0], 'GSM1389658': [1.0, 1.5, 1.0], 'GSM1389659': [1.0, 16.0, 1.0], 'GSM1389660': [1.0, 40.0, 0.0], 'GSM1389661': [1.0, 39.0, 0.0], 'GSM1389662': [1.0, 12.0, 1.0], 'GSM1389663': [1.0, 5.9, 1.0], 'GSM1389664': [1.0, 4.1, 0.0], 'GSM1389665': [1.0, 5.2, 1.0], 'GSM1389666': [1.0, 9.0, 1.0], 'GSM1389667': [1.0, 37.0, 1.0], 'GSM1389668': [1.0, 14.8, 1.0], 'GSM1389669': [1.0, 15.0, 1.0], 'GSM1389670': [1.0, 5.7, 1.0], 'GSM1389671': [1.0, 23.0, 1.0], 'GSM1389672': [1.0, 6.8, 1.0], 'GSM1389673': [1.0, 53.0, 1.0], 'GSM1389674': [1.0, 8.8, 1.0], 'GSM1389675': [1.0, 6.8, 1.0], 'GSM1389676': [1.0, 26.0, 0.0], 'GSM1389677': [1.0, 21.0, 1.0], 'GSM1389678': [1.0, 13.0, 1.0], 'GSM1389679': [1.0, 12.0, 0.0], 'GSM1389680': [1.0, 21.0, 0.0], 'GSM1389681': [1.0, 10.0, 1.0], 'GSM1389682': [1.0, 15.0, 0.0], 'GSM1389683': [1.0, 11.0, 1.0], 'GSM1389684': [1.0, 5.5, 1.0], 'GSM1389685': [1.0, 3.7, 1.0], 'GSM1389686': [1.0, 4.0, 1.0], 'GSM1389687': [1.0, 7.0, 0.0], 'GSM1389688': [1.0, 5.0, 1.0], 'GSM1389689': [1.0, 5.0, 0.0], 'GSM1389690': [1.0, 42.0, 0.0], 'GSM1389691': [1.0, 42.0, 0.0], 'GSM1389692': [1.0, 5.0, 1.0], 'GSM1389693': [1.0, 8.0, 0.0], 'GSM1389694': [1.0, 15.0, 0.0], 'GSM1389695': [1.0, 3.4, 0.0], 'GSM1389696': [1.0, 44.0, 0.0], 'GSM1389697': [1.0, 16.0, 0.0], 'GSM1389698': [1.0, 52.0, 0.0], 'GSM1389699': [1.0, 28.0, 0.0], 'GSM1389700': [1.0, 0.6, 1.0], 'GSM1389701': [1.0, 14.0, 0.0], 'GSM1389702': [1.0, 1.8, 0.0], 'GSM1389703': [1.0, 40.0, 1.0], 'GSM1389704': [1.0, 9.0, 1.0], 'GSM1389705': [1.0, 5.2, 0.0], 'GSM1389706': [1.0, 5.5, 1.0], 'GSM1389707': [1.0, 28.0, 0.0], 'GSM1389708': [1.0, 42.0, 1.0], 'GSM1389709': [1.0, 12.8, 0.0], 'GSM1389710': [1.0, 36.0, 0.0], 'GSM1389711': [1.0, 3.0, 0.0], 'GSM1389712': [1.0, 41.0, 0.0], 'GSM1389713': [1.0, 6.0, 1.0], 'GSM1389714': [1.0, 76.0, 1.0], 'GSM1389715': [1.0, 47.0, 1.0], 'GSM1389716': [1.0, 44.0, 0.0], 'GSM1389717': [1.0, 3.0, 0.0], 'GSM1389718': [1.0, 34.0, 0.0], 'GSM1389719': [1.0, 11.0, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE57802.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"is_gene_available = True # Transcriptome profiling implies gene expression data is available\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# Note from background info that the dataset is about 16p11.2 CNV carriers with some having ASD\n",
"\n",
"# 2.1 Data Availability\n",
"# For trait (ASD), we don't have a direct row, but we can use the genotype information\n",
"trait_row = 4 # 'genotype: Control', 'genotype: 600kbdel', 'genotype: 600kbdup'\n",
"age_row = 2 # 'age: X' values\n",
"gender_row = 1 # 'gender: M', 'gender: F'\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert genotype information to binary trait values.\n",
" Based on the background information, 16p11.2 deletions and duplications are associated with ASD.\n",
" - genotype: Control (0) - control subjects\n",
" - genotype: 600kbdel (1) - deletion carriers, associated with ASD\n",
" - genotype: 600kbdup (1) - duplication carriers, associated with ASD\n",
" \"\"\"\n",
" if not value or ':' not in value:\n",
" return None\n",
" genotype = value.split(':', 1)[1].strip().lower()\n",
" if 'control' in genotype:\n",
" return 0 # Control\n",
" elif '600kbdel' in genotype or '600kbdup' in genotype:\n",
" return 1 # CNV carriers (associated with ASD)\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age values to continuous numeric values.\"\"\"\n",
" if not value or ':' not in value:\n",
" return None\n",
" age_str = value.split(':', 1)[1].strip()\n",
" if age_str.lower() == 'na':\n",
" return None\n",
" try:\n",
" return float(age_str)\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"\n",
" Convert gender values to binary:\n",
" - F (female) = 0\n",
" - M (male) = 1\n",
" \"\"\"\n",
" if not value or ':' not in value:\n",
" return None\n",
" gender = value.split(':', 1)[1].strip().upper()\n",
" if gender == 'F':\n",
" return 0\n",
" elif gender == 'M':\n",
" return 1\n",
" return None\n",
"\n",
"# 3. Save Metadata\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",
" # 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 extracted clinical data\n",
" preview = preview_df(clinical_df)\n",
" print(\"Clinical Data Preview:\")\n",
" print(preview)\n",
" \n",
" # Save clinical data to file\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": "02d2326a",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "670d9d77",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:54.959579Z",
"iopub.status.busy": "2025-03-25T06:52:54.959479Z",
"iopub.status.idle": "2025-03-25T06:52:55.309069Z",
"shell.execute_reply": "2025-03-25T06:52:55.308721Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['1007_PM_s_at', '1053_PM_at', '117_PM_at', '121_PM_at', '1255_PM_g_at',\n",
" '1294_PM_at', '1316_PM_at', '1320_PM_at', '1405_PM_i_at', '1431_PM_at',\n",
" '1438_PM_at', '1487_PM_at', '1494_PM_f_at', '1552256_PM_a_at',\n",
" '1552257_PM_a_at', '1552258_PM_at', '1552261_PM_at', '1552263_PM_at',\n",
" '1552264_PM_a_at', '1552266_PM_at'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "aa4aa5c4",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "866cd7bc",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:55.310367Z",
"iopub.status.busy": "2025-03-25T06:52:55.310246Z",
"iopub.status.idle": "2025-03-25T06:52:55.312154Z",
"shell.execute_reply": "2025-03-25T06:52:55.311861Z"
}
},
"outputs": [],
"source": [
"# Examining the gene identifiers in the expression data\n",
"# These appear to be Affymetrix probe IDs (with the \"PM\" format and \"_at\" suffixes)\n",
"# rather than standard human gene symbols like BRCA1, TP53, etc.\n",
"# Affymetrix probe IDs need to be mapped to gene symbols for biological interpretation\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "9652da19",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f3791951",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:55.313237Z",
"iopub.status.busy": "2025-03-25T06:52:55.313136Z",
"iopub.status.idle": "2025-03-25T06:53:01.689636Z",
"shell.execute_reply": "2025-03-25T06:53:01.689262Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['1007_PM_s_at', '1053_PM_at', '117_PM_at', '121_PM_at', '1255_PM_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Aug 20, 2010', 'Aug 20, 2010', 'Aug 20, 2010', 'Aug 20, 2010', 'Aug 20, 2010'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001954 /// NM_013993 /// NM_013994', 'NM_002914 /// NM_181471', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein amino acid phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0031100 // organ regeneration // inferred from electronic annotation /// 0043583 // ear development // inferred from electronic annotation /// 0043588 // skin development // inferred from electronic annotation /// 0051789 // response to protein stimulus // inferred from electronic annotation /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation', '0006260 // DNA replication // not recorded /// 0006260 // DNA replication // inferred from electronic annotation /// 0006297 // nucleotide-excision repair, DNA gap filling // not recorded /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation', '0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement', '0001656 // metanephros development // inferred from electronic annotation /// 0006350 // transcription // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from electronic annotation /// 0045449 // regulation of transcription // inferred from electronic annotation /// 0045893 // positive regulation of transcription, DNA-dependent // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-dependent // inferred from direct assay /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from electronic annotation', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007601 // visual perception // traceable author statement /// 0007602 // phototransduction // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from electronic annotation /// 0005887 // integral to plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral to membrane // inferred from electronic annotation /// 0016323 // basolateral plasma membrane // inferred from electronic annotation', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // not recorded /// 0005663 // DNA replication factor C complex // inferred from direct assay /// 0005663 // DNA replication factor C complex // inferred from electronic annotation', nan, '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005654 // nucleoplasm // inferred from electronic annotation', '0016020 // membrane // inferred from electronic annotation'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0004872 // receptor activity // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005515 // protein binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0003689 // DNA clamp loader activity // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0005524 // ATP binding // traceable author statement /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation', '0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from electronic annotation /// 0003700 // transcription factor activity // traceable author statement /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from sequence or structural similarity /// 0005515 // protein binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0016563 // transcription activator activity // inferred from sequence or structural similarity /// 0016563 // transcription activator activity // inferred from direct assay /// 0016563 // transcription activator activity // inferred from electronic annotation /// 0043565 // sequence-specific DNA binding // inferred from electronic annotation', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // traceable author statement /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation']}\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": "88ae873c",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e8d889dc",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:53:01.690943Z",
"iopub.status.busy": "2025-03-25T06:53:01.690821Z",
"iopub.status.idle": "2025-03-25T06:53:02.079957Z",
"shell.execute_reply": "2025-03-25T06:53:02.079638Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First 20 gene symbols after mapping:\n",
"Index(['A1BG', 'A1CF', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A4GALT', 'A4GNT',\n",
" 'AAA1', 'AAAS', 'AACS', 'AACSL', 'AADAC', 'AADACL2', 'AADAT', 'AAGAB',\n",
" 'AAK1', 'AAMP', 'AANAT', 'AARS'],\n",
" dtype='object', name='Gene')\n"
]
}
],
"source": [
"# 1. Identify which columns in the annotation data contain probe IDs and gene symbols\n",
"# From the preview, 'ID' contains probe IDs like '1007_PM_s_at' matching the expression data IDs\n",
"# 'Gene Symbol' contains standard gene symbols like 'DDR1'\n",
"\n",
"# 2. Get the gene mapping dataframe\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level data to gene-level data\n",
"gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)\n",
"\n",
"# Print the first few gene symbols after mapping to verify\n",
"print(\"First 20 gene symbols after mapping:\")\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "95773b7b",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5085ee68",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:53:02.081256Z",
"iopub.status.busy": "2025-03-25T06:53:02.081139Z",
"iopub.status.idle": "2025-03-25T06:53:09.557093Z",
"shell.execute_reply": "2025-03-25T06:53:09.556747Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{'GSM1389621': [0.0, 46.0, 1.0], 'GSM1389622': [0.0, 33.0, 0.0], 'GSM1389623': [0.0, nan, 1.0], 'GSM1389624': [0.0, nan, 0.0], 'GSM1389625': [0.0, 22.0, 1.0], 'GSM1389626': [0.0, 52.0, 1.0], 'GSM1389627': [0.0, 25.0, 1.0], 'GSM1389628': [0.0, 31.0, 0.0], 'GSM1389629': [0.0, 60.0, 1.0], 'GSM1389630': [0.0, nan, 1.0], 'GSM1389631': [0.0, 40.0, 1.0], 'GSM1389632': [0.0, 50.0, 1.0], 'GSM1389633': [0.0, 51.0, 1.0], 'GSM1389634': [0.0, 39.0, 1.0], 'GSM1389635': [0.0, 6.0, 1.0], 'GSM1389636': [0.0, 51.0, 1.0], 'GSM1389637': [0.0, 56.0, 0.0], 'GSM1389638': [1.0, 16.0, 1.0], 'GSM1389639': [1.0, 41.0, 1.0], 'GSM1389640': [1.0, 31.0, 0.0], 'GSM1389641': [1.0, 35.0, 1.0], 'GSM1389642': [1.0, 4.0, 1.0], 'GSM1389643': [1.0, 10.0, 0.0], 'GSM1389644': [1.0, 12.0, 0.0], 'GSM1389645': [1.0, 7.0, 1.0], 'GSM1389646': [1.0, 6.0, 1.0], 'GSM1389647': [1.0, 1.4, 1.0], 'GSM1389648': [1.0, 10.0, 0.0], 'GSM1389649': [1.0, 6.0, 1.0], 'GSM1389650': [1.0, 38.0, 1.0], 'GSM1389651': [1.0, 14.7, 1.0], 'GSM1389652': [1.0, 11.0, 0.0], 'GSM1389653': [1.0, 7.0, 0.0], 'GSM1389654': [1.0, 12.8, 1.0], 'GSM1389655': [1.0, 11.9, 0.0], 'GSM1389656': [1.0, 7.7, 0.0], 'GSM1389657': [1.0, 3.3, 1.0], 'GSM1389658': [1.0, 1.5, 1.0], 'GSM1389659': [1.0, 16.0, 1.0], 'GSM1389660': [1.0, 40.0, 0.0], 'GSM1389661': [1.0, 39.0, 0.0], 'GSM1389662': [1.0, 12.0, 1.0], 'GSM1389663': [1.0, 5.9, 1.0], 'GSM1389664': [1.0, 4.1, 0.0], 'GSM1389665': [1.0, 5.2, 1.0], 'GSM1389666': [1.0, 9.0, 1.0], 'GSM1389667': [1.0, 37.0, 1.0], 'GSM1389668': [1.0, 14.8, 1.0], 'GSM1389669': [1.0, 15.0, 1.0], 'GSM1389670': [1.0, 5.7, 1.0], 'GSM1389671': [1.0, 23.0, 1.0], 'GSM1389672': [1.0, 6.8, 1.0], 'GSM1389673': [1.0, 53.0, 1.0], 'GSM1389674': [1.0, 8.8, 1.0], 'GSM1389675': [1.0, 6.8, 1.0], 'GSM1389676': [1.0, 26.0, 0.0], 'GSM1389677': [1.0, 21.0, 1.0], 'GSM1389678': [1.0, 13.0, 1.0], 'GSM1389679': [1.0, 12.0, 0.0], 'GSM1389680': [1.0, 21.0, 0.0], 'GSM1389681': [1.0, 10.0, 1.0], 'GSM1389682': [1.0, 15.0, 0.0], 'GSM1389683': [1.0, 11.0, 1.0], 'GSM1389684': [1.0, 5.5, 1.0], 'GSM1389685': [1.0, 3.7, 1.0], 'GSM1389686': [1.0, 4.0, 1.0], 'GSM1389687': [1.0, 7.0, 0.0], 'GSM1389688': [1.0, 5.0, 1.0], 'GSM1389689': [1.0, 5.0, 0.0], 'GSM1389690': [1.0, 42.0, 0.0], 'GSM1389691': [1.0, 42.0, 0.0], 'GSM1389692': [1.0, 5.0, 1.0], 'GSM1389693': [1.0, 8.0, 0.0], 'GSM1389694': [1.0, 15.0, 0.0], 'GSM1389695': [1.0, 3.4, 0.0], 'GSM1389696': [1.0, 44.0, 0.0], 'GSM1389697': [1.0, 16.0, 0.0], 'GSM1389698': [1.0, 52.0, 0.0], 'GSM1389699': [1.0, 28.0, 0.0], 'GSM1389700': [1.0, 0.6, 1.0], 'GSM1389701': [1.0, 14.0, 0.0], 'GSM1389702': [1.0, 1.8, 0.0], 'GSM1389703': [1.0, 40.0, 1.0], 'GSM1389704': [1.0, 9.0, 1.0], 'GSM1389705': [1.0, 5.2, 0.0], 'GSM1389706': [1.0, 5.5, 1.0], 'GSM1389707': [1.0, 28.0, 0.0], 'GSM1389708': [1.0, 42.0, 1.0], 'GSM1389709': [1.0, 12.8, 0.0], 'GSM1389710': [1.0, 36.0, 0.0], 'GSM1389711': [1.0, 3.0, 0.0], 'GSM1389712': [1.0, 41.0, 0.0], 'GSM1389713': [1.0, 6.0, 1.0], 'GSM1389714': [1.0, 76.0, 1.0], 'GSM1389715': [1.0, 47.0, 1.0], 'GSM1389716': [1.0, 44.0, 0.0], 'GSM1389717': [1.0, 3.0, 0.0], 'GSM1389718': [1.0, 34.0, 0.0], 'GSM1389719': [1.0, 11.0, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE57802.csv\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE57802.csv\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"For the feature 'Autism_spectrum_disorder_(ASD)', the least common label is '0.0' with 17 occurrences. This represents 17.17% of the dataset.\n",
"Quartiles for 'Age':\n",
" 25%: 6.4\n",
" 50% (Median): 14.7\n",
" 75%: 36.5\n",
"Min: 0.6\n",
"Max: 76.0\n",
"For the feature 'Gender', the least common label is '0.0' with 41 occurrences. This represents 41.41% of the dataset.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/GSE57802.csv\n"
]
}
],
"source": [
"# 1. We need to first create the selected_clinical_df using clinical_data from Step 1\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(\"Preview of selected clinical features:\")\n",
"print(preview)\n",
"\n",
"# Save the clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"selected_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"# Create directory if it doesn't exist\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. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"\n",
"# 4. Determine whether the trait and demographic features are severely biased\n",
"trait_type = 'binary' if len(linked_data[trait].unique()) <= 2 else 'continuous'\n",
"if trait_type == 'binary':\n",
" is_trait_biased = judge_binary_variable_biased(linked_data, trait)\n",
"else:\n",
" is_trait_biased = judge_continuous_variable_biased(linked_data, trait)\n",
"\n",
"# Remove biased demographic features\n",
"unbiased_linked_data = linked_data.copy()\n",
"if 'Age' in unbiased_linked_data.columns:\n",
" age_biased = judge_continuous_variable_biased(unbiased_linked_data, 'Age')\n",
" if age_biased:\n",
" unbiased_linked_data = unbiased_linked_data.drop(columns=['Age'])\n",
" \n",
"if 'Gender' in unbiased_linked_data.columns:\n",
" gender_biased = judge_binary_variable_biased(unbiased_linked_data, 'Gender')\n",
" if gender_biased:\n",
" unbiased_linked_data = unbiased_linked_data.drop(columns=['Gender'])\n",
"\n",
"# 5. Conduct quality check and save the 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 iPSC-derived neurons of ASD patients and unaffected siblings.\"\n",
")\n",
"\n",
"# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\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\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"The dataset was determined to be not usable for analysis.\")"
]
}
],
"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
}
|