File size: 26,746 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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "c9878eb3",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:29.255205Z",
"iopub.status.busy": "2025-03-25T06:52:29.254564Z",
"iopub.status.idle": "2025-03-25T06:52:29.419701Z",
"shell.execute_reply": "2025-03-25T06:52:29.419374Z"
}
},
"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 = \"GSE42133\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Autism_spectrum_disorder_(ASD)\"\n",
"in_cohort_dir = \"../../input/GEO/Autism_spectrum_disorder_(ASD)/GSE42133\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/GSE42133.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE42133.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE42133.csv\"\n",
"json_path = \"../../output/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "517ada7b",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "795cdabd",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:29.421109Z",
"iopub.status.busy": "2025-03-25T06:52:29.420962Z",
"iopub.status.idle": "2025-03-25T06:52:29.804787Z",
"shell.execute_reply": "2025-03-25T06:52:29.804420Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Disrupted functional neworks in autism underlie early brain maldevelopment and provide accurate classification\"\n",
"!Series_summary\t\"The disrupted genetic mechanisms underlying neural abnormalities in Autism Spectrum Disorder remain mostly unknown and speculative. No biological marker nor genetic signature is currently available to assist with early diagnosis.\"\n",
"!Series_summary\t\"We identified a blood-based gene expression signature relevant to the brain pathophysiology in autism. Also we identified genes that are differentially expressed in ASD subjects vs controls and gene modules that efficiently classify ASD and TD subjects.\"\n",
"!Series_overall_design\t\"Leukocyte gene expression levels were analysed in autistic and tipically develoing infants and toddlers with the purpose to identify gene expression signatures relevant to the autistic brain and to assist in the classification of subjects.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['dx (diagnosis): ASD', 'dx (diagnosis): Control'], 1: ['gender: male'], 2: ['cell type: leukocyte']}\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": "61a37ba8",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5067dcbc",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:29.806110Z",
"iopub.status.busy": "2025-03-25T06:52:29.805989Z",
"iopub.status.idle": "2025-03-25T06:52:29.816713Z",
"shell.execute_reply": "2025-03-25T06:52:29.816431Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{'GSM1033105': [1.0], 'GSM1033106': [1.0], 'GSM1033107': [1.0], 'GSM1033108': [1.0], 'GSM1033109': [0.0], 'GSM1033110': [0.0], 'GSM1033111': [1.0], 'GSM1033112': [0.0], 'GSM1033113': [1.0], 'GSM1033114': [0.0], 'GSM1033115': [1.0], 'GSM1033116': [1.0], 'GSM1033117': [1.0], 'GSM1033118': [1.0], 'GSM1033119': [0.0], 'GSM1033120': [0.0], 'GSM1033121': [0.0], 'GSM1033122': [1.0], 'GSM1033123': [0.0], 'GSM1033124': [0.0], 'GSM1033125': [1.0], 'GSM1033126': [1.0], 'GSM1033127': [0.0], 'GSM1033128': [1.0], 'GSM1033129': [1.0], 'GSM1033130': [1.0], 'GSM1033131': [0.0], 'GSM1033132': [0.0], 'GSM1033133': [0.0], 'GSM1033134': [0.0], 'GSM1033135': [0.0], 'GSM1033136': [0.0], 'GSM1033137': [1.0], 'GSM1033138': [0.0], 'GSM1033139': [1.0], 'GSM1033140': [0.0], 'GSM1033141': [0.0], 'GSM1033142': [0.0], 'GSM1033143': [0.0], 'GSM1033144': [0.0], 'GSM1033145': [0.0], 'GSM1033146': [0.0], 'GSM1033147': [0.0], 'GSM1033148': [1.0], 'GSM1033149': [1.0], 'GSM1033150': [0.0], 'GSM1033152': [0.0], 'GSM1033153': [0.0], 'GSM1033154': [0.0], 'GSM1033155': [0.0], 'GSM1033156': [1.0], 'GSM1033157': [1.0], 'GSM1033158': [0.0], 'GSM1033159': [1.0], 'GSM1033160': [0.0], 'GSM1033161': [0.0], 'GSM1033162': [1.0], 'GSM1033163': [1.0], 'GSM1033164': [1.0], 'GSM1033165': [0.0], 'GSM1033166': [0.0], 'GSM1033167': [1.0], 'GSM1033168': [0.0], 'GSM1033169': [1.0], 'GSM1033170': [0.0], 'GSM1033171': [1.0], 'GSM1033172': [0.0], 'GSM1033173': [1.0], 'GSM1033174': [1.0], 'GSM1033175': [0.0], 'GSM1033176': [0.0], 'GSM1033177': [0.0], 'GSM1033178': [1.0], 'GSM1033179': [0.0], 'GSM1033180': [1.0], 'GSM1033181': [1.0], 'GSM1033182': [1.0], 'GSM1033183': [1.0], 'GSM1033184': [1.0], 'GSM1033185': [1.0], 'GSM1033186': [1.0], 'GSM1033187': [0.0], 'GSM1033188': [1.0], 'GSM1033189': [1.0], 'GSM1033190': [1.0], 'GSM1033191': [1.0], 'GSM1033192': [1.0], 'GSM1033193': [1.0], 'GSM1033194': [0.0], 'GSM1033195': [1.0], 'GSM1033196': [1.0], 'GSM1033197': [1.0], 'GSM1033198': [1.0], 'GSM1033199': [1.0], 'GSM1033200': [1.0], 'GSM1033201': [1.0], 'GSM1033202': [1.0], 'GSM1033203': [0.0], 'GSM1033204': [0.0], 'GSM1033205': [1.0], 'GSM1033206': [1.0], 'GSM1033207': [1.0], 'GSM1033208': [0.0], 'GSM1033209': [0.0], 'GSM1033211': [1.0], 'GSM1033212': [0.0], 'GSM1033213': [1.0], 'GSM1033214': [1.0], 'GSM1033215': [1.0], 'GSM1033216': [1.0], 'GSM1033217': [1.0], 'GSM1033218': [0.0], 'GSM1033219': [1.0], 'GSM1033220': [1.0], 'GSM1033221': [1.0], 'GSM1033222': [1.0], 'GSM1033223': [0.0], 'GSM1033224': [1.0], 'GSM1033225': [1.0], 'GSM1033227': [1.0], 'GSM1033228': [1.0], 'GSM1033229': [1.0], 'GSM1033230': [1.0], 'GSM1033231': [1.0], 'GSM1033232': [1.0], 'GSM1033233': [1.0], 'GSM1033234': [1.0], 'GSM1033235': [0.0], 'GSM1033236': [1.0], 'GSM1033237': [1.0], 'GSM1033238': [1.0], 'GSM1033239': [1.0], 'GSM1033240': [1.0], 'GSM1033241': [1.0], 'GSM1033242': [0.0], 'GSM1033243': [1.0], 'GSM1033244': [1.0], 'GSM1033245': [1.0], 'GSM1033246': [0.0], 'GSM1033247': [0.0], 'GSM1033248': [1.0], 'GSM1033249': [1.0], 'GSM1033250': [1.0], 'GSM1033251': [1.0], 'GSM1033253': [1.0], 'GSM1033254': [1.0], 'GSM1033255': [0.0]}\n",
"Clinical data saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE42133.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains gene expression data from leukocytes\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# Trait (diagnosis) is available in row 0\n",
"trait_row = 0\n",
"\n",
"# Age is not available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# Gender appears to be constant (all male) as indicated in row 1\n",
"# Since all subjects are male, this is not a useful variable for our associative study\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" # Extract the value after the colon and trim whitespace\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Convert to binary: ASD = 1, Control = 0\n",
" if value.lower() == \"asd\":\n",
" return 1\n",
" elif value.lower() == \"control\":\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" # Not needed as age data is not available\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" # Not needed as gender data is not variable (all male)\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save initial filtering 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 (if applicable)\n",
"if trait_row is not None:\n",
" # Since clinical_data is not defined in the prompt, assuming it was loaded in a previous step\n",
" # If not, we'd need to load it first\n",
" try:\n",
" # Extract clinical features\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data, # Assuming clinical_data was loaded in a previous step\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 to CSV\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" except NameError:\n",
" print(\"Warning: clinical_data not found. Make sure it was loaded in a previous step.\")\n"
]
},
{
"cell_type": "markdown",
"id": "7e9eaecd",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f0aa0c26",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:29.817749Z",
"iopub.status.busy": "2025-03-25T06:52:29.817646Z",
"iopub.status.idle": "2025-03-25T06:52:30.498468Z",
"shell.execute_reply": "2025-03-25T06:52:30.498085Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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. 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": "e3765e18",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c8dbd866",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:30.499749Z",
"iopub.status.busy": "2025-03-25T06:52:30.499627Z",
"iopub.status.idle": "2025-03-25T06:52:30.501522Z",
"shell.execute_reply": "2025-03-25T06:52:30.501233Z"
}
},
"outputs": [],
"source": [
"# The identifiers starting with \"ILMN_\" are Illumina probe IDs, not human gene symbols.\n",
"# These are microarray probe identifiers that need to be mapped to gene symbols.\n",
"# Illumina probes (ILMN_xxxxxxx) require mapping to gene symbols for biological interpretation.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "fdf14187",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bdccf79f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:30.502628Z",
"iopub.status.busy": "2025-03-25T06:52:30.502519Z",
"iopub.status.idle": "2025-03-25T06:52:42.585874Z",
"shell.execute_reply": "2025-03-25T06:52:42.585506Z"
}
},
"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": "d08ac800",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "62b5f752",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:42.587160Z",
"iopub.status.busy": "2025-03-25T06:52:42.587041Z",
"iopub.status.idle": "2025-03-25T06:52:43.084912Z",
"shell.execute_reply": "2025-03-25T06:52:43.084540Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data after mapping:\n",
"Shape: (21464, 147)\n",
"First few gene symbols:\n",
"Index(['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2',\n",
" 'A4GALT', 'A4GNT'],\n",
" dtype='object', name='Gene')\n"
]
}
],
"source": [
"# 1. Identify the columns for probe IDs and gene symbols in the gene annotation dataframe\n",
"# From the preview, we can see that 'ID' contains probe identifiers (ILMN_xxxxxxx) \n",
"# and 'Symbol' contains gene symbols\n",
"\n",
"# 2. Get the gene mapping dataframe using get_gene_mapping from the library\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"# This function properly handles many-to-many relationships as required\n",
"gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)\n",
"\n",
"# Preview the results to verify the mapping worked correctly\n",
"print(\"Gene expression data after mapping:\")\n",
"print(f\"Shape: {gene_data.shape}\")\n",
"print(\"First few gene symbols:\")\n",
"print(gene_data.index[:10])\n"
]
},
{
"cell_type": "markdown",
"id": "c03ca10f",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "ec2a6186",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:52:43.086231Z",
"iopub.status.busy": "2025-03-25T06:52:43.086121Z",
"iopub.status.idle": "2025-03-25T06:52:53.442185Z",
"shell.execute_reply": "2025-03-25T06:52:53.441793Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical features:\n",
"{'GSM1033105': [1.0], 'GSM1033106': [1.0], 'GSM1033107': [1.0], 'GSM1033108': [1.0], 'GSM1033109': [0.0], 'GSM1033110': [0.0], 'GSM1033111': [1.0], 'GSM1033112': [0.0], 'GSM1033113': [1.0], 'GSM1033114': [0.0], 'GSM1033115': [1.0], 'GSM1033116': [1.0], 'GSM1033117': [1.0], 'GSM1033118': [1.0], 'GSM1033119': [0.0], 'GSM1033120': [0.0], 'GSM1033121': [0.0], 'GSM1033122': [1.0], 'GSM1033123': [0.0], 'GSM1033124': [0.0], 'GSM1033125': [1.0], 'GSM1033126': [1.0], 'GSM1033127': [0.0], 'GSM1033128': [1.0], 'GSM1033129': [1.0], 'GSM1033130': [1.0], 'GSM1033131': [0.0], 'GSM1033132': [0.0], 'GSM1033133': [0.0], 'GSM1033134': [0.0], 'GSM1033135': [0.0], 'GSM1033136': [0.0], 'GSM1033137': [1.0], 'GSM1033138': [0.0], 'GSM1033139': [1.0], 'GSM1033140': [0.0], 'GSM1033141': [0.0], 'GSM1033142': [0.0], 'GSM1033143': [0.0], 'GSM1033144': [0.0], 'GSM1033145': [0.0], 'GSM1033146': [0.0], 'GSM1033147': [0.0], 'GSM1033148': [1.0], 'GSM1033149': [1.0], 'GSM1033150': [0.0], 'GSM1033152': [0.0], 'GSM1033153': [0.0], 'GSM1033154': [0.0], 'GSM1033155': [0.0], 'GSM1033156': [1.0], 'GSM1033157': [1.0], 'GSM1033158': [0.0], 'GSM1033159': [1.0], 'GSM1033160': [0.0], 'GSM1033161': [0.0], 'GSM1033162': [1.0], 'GSM1033163': [1.0], 'GSM1033164': [1.0], 'GSM1033165': [0.0], 'GSM1033166': [0.0], 'GSM1033167': [1.0], 'GSM1033168': [0.0], 'GSM1033169': [1.0], 'GSM1033170': [0.0], 'GSM1033171': [1.0], 'GSM1033172': [0.0], 'GSM1033173': [1.0], 'GSM1033174': [1.0], 'GSM1033175': [0.0], 'GSM1033176': [0.0], 'GSM1033177': [0.0], 'GSM1033178': [1.0], 'GSM1033179': [0.0], 'GSM1033180': [1.0], 'GSM1033181': [1.0], 'GSM1033182': [1.0], 'GSM1033183': [1.0], 'GSM1033184': [1.0], 'GSM1033185': [1.0], 'GSM1033186': [1.0], 'GSM1033187': [0.0], 'GSM1033188': [1.0], 'GSM1033189': [1.0], 'GSM1033190': [1.0], 'GSM1033191': [1.0], 'GSM1033192': [1.0], 'GSM1033193': [1.0], 'GSM1033194': [0.0], 'GSM1033195': [1.0], 'GSM1033196': [1.0], 'GSM1033197': [1.0], 'GSM1033198': [1.0], 'GSM1033199': [1.0], 'GSM1033200': [1.0], 'GSM1033201': [1.0], 'GSM1033202': [1.0], 'GSM1033203': [0.0], 'GSM1033204': [0.0], 'GSM1033205': [1.0], 'GSM1033206': [1.0], 'GSM1033207': [1.0], 'GSM1033208': [0.0], 'GSM1033209': [0.0], 'GSM1033211': [1.0], 'GSM1033212': [0.0], 'GSM1033213': [1.0], 'GSM1033214': [1.0], 'GSM1033215': [1.0], 'GSM1033216': [1.0], 'GSM1033217': [1.0], 'GSM1033218': [0.0], 'GSM1033219': [1.0], 'GSM1033220': [1.0], 'GSM1033221': [1.0], 'GSM1033222': [1.0], 'GSM1033223': [0.0], 'GSM1033224': [1.0], 'GSM1033225': [1.0], 'GSM1033227': [1.0], 'GSM1033228': [1.0], 'GSM1033229': [1.0], 'GSM1033230': [1.0], 'GSM1033231': [1.0], 'GSM1033232': [1.0], 'GSM1033233': [1.0], 'GSM1033234': [1.0], 'GSM1033235': [0.0], 'GSM1033236': [1.0], 'GSM1033237': [1.0], 'GSM1033238': [1.0], 'GSM1033239': [1.0], 'GSM1033240': [1.0], 'GSM1033241': [1.0], 'GSM1033242': [0.0], 'GSM1033243': [1.0], 'GSM1033244': [1.0], 'GSM1033245': [1.0], 'GSM1033246': [0.0], 'GSM1033247': [0.0], 'GSM1033248': [1.0], 'GSM1033249': [1.0], 'GSM1033250': [1.0], 'GSM1033251': [1.0], 'GSM1033253': [1.0], 'GSM1033254': [1.0], 'GSM1033255': [0.0]}\n",
"Clinical data saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE42133.csv\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE42133.csv\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"For the feature 'Autism_spectrum_disorder_(ASD)', the least common label is '0.0' with 56 occurrences. This represents 38.10% of the dataset.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Autism_spectrum_disorder_(ASD)/GSE42133.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
}
|