File size: 27,092 Bytes
f88156f |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "aa4624dc",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:02:30.497571Z",
"iopub.status.busy": "2025-03-25T07:02:30.497472Z",
"iopub.status.idle": "2025-03-25T07:02:30.657928Z",
"shell.execute_reply": "2025-03-25T07:02:30.657589Z"
}
},
"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 = \"Breast_Cancer\"\n",
"cohort = \"GSE249377\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Breast_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Breast_Cancer/GSE249377\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Breast_Cancer/GSE249377.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Breast_Cancer/gene_data/GSE249377.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Breast_Cancer/clinical_data/GSE249377.csv\"\n",
"json_path = \"../../output/preprocess/Breast_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "d0646da5",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2b785d56",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:02:30.659281Z",
"iopub.status.busy": "2025-03-25T07:02:30.659137Z",
"iopub.status.idle": "2025-03-25T07:02:30.843342Z",
"shell.execute_reply": "2025-03-25T07:02:30.843008Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Exploring the Effects of Experimental Parameters and Data Modeling Approaches on In Vitro Transcriptomic Point-of-Departure Estimates\"\n",
"!Series_summary\t\"Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr).\"\n",
"!Series_overall_design\t\"Multiple computational approaches for determining tPODs are compared using six HTTr datasets, all generated from a single cell type (MCF7, a breast cancer cell line), but using three different exposure durations and with two different media formulations. Each dataset included 44 chemicals in an eight-point concentration-response. We previously published a subset of these data (GSE162855) corresponding to one exposure time (6 hrs) and media formulation (DMEM + 10% HI-FBS). In the current study we incorporate additional data for all 5 additional combinations of exposure times (6, 12, and 24 hrs) and media formulations (DMEM + either 10% HI-FBS or 10% charcoal-stripped FBS), and compare results across a broader set of computational approaches for determining an overall transcriptomic point of departure (tPOD) for each chemical.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['cell line: NA', 'cell line: MCF7'], 1: ['media: NA', 'media: DMEM + 10% HI-FBS', 'media: DMEM + 10% charcoal-stripped FBS'], 2: ['treatment: untreated', 'treatment: 12h exposure of 0.03 uM of Fulvestrant', 'treatment: 12h exposure of 0.3 uM of Atrazine', 'treatment: 12h exposure of 0.3 uM of Butafenacil', 'treatment: 12h exposure of 0.1 uM of Propiconazole', 'treatment: 12h exposure of 1 uM of Tetrac', 'treatment: 12h exposure of 0.3 uM of Cladribine', 'treatment: 12h exposure of 30 uM of Lovastatin', 'treatment: 12h exposure of 0.3 uM of 4-Hydroxytamoxifen', 'treatment: 12h exposure of 3 uM of Butafenacil', 'treatment: 12h exposure of 3 uM of Cypermethrin', 'treatment: 12h exposure of 100 uM of Bifenthrin', 'treatment: 12h exposure of 1 uM of Fulvestrant', 'treatment: 12h exposure of 0.3 uM of Prochloraz', 'treatment: 12h exposure of 1 uM of Reserpine', 'treatment: 12h exposure of 100 uM of Butafenacil', 'treatment: 12h exposure of 10 uM of Amiodarone hydrochloride', 'treatment: 12h exposure of 100 uM of Fomesafen', 'treatment: 12h exposure of 1 uM of Lactofen', 'treatment: 12h exposure of 3 uM of Cladribine', 'treatment: 12h exposure of 0.1 uM of Maneb', 'treatment: 12h exposure of 0.1 uM of Cycloheximide', 'treatment: 12h exposure of 100 uM of Bisphenol B', 'treatment: 12h exposure of 0.3 uM of Clofibrate', 'treatment: 12h exposure of 0.03 uM of Thiram', 'treatment: 12h exposure of 0.3 uM of PFOA', 'treatment: 12h exposure of 100 uM of Simazine', 'treatment: 12h exposure of 0.03 uM of Prochloraz', 'treatment: 12h exposure of 100 uM of Amiodarone hydrochloride', 'treatment: 12h exposure of 0.1 uM of Cyproterone acetate'], 3: ['chemical name: NA', 'chemical name: Fulvestrant', 'chemical name: Atrazine', 'chemical name: Butafenacil', 'chemical name: Propiconazole', 'chemical name: Tetrac', 'chemical name: Cladribine', 'chemical name: Lovastatin', 'chemical name: 4-Hydroxytamoxifen', 'chemical name: Cypermethrin', 'chemical name: Bifenthrin', 'chemical name: Prochloraz', 'chemical name: Reserpine', 'chemical name: Amiodarone hydrochloride', 'chemical name: Fomesafen', 'chemical name: Lactofen', 'chemical name: Maneb', 'chemical name: Cycloheximide', 'chemical name: Bisphenol B', 'chemical name: Clofibrate', 'chemical name: Thiram', 'chemical name: PFOA', 'chemical name: Simazine', 'chemical name: Cyproterone acetate', 'chemical name: Cyproconazole', 'chemical name: Vinclozolin', 'chemical name: 4-Nonylphenol, branched', 'chemical name: Fenofibrate', 'chemical name: Troglitazone', 'chemical name: Farglitazar'], 4: ['chemical sample id: NA', 'chemical sample id: TP0001651F04', 'chemical sample id: TP0001651E05', 'chemical sample id: TP0001651A03', 'chemical sample id: TP0001651B04', 'chemical sample id: TP0001651F01', 'chemical sample id: TP0001651G04', 'chemical sample id: TP0001651G02', 'chemical sample id: TP0001651C02', 'chemical sample id: TP0001651D03', 'chemical sample id: TP0001651E01', 'chemical sample id: TP0001651E03', 'chemical sample id: TP0001651B03', 'chemical sample id: TP0001651B05', 'chemical sample id: TP0001651H03', 'chemical sample id: TP0001651D01', 'chemical sample id: TP0001651H02', 'chemical sample id: TP0001651C06', 'chemical sample id: TP0001651C05', 'chemical sample id: TP0001651D04', 'chemical sample id: TP0001651D05', 'chemical sample id: TP0001651D02', 'chemical sample id: TP0001651C03', 'chemical sample id: TP0001651G05', 'chemical sample id: TP0001651A06', 'chemical sample id: TP0001651G01', 'chemical sample id: TP0001651E02', 'chemical sample id: TP0001651F05', 'chemical sample id: TP0001651B06', 'chemical sample id: TP0001651E04'], 5: ['chemical concentration: NA', 'chemical concentration: 0.03 uM', 'chemical concentration: 0.3 uM', 'chemical concentration: 0.1 uM', 'chemical concentration: 1 uM', 'chemical concentration: 30 uM', 'chemical concentration: 3 uM', 'chemical concentration: 100 uM', 'chemical concentration: 10 uM', 'chemical concentration: 0 uM'], 6: ['dose level: NA', 'dose level: 1', 'dose level: 3', 'dose level: 2', 'dose level: 4', 'dose level: 7', 'dose level: 5', 'dose level: 8', 'dose level: 6', 'dose level: 0'], 7: ['exposure time: NA', 'exposure time: 12h', 'exposure time: 24h', 'exposure time: 6h'], 8: ['assay plate: TC00283154', 'assay plate: TC00283157', 'assay plate: TC00283174', 'assay plate: TC00283179', 'assay plate: TC00283182', 'assay plate: TC00283185', 'assay plate: TC00283191', 'assay plate: TC00283197', 'assay plate: TC00283200', 'assay plate: TC00283203', 'assay plate: TC00283212', 'assay plate: TC00283215', 'assay plate: TC00283221', 'assay plate: TC00283224', 'assay plate: TC00283227'], 9: ['assay plate well: A01', 'assay plate well: A02', 'assay plate well: A03', 'assay plate well: A04', 'assay plate well: A05', 'assay plate well: A06', 'assay plate well: A07', 'assay plate well: A08', 'assay plate well: A09', 'assay plate well: A10', 'assay plate well: A11', 'assay plate well: A12', 'assay plate well: A13', 'assay plate well: A14', 'assay plate well: A15', 'assay plate well: A16', 'assay plate well: A17', 'assay plate well: A18', 'assay plate well: A19', 'assay plate well: A20', 'assay plate well: A21', 'assay plate well: A22', 'assay plate well: A23', 'assay plate well: A24', 'assay plate well: B01', 'assay plate well: B02', 'assay plate well: B03', 'assay plate well: B04', 'assay plate well: B05', 'assay plate well: B06']}\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": "dcc403da",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b27da8b9",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:02:30.844593Z",
"iopub.status.busy": "2025-03-25T07:02:30.844486Z",
"iopub.status.idle": "2025-03-25T07:02:30.850704Z",
"shell.execute_reply": "2025-03-25T07:02:30.850430Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# From the Series summary and overall design, this dataset appears to contain gene expression data\n",
"# from high-throughput transcriptomics (HTTr) experiments in MCF7 cells\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# There is no explicit trait (breast cancer) information in the sample characteristics\n",
"# The MCF7 cell line is a breast cancer cell line, but this is a constant across all samples\n",
"trait_row = None # No trait variable is available\n",
"\n",
"# Age is not available in this dataset as it's a cell line study\n",
"age_row = None\n",
"\n",
"# No gender information is available as this is a cell line study\n",
"gender_row = None\n",
"\n",
"# Define conversion functions for trait data (even though not used in this case)\n",
"def convert_trait(value):\n",
" # Not used in this dataset, but required for the function signature\n",
" if value is None or 'NA' in value:\n",
" return None\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" return 1 if value.lower() == 'yes' else 0\n",
"\n",
"def convert_age(value):\n",
" # Not used in this dataset\n",
" if value is None or 'NA' in value:\n",
" return None\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" try:\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" # Not used in this dataset\n",
" if value is None or 'NA' in value:\n",
" return None\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" return 1 if value.lower() in ['male', 'm'] else 0 if value.lower() in ['female', 'f'] else None\n",
"\n",
"# 3. Save Metadata\n",
"# Trait data is not available as trait_row is None\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n",
" is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"# Skip this step since trait_row is None (no clinical data is available as determined above)\n"
]
},
{
"cell_type": "markdown",
"id": "fadacbb2",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bc7f47fd",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:02:30.851758Z",
"iopub.status.busy": "2025-03-25T07:02:30.851657Z",
"iopub.status.idle": "2025-03-25T07:02:30.858877Z",
"shell.execute_reply": "2025-03-25T07:02:30.858601Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOFT file: ../../input/GEO/Breast_Cancer/GSE249377/GSE249377_family.soft.gz\n",
"Matrix file: ../../input/GEO/Breast_Cancer/GSE249377/GSE249377_series_matrix.txt.gz\n",
"Examining matrix file content...\n",
"Line 0: !Series_title\t\"Exploring the Effects of Experimental Parameters and Data Modeling Approaches on In V...\n",
"Error examining file: unsupported operand type(s) for +: 'NoneType' and 'int'\n",
"Gene expression data could not be successfully extracted from this dataset.\n"
]
}
],
"source": [
"# 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",
"print(f\"SOFT file: {soft_file}\")\n",
"print(f\"Matrix file: {matrix_file}\")\n",
"\n",
"# Set gene availability flag\n",
"is_gene_available = True # Initially assume gene data is available\n",
"\n",
"# First check if the matrix file contains the expected marker and examine file content\n",
"print(\"Examining matrix file content...\")\n",
"marker_row = None\n",
"sample_lines = []\n",
"\n",
"try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for i, line in enumerate(file):\n",
" # Store marker position if found\n",
" if \"!series_matrix_table_begin\" in line:\n",
" marker_row = i\n",
" print(f\"Found matrix table marker at line {i}\")\n",
" \n",
" # Store lines around the marker for inspection\n",
" if marker_row is not None and i >= marker_row and i < marker_row + 10:\n",
" sample_lines.append(line.strip())\n",
" \n",
" # Also capture some lines from the beginning\n",
" if i < 10:\n",
" print(f\"Line {i}: {line.strip()[:100]}...\")\n",
" \n",
" # Don't read the entire file\n",
" if i > marker_row + 20 and marker_row is not None:\n",
" break\n",
" if i > 100 and marker_row is None:\n",
" break\n",
"\n",
" if marker_row is not None:\n",
" print(\"\\nLines immediately after the marker:\")\n",
" for i, line in enumerate(sample_lines):\n",
" print(f\"Line {marker_row + i}: {line[:100]}...\")\n",
" \n",
" # Try a more manual approach to extract the data\n",
" data_lines = []\n",
" gene_ids = []\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for i, line in enumerate(file):\n",
" if i <= marker_row: # Skip until after the marker\n",
" continue\n",
" if line.startswith('!'): # Skip any remaining comment lines\n",
" continue\n",
" if not line.strip(): # Skip empty lines\n",
" continue\n",
" \n",
" # Found a data line\n",
" if i == marker_row + 1: # This should be the header line\n",
" headers = line.strip().split('\\t')\n",
" print(f\"Found headers: {headers[:5]}... (total: {len(headers)})\")\n",
" else:\n",
" parts = line.strip().split('\\t')\n",
" if len(parts) > 1: # Ensure it's a valid data line\n",
" gene_ids.append(parts[0])\n",
" data_lines.append(parts)\n",
"\n",
" # Don't process too many lines for this test\n",
" if len(data_lines) > 100:\n",
" break\n",
" \n",
" if len(data_lines) > 0:\n",
" print(f\"Successfully parsed {len(data_lines)} data lines manually\")\n",
" print(f\"First few gene IDs: {gene_ids[:10]}\")\n",
" \n",
" # Now try the proper extraction\n",
" try:\n",
" # Try using the library function\n",
" gene_data = get_genetic_data(matrix_file)\n",
" \n",
" if len(gene_data) > 0:\n",
" print(f\"Successfully extracted gene data with shape: {gene_data.shape}\")\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20].tolist())\n",
" else:\n",
" # If the library function fails, try direct pandas method\n",
" print(\"Library function returned empty data, trying direct pandas method...\")\n",
" gene_data = pd.read_csv(matrix_file, compression='gzip', \n",
" skiprows=marker_row+1, \n",
" header=0, \n",
" sep='\\t', \n",
" on_bad_lines='skip')\n",
" \n",
" if len(gene_data) > 0:\n",
" id_col = gene_data.columns[0]\n",
" gene_data = gene_data.rename(columns={id_col: 'ID'})\n",
" gene_data.set_index('ID', inplace=True)\n",
" print(f\"Successfully extracted gene data with shape: {gene_data.shape}\")\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20].tolist())\n",
" else:\n",
" print(\"Still couldn't extract gene data using pandas.\")\n",
" is_gene_available = False\n",
" except Exception as e:\n",
" print(f\"Error extracting gene data with standard methods: {e}\")\n",
" is_gene_available = False\n",
" else:\n",
" print(\"No data lines found after the marker\")\n",
" is_gene_available = False\n",
" else:\n",
" print(\"Could not find '!series_matrix_table_begin' marker in the file.\")\n",
" is_gene_available = False\n",
" \n",
"except Exception as e:\n",
" print(f\"Error examining file: {e}\")\n",
" is_gene_available = False\n",
"\n",
"if not is_gene_available:\n",
" print(\"Gene expression data could not be successfully extracted from this dataset.\")\n",
" # Update the validation record since gene data isn't available\n",
" is_trait_available = trait_row is not None\n",
" validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n",
" is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n"
]
},
{
"cell_type": "markdown",
"id": "060908d5",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ab92883a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:02:30.859849Z",
"iopub.status.busy": "2025-03-25T07:02:30.859748Z",
"iopub.status.idle": "2025-03-25T07:02:31.808420Z",
"shell.execute_reply": "2025-03-25T07:02:31.807759Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOFT file: ../../input/GEO/Breast_Cancer/GSE249377/GSE249377_family.soft.gz\n",
"Matrix file: ../../input/GEO/Breast_Cancer/GSE249377/GSE249377_series_matrix.txt.gz\n",
"Found the matrix table marker at line 84\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning: Extracted gene data has 0 rows.\n",
"Examining file content to diagnose the issue:\n",
"Line 82: !Sample_relation\t\"SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRX22782419\"\t\"SRA: https://www.ncbi.nlm...\n",
"Line 83: !Sample_supplementary_file_1\t\"NONE\"\t\"NONE\"\t\"NONE\"\t\"NONE\"\t\"NONE\"\t\"NONE\"\t\"NONE\"\t\"NONE\"\t\"NONE\"\t\"NONE\"\t\"...\n",
"Line 84: !series_matrix_table_begin...\n",
"Line 85: \"ID_REF\"\t\"GSM7937728\"\t\"GSM7937729\"\t\"GSM7937730\"\t\"GSM7937731\"\t\"GSM7937732\"\t\"GSM7937733\"\t\"GSM7937734\"\t...\n",
"Line 86: !series_matrix_table_end...\n",
"Gene expression data could not be successfully extracted from this dataset.\n"
]
}
],
"source": [
"# 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",
"print(f\"SOFT file: {soft_file}\")\n",
"print(f\"Matrix file: {matrix_file}\")\n",
"\n",
"# Set gene availability flag\n",
"is_gene_available = True # Initially assume gene data is available\n",
"\n",
"# First check if the matrix file contains the expected marker\n",
"found_marker = False\n",
"marker_row = None\n",
"try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for i, line in enumerate(file):\n",
" if \"!series_matrix_table_begin\" in line:\n",
" found_marker = True\n",
" marker_row = i\n",
" print(f\"Found the matrix table marker at line {i}\")\n",
" break\n",
" \n",
" if not found_marker:\n",
" print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n",
" is_gene_available = False\n",
" \n",
" # If marker was found, try to extract gene data\n",
" if is_gene_available:\n",
" try:\n",
" # Try using the library function\n",
" gene_data = get_genetic_data(matrix_file)\n",
" \n",
" if gene_data.shape[0] == 0:\n",
" print(\"Warning: Extracted gene data has 0 rows.\")\n",
" is_gene_available = False\n",
" else:\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" # Print the first 20 gene/probe identifiers\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20].tolist())\n",
" except Exception as e:\n",
" print(f\"Error extracting gene data with get_genetic_data(): {e}\")\n",
" is_gene_available = False\n",
" \n",
" # If gene data extraction failed, examine file content to diagnose\n",
" if not is_gene_available:\n",
" print(\"Examining file content to diagnose the issue:\")\n",
" try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Print lines around the marker if found\n",
" if marker_row is not None:\n",
" for i, line in enumerate(file):\n",
" if i >= marker_row - 2 and i <= marker_row + 10:\n",
" print(f\"Line {i}: {line.strip()[:100]}...\")\n",
" if i > marker_row + 10:\n",
" break\n",
" else:\n",
" # If marker not found, print first 10 lines\n",
" for i, line in enumerate(file):\n",
" if i < 10:\n",
" print(f\"Line {i}: {line.strip()[:100]}...\")\n",
" else:\n",
" break\n",
" except Exception as e2:\n",
" print(f\"Error examining file: {e2}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing file: {e}\")\n",
" is_gene_available = False\n",
"\n",
"# Update validation information if gene data extraction failed\n",
"if not is_gene_available:\n",
" print(\"Gene expression data could not be successfully extracted from this dataset.\")\n",
" # Update the validation record since gene data isn't available\n",
" is_trait_available = False # We already determined trait data isn't available in step 2\n",
" validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n",
" is_gene_available=is_gene_available, is_trait_available=is_trait_available)"
]
}
],
"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
}
|