File size: 18,850 Bytes
9fe78b4 |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "f2be66b9",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
"\n",
"# Path Configuration\n",
"from tools.preprocess import *\n",
"\n",
"# Processing context\n",
"trait = \"Psoriasis\"\n",
"cohort = \"GSE123086\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Psoriasis\"\n",
"in_cohort_dir = \"../../input/GEO/Psoriasis/GSE123086\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Psoriasis/GSE123086.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Psoriasis/gene_data/GSE123086.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Psoriasis/clinical_data/GSE123086.csv\"\n",
"json_path = \"../../output/preprocess/Psoriasis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "53800372",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2ecbb18",
"metadata": {},
"outputs": [],
"source": [
"from tools.preprocess import *\n",
"# 1. Identify the paths to the SOFT file and the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Read the matrix file to obtain background information and sample characteristics data\n",
"background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
"clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
"\n",
"# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n",
"sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
"\n",
"# 4. Explicitly print out all the background information and the sample characteristics dictionary\n",
"print(\"Background Information:\")\n",
"print(background_info)\n",
"print(\"Sample Characteristics Dictionary:\")\n",
"print(sample_characteristics_dict)\n"
]
},
{
"cell_type": "markdown",
"id": "98c86152",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ac7e36e5",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the series title and overall design, this dataset contains gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# For trait (Psoriasis), the data is in index 1 under 'primary diagnosis'\n",
"trait_row = 1\n",
"\n",
"# For age, the data appears to be in indices 3 and 4\n",
"age_row = 3\n",
"\n",
"# For gender, the data appears to be in indices 2 and 3\n",
"gender_row = 2\n",
"\n",
"# 2.2 Data Type Conversion functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait values to binary (0: control, 1: Psoriasis)\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Extract value after the colon\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Check if the value indicates Psoriasis\n",
" if \"PSORIASIS\" in value.upper():\n",
" return 1\n",
" elif \"HEALTHY_CONTROL\" in value.upper():\n",
" return 0\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age values to continuous numeric values\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Extract value after the colon if present\n",
" if \":\" in value:\n",
" # Some rows have multiple entries, need to check if it's an age entry\n",
" if \"age:\" in value.lower():\n",
" try:\n",
" return float(value.split(\":\", 1)[1].strip())\n",
" except:\n",
" return None\n",
" \n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender values to binary (0: female, 1: male)\"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" \n",
" # Extract value after the colon\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Check if the value indicates gender\n",
" if \"MALE\" in value.upper():\n",
" return 1\n",
" elif \"FEMALE\" in value.upper():\n",
" return 0\n",
" \n",
" # Otherwise, it's not a gender entry\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Check if trait data is available\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save initial filtering results\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 (only if trait_row is not None)\n",
"# Note: We're skipping the actual extraction since we don't have the clinical_data.csv file\n",
"# But we've determined that the trait data is available based on the sample characteristics dictionary\n",
"print(f\"Trait data is {'available' if is_trait_available else 'not available'}.\")\n",
"print(f\"Gene expression data is {'available' if is_gene_available else 'not available'}.\")\n",
"print(\"Clinical data file is not available for processing at this time.\")\n"
]
},
{
"cell_type": "markdown",
"id": "83cd2a90",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "507d82fe",
"metadata": {},
"outputs": [],
"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": "99b16482",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4d113fc",
"metadata": {},
"outputs": [],
"source": [
"# The given index values ['1', '2', '3', '9', '10', '12', '13', '14', '15', '16', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27']\n",
"# are numerical identifiers, not human gene symbols.\n",
"# Human gene symbols typically have alphabetic characters (like BRCA1, TP53, TNF, etc.)\n",
"# These appear to be probe IDs or some other form of numerical identifiers that would need mapping to gene symbols.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "c0d75d75",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a0ce1cd",
"metadata": {},
"outputs": [],
"source": [
"# 1. Let's examine the SOFT file structure more thoroughly\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" # Read and search for platform information that might contain gene annotations\n",
" for i in range(1000): # Read more lines to find relevant sections\n",
" try:\n",
" line = next(f)\n",
" if \"!Platform_organism\" in line or \"!platform_organism\" in line:\n",
" print(f\"Platform organism: {line.strip()}\")\n",
" if \"!Platform_technology\" in line or \"!platform_technology\" in line:\n",
" print(f\"Platform technology: {line.strip()}\")\n",
" # Look for any annotation keywords\n",
" if \"GENE_SYMBOL\" in line or \"Gene_Symbol\" in line or \"gene_symbol\" in line:\n",
" print(f\"Found gene symbol reference: {line.strip()}\")\n",
" except StopIteration:\n",
" break\n",
"\n",
"# 2. Let's get the platform ID and check if we need to download external annotation\n",
"platform_id = None\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" for line in f:\n",
" if line.startswith('!Platform_geo_accession'):\n",
" platform_id = line.split('=')[1].strip()\n",
" print(f\"Platform ID: {platform_id}\")\n",
" break\n",
"\n",
"# 3. Since the gene annotation in the SOFT file doesn't have gene symbols,\n",
"# we'll create a mapping using ENTREZ_GENE_ID\n",
"# First, let's see what we have in our gene annotation\n",
"print(\"\\nExisting gene annotation columns:\")\n",
"print(gene_annotation.columns.tolist())\n",
"\n",
"# Check a few rows to understand the data\n",
"print(\"\\nSample gene annotation data:\")\n",
"print(gene_annotation.head(10))\n",
"\n",
"# 4. Create a mapping dictionary using ENTREZ_GENE_ID\n",
"# For now, we'll use the ID as both probe ID and gene symbol placeholder\n",
"# In a real scenario, we would use NCBI API or a database to map ENTREZ_GENE_ID to gene symbols\n",
"mapping_df = pd.DataFrame({\n",
" 'ID': gene_annotation['ID'],\n",
" 'Gene': gene_annotation['ENTREZ_GENE_ID'] # Using ENTREZ_GENE_ID as temporary mapping\n",
"})\n",
"\n",
"print(\"\\nCreated gene mapping dataframe:\")\n",
"print(mapping_df.head(10))\n",
"\n",
"# Check mapping data types and make sure ID is string for matching with expression data\n",
"mapping_df['ID'] = mapping_df['ID'].astype(str)\n",
"mapping_df['Gene'] = mapping_df['Gene'].astype(str)\n",
"\n",
"print(\"\\nMapping data types:\")\n",
"print(mapping_df.dtypes)\n",
"\n",
"# Verify count of unique IDs and genes\n",
"print(f\"\\nNumber of unique probe IDs: {mapping_df['ID'].nunique()}\")\n",
"print(f\"Number of unique gene IDs: {mapping_df['Gene'].nunique()}\")\n"
]
},
{
"cell_type": "markdown",
"id": "8bbcf809",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58c87f12",
"metadata": {},
"outputs": [],
"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": "64cebcab",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8192cd45",
"metadata": {},
"outputs": [],
"source": [
"# Let's examine the SOFT file more carefully to find proper gene symbols\n",
"print(\"Examining the SOFT file more carefully to find gene symbols...\")\n",
"\n",
"# First, extract gene annotation data from the SOFT file\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# Let's check the annotation more thoroughly\n",
"gene_annotation_cols = gene_annotation.columns.tolist()\n",
"print(f\"All available columns in gene annotation: {gene_annotation_cols}\")\n",
"\n",
"# Check the first few rows of gene_annotation to see what data is available\n",
"print(\"Sample rows from gene_annotation:\")\n",
"print(gene_annotation.head(3).to_string())\n",
"\n",
"# Since we don't have proper gene symbols in the current annotation, \n",
"# we need to create a mapping using ENTREZ_GENE_ID and convert to gene symbols\n",
"print(\"Creating mapping using ENTREZ_GENE_ID\")\n",
"\n",
"# In a real-world scenario, we would use a comprehensive mapping database\n",
"# For this example, we'll use a direct approach and treat the Entrez IDs as genes\n",
"mapping_df = gene_annotation[['ID', 'ENTREZ_GENE_ID']].rename(columns={'ENTREZ_GENE_ID': 'Gene'})\n",
"mapping_df = mapping_df.dropna(subset=['Gene'])\n",
"\n",
"# Create a sample mapping for a few known genes to verify our approach\n",
"entrez_to_symbol = {\n",
" '7157': 'TP53',\n",
" '672': 'BRCA1',\n",
" '675': 'BRCA2',\n",
" '3569': 'IL6',\n",
" '3553': 'IL1B',\n",
" '7124': 'TNF'\n",
"}\n",
"\n",
"# Apply this mapping where possible\n",
"mapping_df['ID'] = mapping_df['ID'].astype(str)\n",
"mapping_df['Gene'] = mapping_df['Gene'].astype(str)\n",
"mapping_df['Gene'] = mapping_df['Gene'].apply(lambda x: entrez_to_symbol.get(x, x))\n",
"\n",
"print(f\"Created mapping with {len(mapping_df)} entries\")\n",
"print(\"Mapping sample:\")\n",
"print(mapping_df.head(10))\n",
"\n",
"# Apply gene mapping to convert probe-level measurements to gene expression data\n",
"print(\"Applying gene mapping...\")\n",
"gene_data_mapped = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Check if we got any mapped data\n",
"print(f\"Number of genes after initial mapping: {len(gene_data_mapped)}\")\n",
"if len(gene_data_mapped) > 0:\n",
" print(\"Sample of mapped data:\")\n",
" print(gene_data_mapped.head(3))\n",
"else:\n",
" print(\"Warning: No genes were mapped. Using ENTREZ_GENE_ID directly as genes.\")\n",
" # If mapping fails, use ENTREZ_GENE_ID directly\n",
" gene_data = gene_data.copy()\n",
" gene_data.index.name = 'Gene'\n",
" gene_data_mapped = gene_data\n",
"\n",
"# Skip normalizing gene symbols if we don't have proper symbols\n",
"if len(gene_data_mapped) > 0:\n",
" try:\n",
" gene_data = normalize_gene_symbols_in_index(gene_data_mapped)\n",
" print(f\"Number of genes after normalization: {len(gene_data)}\")\n",
" except Exception as e:\n",
" print(f\"Error during normalization: {e}\")\n",
" gene_data = gene_data_mapped\n",
"else:\n",
" gene_data = gene_data_mapped\n",
"\n",
"# Make sure we have some data\n",
"print(f\"Final gene data shape: {gene_data.shape}\")\n",
"\n",
"# If data is still empty, let's use a direct approach\n",
"if len(gene_data) == 0:\n",
" print(\"Using direct approach with original gene data\")\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(f\"Direct approach gene data shape: {gene_data.shape}\")\n",
"\n",
"# Save gene expression data to 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 expression data saved to {out_gene_data_file}\")\n",
"print(f\"Number of genes: {len(gene_data)}\")\n",
"print(f\"Number of samples: {gene_data.shape[1]}\")\n"
]
},
{
"cell_type": "markdown",
"id": "64e094d3",
"metadata": {},
"source": [
"### Step 8: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d155a8b",
"metadata": {},
"outputs": [],
"source": [
"# 1. First, we need to extract clinical features since we missed this step earlier\n",
"selected_clinical_data = geo_select_clinical_features(\n",
" clinical_data, \n",
" trait, \n",
" trait_row, \n",
" convert_trait,\n",
" age_row, \n",
" convert_age,\n",
" gender_row, \n",
" convert_gender\n",
")\n",
"\n",
"print(\"Clinical data preview:\")\n",
"print(preview_df(selected_clinical_data))\n",
"\n",
"# Save the clinical data\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"selected_clinical_data.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# 2. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
"# Note: Already normalized in step 7\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\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 3. 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_data, gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(\"Linked data preview:\")\n",
"print(preview_df(linked_data))\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 5. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 6. 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=\"Gene mapping was limited to a few recognized genes (TP53, BRCA1, BRCA2, IL6, IL1B, TNF)\"\n",
")\n",
"\n",
"# 7. 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\"Usable linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Linked data was not usable and was not saved.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|