File size: 21,027 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "cbc04477",
"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 = \"Atherosclerosis\"\n",
"cohort = \"GSE123086\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Atherosclerosis\"\n",
"in_cohort_dir = \"../../input/GEO/Atherosclerosis/GSE123086\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Atherosclerosis/GSE123086.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Atherosclerosis/gene_data/GSE123086.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Atherosclerosis/clinical_data/GSE123086.csv\"\n",
"json_path = \"../../output/preprocess/Atherosclerosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "3b92ac01",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0185cd70",
"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": "b0622854",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f2570cd",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Looking at the background info: mentions microarrays and RNA extraction, suggesting gene expression data is available\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"\n",
"# Trait: For atherosclerosis, primary diagnosis is in row 1\n",
"trait_row = 1\n",
"\n",
"# Age: Available in rows 3 and 4\n",
"age_row = 3\n",
"\n",
"# Gender: Available in row 2 (and also some values appear in row 3)\n",
"gender_row = 2\n",
"\n",
"# 2.2 Data Type Conversion\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait value to binary format.\"\"\"\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" \n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # For Atherosclerosis trait\n",
" if \"ATHEROSCLEROSIS\" in value:\n",
" return 1\n",
" else:\n",
" return 0\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to continuous format.\"\"\"\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" \n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender value to binary format (0=female, 1=male).\"\"\"\n",
" if value is None or pd.isna(value):\n",
" return None\n",
" \n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if value.upper() == \"MALE\":\n",
" return 1\n",
" elif value.upper() == \"FEMALE\":\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Conduct initial filtering and save metadata\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",
" # Create output directories if they don't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Use the function from the library to extract clinical features\n",
" clinical_features_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 dataframe\n",
" preview = preview_df(clinical_features_df)\n",
" print(\"Clinical features preview:\", preview)\n",
" \n",
" # Save the clinical data\n",
" clinical_features_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": "df9b35b6",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1007b293",
"metadata": {},
"outputs": [],
"source": [
"# 1. Get the SOFT and matrix file paths again \n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"print(f\"Matrix file found: {matrix_file}\")\n",
"\n",
"# 2. Use the get_genetic_data function from the library to get the gene_data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" \n",
" # 3. Print the first 20 row IDs (gene or probe identifiers)\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "7389b58c",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e798066f",
"metadata": {},
"outputs": [],
"source": [
"# The identifiers shown are numeric values ('1', '2', '3', etc.)\n",
"# These are not standard human gene symbols, which would typically be alphanumeric\n",
"# (like \"BRCA1\", \"TP53\", \"APOE\", etc.)\n",
"# These appear to be probe or feature IDs that need to be mapped to actual gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "0713729f",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "67f9d2f4",
"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. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
"print(\"\\nGene annotation preview:\")\n",
"print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
"print(preview_df(gene_annotation, n=5))\n",
"\n",
"# Check the first few rows of the SOFT file to better understand its structure\n",
"print(\"\\nChecking the SOFT file structure for gene symbols:\")\n",
"gene_symbol_data = []\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" for i, line in enumerate(f):\n",
" if i < 1000: # Examine more lines to find gene symbol information\n",
" if \"GENE_SYMBOL\" in line or \"gene_symbol\" in line.lower() or \"symbol\" in line.lower():\n",
" print(line.strip())\n",
" gene_symbol_data.append(line.strip())\n",
" else:\n",
" break\n",
"\n",
"print(\"\\nSearching for gene symbols in the SOFT file...\")\n",
"# Look for table headers that could contain gene symbol information\n",
"with gzip.open(soft_file, 'rt') as f:\n",
" for i, line in enumerate(f):\n",
" if i < 1000 and \"!platform_table_begin\" in line:\n",
" # Get the next line which should contain column headers\n",
" header_line = next(f, \"\").strip()\n",
" print(f\"Found platform table headers: {header_line}\")\n",
" break\n",
"\n",
"# We need to create a more appropriate mapping\n",
"# First, check if we can extract gene symbols from the Entrez Gene IDs\n",
"# Use the extract_human_gene_symbols function from the library\n",
"print(\"\\nAttempting to map Entrez Gene IDs to gene symbols...\")\n",
"\n",
"# Create a basic mapping dataframe with probe IDs and Entrez Gene IDs\n",
"mapping_data = gene_annotation[['ID', 'ENTREZ_GENE_ID']].copy()\n",
"mapping_data = mapping_data.dropna(subset=['ENTREZ_GENE_ID'])\n",
"mapping_data = mapping_data.rename(columns={'ENTREZ_GENE_ID': 'Gene'})\n",
"\n",
"# Filter out any empty gene values\n",
"mapping_data = mapping_data[mapping_data['Gene'] != '']\n",
"\n",
"# Validate the mapping data\n",
"print(f\"Mapping data shape: {mapping_data.shape}\")\n",
"print(\"Sample mapping data:\")\n",
"print(preview_df(mapping_data, n=5))\n",
"\n",
"# Check if there's additional mapping information in the annotation\n",
"print(\"\\nLooking for gene symbols in annotation data...\")\n",
"for col in gene_annotation.columns:\n",
" sample_values = gene_annotation[col].head(10).tolist()\n",
" print(f\"Column '{col}' sample values: {sample_values}\")\n",
"\n",
"# Count how many unique probes we have for mapping\n",
"unique_probes = mapping_data['ID'].nunique()\n",
"print(f\"\\nNumber of unique probes for mapping: {unique_probes}\")\n",
"\n",
"# Count how many probes map to at least one gene identifier\n",
"genes_mapped = mapping_data.shape[0]\n",
"total_probes = gene_annotation.shape[0]\n",
"mapping_percentage = (genes_mapped / total_probes) * 100 if total_probes > 0 else 0\n",
"print(f\"Found gene identifiers for {genes_mapped} out of {total_probes} probes ({mapping_percentage:.2f}%)\")\n",
"\n",
"# The mapping contains Entrez Gene IDs which are numeric identifiers from NCBI\n",
"# We'll use these directly since it appears no human-readable gene symbols are available in the annotation\n",
"gene_mapping = mapping_data\n",
"\n",
"# Display the final mapping we'll use\n",
"print(\"\\nFinal gene mapping sample:\")\n",
"print(preview_df(gene_mapping, n=5))\n"
]
},
{
"cell_type": "markdown",
"id": "b2f994f0",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c27cb5a",
"metadata": {},
"outputs": [],
"source": [
"# 1. Analyze the gene identifiers and annotation to decide on mapping strategy\n",
"# From previous steps, we see the gene identifiers in the expression data are numeric IDs ('1', '2', '3', etc.)\n",
"# The gene annotation has 'ID', 'ENTREZ_GENE_ID', and 'SPOT_ID' columns\n",
"# The 'ID' in gene annotation corresponds to the probe IDs in the expression data\n",
"# The 'ENTREZ_GENE_ID' contains Entrez Gene IDs which we'll use as gene identifiers\n",
"\n",
"# 2. Create a gene mapping dataframe\n",
"gene_mapping = gene_annotation[['ID', 'ENTREZ_GENE_ID']].copy()\n",
"gene_mapping = gene_mapping.dropna(subset=['ENTREZ_GENE_ID'])\n",
"gene_mapping = gene_mapping.rename(columns={'ENTREZ_GENE_ID': 'Gene'})\n",
"\n",
"# Display the gene mapping\n",
"print(f\"Gene mapping shape: {gene_mapping.shape}\")\n",
"print(\"Sample of gene mapping dataframe:\")\n",
"print(preview_df(gene_mapping))\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"# We need to handle the issue with apply_gene_mapping which expects gene symbols\n",
"\n",
"# First, select only the rows in gene_mapping that correspond to probes in our gene_data\n",
"valid_mapping = gene_mapping[gene_mapping['ID'].isin(gene_data.index)]\n",
"print(f\"Number of probes in gene_data that have mapping: {len(valid_mapping)}\")\n",
"\n",
"# Create a simpler mapping function that preserves the Entrez Gene IDs\n",
"def map_probes_to_genes(expression_df, mapping_df):\n",
" \"\"\"Maps probe-level expression to gene-level expression using Entrez Gene IDs.\"\"\"\n",
" # Ensure mapping only includes probes that exist in expression data\n",
" mapping_df = mapping_df[mapping_df['ID'].isin(expression_df.index)].copy()\n",
" \n",
" # Set the probe ID as index for joining\n",
" mapping_df.set_index('ID', inplace=True)\n",
" \n",
" # Get all sample columns (all columns in expression_df)\n",
" sample_cols = expression_df.columns.tolist()\n",
" \n",
" # Create a mapping dictionary from probe to gene\n",
" probe_to_gene = mapping_df['Gene'].to_dict()\n",
" \n",
" # Initialize a dictionary to collect gene expression values\n",
" gene_expression = {}\n",
" gene_counts = {}\n",
" \n",
" # Process each probe's expression\n",
" for probe_id, row in expression_df.iterrows():\n",
" if probe_id in probe_to_gene:\n",
" gene = probe_to_gene[probe_id]\n",
" \n",
" # Initialize gene entry if not present\n",
" if gene not in gene_expression:\n",
" gene_expression[gene] = {col: 0 for col in sample_cols}\n",
" gene_counts[gene] = 0\n",
" \n",
" # Add this probe's expression to the gene\n",
" for col in sample_cols:\n",
" gene_expression[gene][col] += row[col]\n",
" \n",
" gene_counts[gene] += 1\n",
" \n",
" # Create a dataframe from the collected expression values\n",
" gene_df = pd.DataFrame.from_dict(gene_expression, orient='index')\n",
" \n",
" # Average the expression by the number of probes per gene\n",
" for gene, count in gene_counts.items():\n",
" gene_df.loc[gene] = gene_df.loc[gene] / count\n",
" \n",
" return gene_df\n",
"\n",
"# Apply the mapping function\n",
"gene_data = map_probes_to_genes(gene_data, gene_mapping)\n",
"\n",
"# Display the resulting gene expression data\n",
"print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
"print(\"First 5 gene IDs in the mapped data:\")\n",
"print(gene_data.index[:5])\n",
"print(\"Sample of gene expression data (first 5 genes, first 5 columns):\")\n",
"print(gene_data.iloc[:5, :5])\n",
"\n",
"# Create output directory if it doesn't exist\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"\n",
"# Save the gene expression data\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Gene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "cbe66119",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a02f1489",
"metadata": {},
"outputs": [],
"source": [
"# 1. Normalize gene symbols using NCBI database\n",
"print(\"Normalizing gene symbols...\")\n",
"gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
"print(\"First 10 normalized gene symbols:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Save the normalized gene data\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to: {out_gene_data_file}\")\n",
"\n",
"# 2. Extract and prepare clinical data from the matrix file\n",
"print(\"\\nPreparing clinical data...\")\n",
"\n",
"# Get the clinical data rows\n",
"_, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
"clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
"_, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
"\n",
"# Process clinical data using the parameters defined in Step 2\n",
"selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=0, # From Step 2: trait_row = 0\n",
" convert_trait=convert_trait, # Function defined in Step 2\n",
" age_row=None, # From Step 2: age_row = None\n",
" convert_age=None,\n",
" gender_row=None, # From Step 2: gender_row = None\n",
" convert_gender=None\n",
")\n",
"\n",
"print(\"Clinical data preview:\")\n",
"print(preview_df(selected_clinical_df))\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",
"# 3. Link clinical and genetic data\n",
"print(\"\\nLinking clinical and genetic data...\")\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
"if linked_data.shape[0] > 0 and linked_data.shape[1] > 5:\n",
" print(linked_data.iloc[:5, :5])\n",
"else:\n",
" print(linked_data)\n",
"\n",
"# 4. Handle missing values\n",
"print(\"\\nHandling missing values...\")\n",
"linked_data_clean = handle_missing_values(linked_data, trait)\n",
"print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n",
"\n",
"# 5. Check for bias in the dataset\n",
"print(\"\\nChecking for bias in dataset features...\")\n",
"is_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n",
"\n",
"# 6. Conduct final quality validation\n",
"note = \"This GSE57691 dataset contains gene expression data from patients with abdominal aortic aneurysm (AAA) and aortic occlusive disease (AOD) compared to control subjects. The dataset focuses on atherosclerosis-related vascular changes.\"\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_biased,\n",
" df=linked_data_clean,\n",
" note=note\n",
")\n",
"\n",
"# 7. Save the linked data if it's usable\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data_clean.to_csv(out_data_file, index=True)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset deemed not usable for associative studies. Linked data not saved.\")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
|