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{
"cells": [
{
"cell_type": "code",
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"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 = \"Stomach_Cancer\"\n",
"cohort = \"GSE98708\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Stomach_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Stomach_Cancer/GSE98708\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Stomach_Cancer/GSE98708.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Stomach_Cancer/gene_data/GSE98708.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Stomach_Cancer/clinical_data/GSE98708.csv\"\n",
"json_path = \"../../output/preprocess/Stomach_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "f45a460f",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e95fdc08",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files in the cohort directory:\n",
"['GSE98708_family.soft.gz', 'GSE98708_series_matrix.txt.gz']\n",
"Identified SOFT files: ['GSE98708_family.soft.gz']\n",
"Identified matrix files: ['GSE98708_series_matrix.txt.gz']\n",
"\n",
"Background Information:\n",
"!Series_title\t\"Expression profiling of frozen primary and patient derived xenograft gastric cancer\"\n",
"!Series_summary\t\"Expression profiling of frozen primary and patient derived xenograft gastric cancer\"\n",
"!Series_overall_design\t\"Expression profiling of frozen primary and patient derived xenograft gastric cancer\"\n",
"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: gastric cancer'], 1: ['sample type: PDX', 'sample type: primary tumor'], 2: ['patient id: GTR0222', 'patient id: GTR0227', 'patient id: GTR0230', 'patient id: GTR0233', 'patient id: GTR0244', 'patient id: GTR0245', 'patient id: GTR0247', 'patient id: GTR0249', 'patient id: GTR0255', 'patient id: GTR0259', 'patient id: GTR0263', 'patient id: GTR0220', 'patient id: GTR0102', 'patient id: GTR0103', 'patient id: GTR0105', 'patient id: GTR0124', 'patient id: GTR0145', 'patient id: GTR0164', 'patient id: GTR0193', 'patient id: GTR0194', 'patient id: GTR0202', 'patient id: GTR0207', 'patient id: GTR0208', 'patient id: GTR0213', 'patient id: GTR0032', 'patient id: GTR0060', 'patient id: GTR0165', 'patient id: GTR0181', 'patient id: GTR0044', 'patient id: GTR0219']}\n"
]
}
],
"source": [
"# 1. Let's first list the directory contents to understand what files are available\n",
"import os\n",
"\n",
"print(\"Files in the cohort directory:\")\n",
"files = os.listdir(in_cohort_dir)\n",
"print(files)\n",
"\n",
"# Adapt file identification to handle different naming patterns\n",
"soft_files = [f for f in files if 'soft' in f.lower() or '.soft' in f.lower() or '_soft' in f.lower()]\n",
"matrix_files = [f for f in files if 'matrix' in f.lower() or '.matrix' in f.lower() or '_matrix' in f.lower()]\n",
"\n",
"# If no files with these patterns are found, look for alternative file types\n",
"if not soft_files:\n",
" soft_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n",
"if not matrix_files:\n",
" matrix_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n",
"\n",
"print(\"Identified SOFT files:\", soft_files)\n",
"print(\"Identified matrix files:\", matrix_files)\n",
"\n",
"# Use the first files found, if any\n",
"if len(soft_files) > 0 and len(matrix_files) > 0:\n",
" soft_file = os.path.join(in_cohort_dir, soft_files[0])\n",
" matrix_file = os.path.join(in_cohort_dir, matrix_files[0])\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(\"\\nBackground Information:\")\n",
" print(background_info)\n",
" print(\"\\nSample Characteristics Dictionary:\")\n",
" print(sample_characteristics_dict)\n",
"else:\n",
" print(\"No appropriate files found in the directory.\")\n"
]
},
{
"cell_type": "markdown",
"id": "815926e9",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e5c62701",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T04:03:07.159916Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of selected clinical data:\n",
"{0: [1.0]}\n",
"Clinical data saved to ../../output/preprocess/Stomach_Cancer/clinical_data/GSE98708.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information and summary, the dataset appears to be expression profiling of gastric cancer\n",
"# samples, 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 (Stomach Cancer)\n",
"# Looking at the sample characteristics, row 0 contains 'tissue: gastric cancer'\n",
"# This confirms all samples are gastric cancer tissue\n",
"trait_row = 0\n",
"\n",
"# Age data is not available in the sample characteristics dictionary\n",
"age_row = None\n",
"\n",
"# Gender data is not available in the sample characteristics dictionary\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert the trait value to binary (1 for gastric cancer, 0 for normal)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Handle non-string types\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" \n",
" # Check if it's related to gastric cancer\n",
" if 'gastric cancer' in value:\n",
" return 1\n",
" elif 'normal' in value:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Function to convert age to continuous value (not used in this dataset)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" if not isinstance(value, str):\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",
" \"\"\"Function to convert gender to binary (0 for female, 1 for male) (not used in this dataset)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" \n",
" if value in ['male', 'm']:\n",
" return 1\n",
" elif value in ['female', 'f']:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# is_trait_available is determined by whether trait_row is None\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort info (initial filtering)\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",
"# Since trait_row is not None, we proceed with clinical feature extraction\n",
"if trait_row is not None:\n",
" try:\n",
" # Load the clinical data from the provided dictionary instead of parsing the file again\n",
" # Use whatever clinical_data source that was provided in the previous step\n",
" # For this dataset, we know the clinical characteristics from the dictionary already shown\n",
" clinical_data = pd.DataFrame()\n",
" \n",
" # Add the sample characteristic row for trait (gastric cancer)\n",
" clinical_data.loc[trait_row, 0] = 'tissue: gastric cancer'\n",
" \n",
" # Extract clinical features\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 dataframe\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical data:\")\n",
" print(preview)\n",
" \n",
" # Save the clinical data to CSV\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" except Exception as e:\n",
" print(f\"Error processing clinical data: {e}\")\n",
" # If clinical data processing fails, update metadata\n",
" is_trait_available = False\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"
]
},
{
"cell_type": "markdown",
"id": "ad93723e",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "088e6dda",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First 20 gene/probe identifiers:\n",
"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",
"\n",
"Gene expression data shape: (47323, 102)\n"
]
}
],
"source": [
"# Use the helper function to get the proper file paths\n",
"soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Extract gene expression data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file_path)\n",
" \n",
" # Print the first 20 row IDs (gene or probe identifiers)\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
" \n",
" # Print shape to understand the dataset dimensions\n",
" print(f\"\\nGene expression data shape: {gene_data.shape}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "6d90a9a3",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8c602d86",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# These identifiers (ILMN_) are Illumina probe IDs, not standard human gene symbols.\n",
"# They need to be mapped to official gene symbols for proper biological interpretation.\n",
"# ILMN_ prefix indicates these are Illumina BeadArray probe identifiers.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "0de84677",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "43133822",
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"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",
"try:\n",
" # Use the correct variable name from previous steps\n",
" gene_annotation = get_gene_annotation(soft_file_path)\n",
" \n",
" # 2. Preview the gene annotation dataframe\n",
" print(\"Gene annotation preview:\")\n",
" print(preview_df(gene_annotation))\n",
" \n",
"except UnicodeDecodeError as e:\n",
" print(f\"Unicode decoding error: {e}\")\n",
" print(\"Trying alternative approach...\")\n",
" \n",
" # Read the file with Latin-1 encoding which is more permissive\n",
" import gzip\n",
" import pandas as pd\n",
" \n",
" # Manually read the file line by line with error handling\n",
" data_lines = []\n",
" with gzip.open(soft_file_path, 'rb') as f:\n",
" for line in f:\n",
" # Skip lines starting with prefixes we want to filter out\n",
" line_str = line.decode('latin-1')\n",
" if not line_str.startswith('^') and not line_str.startswith('!') and not line_str.startswith('#'):\n",
" data_lines.append(line_str)\n",
" \n",
" # Create dataframe from collected lines\n",
" if data_lines:\n",
" gene_data_str = '\\n'.join(data_lines)\n",
" gene_annotation = pd.read_csv(pd.io.common.StringIO(gene_data_str), sep='\\t', low_memory=False)\n",
" print(\"Gene annotation preview (alternative method):\")\n",
" print(preview_df(gene_annotation))\n",
" else:\n",
" print(\"No valid gene annotation data found after filtering.\")\n",
" gene_annotation = pd.DataFrame()\n",
" \n",
"except Exception as e:\n",
" print(f\"Error extracting gene annotation data: {e}\")\n",
" gene_annotation = pd.DataFrame()\n"
]
},
{
"cell_type": "markdown",
"id": "f957bc19",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3aaf341e",
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"execution": {
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"shell.execute_reply": "2025-03-25T04:03:17.985469Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping preview (probe ID to gene symbol):\n",
"{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052', 'ILMN_1343059'], 'Gene': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low', 'phage_lambda_genome:low', 'thrB']}\n",
"\n",
"Gene expression data after mapping (first few genes):\n",
"{'GSM2610417': [246.4, 340.3, 267.6, 398.4, 207.5], 'GSM2610418': [246.60000000000002, 324.1, 333.3, 392.0, 95.3], 'GSM2610419': [223.3, 330.7, 280.1, 484.2, 137.9], 'GSM2610420': [262.1, 1330.2, 291.9, 369.7, 358.5], 'GSM2610421': [229.5, 1021.2, 396.3, 422.7, 367.4], 'GSM2610422': [281.20000000000005, 344.9, 386.6, 398.79999999999995, 322.2], 'GSM2610423': [235.0, 1637.0, 311.1, 352.4, 587.1], 'GSM2610424': [241.89999999999998, 858.5, 324.29999999999995, 348.7, 718.5], 'GSM2610425': [247.5, 1440.1999999999998, 364.0, 389.0, 575.5], 'GSM2610426': [265.8, 310.8, 247.7, 336.7, 1204.7], 'GSM2610427': [254.79999999999998, 732.6, 290.6, 354.9, 715.8], 'GSM2610428': [258.5, 323.3, 238.6, 354.1, 242.4], 'GSM2610429': [363.1, 3131.3999999999996, 345.6, 456.2, 381.0], 'GSM2610430': [293.4, 482.0, 329.5, 420.0, 255.4], 'GSM2610431': [244.3, 347.70000000000005, 318.4, 393.7, 286.9], 'GSM2610432': [249.6, 3272.2, 328.1, 393.6, 581.2], 'GSM2610433': [241.4, 671.5, 317.8, 366.9, 389.3], 'GSM2610434': [247.2, 313.8, 353.6, 326.6, 378.5], 'GSM2610435': [226.7, 449.8, 295.0, 420.1, 646.5], 'GSM2610436': [248.6, 319.4, 265.0, 383.8, 334.4], 'GSM2610437': [226.7, 725.8, 296.4, 379.1, 185.2], 'GSM2610438': [273.0, 725.4000000000001, 328.40000000000003, 436.5, 449.3], 'GSM2610439': [249.8, 9212.7, 338.0, 422.8, 761.8], 'GSM2610440': [277.29999999999995, 797.0, 329.4, 450.0, 675.9], 'GSM2610441': [314.5, 1810.2, 356.6, 467.2, 330.6], 'GSM2610442': [210.5, 761.5, 341.8, 381.7, 283.7], 'GSM2610443': [218.5, 313.5, 341.7, 371.9, 273.2], 'GSM2610444': [305.9, 356.1, 355.8, 421.6, 696.1], 'GSM2610445': [223.4, 2342.0, 314.6, 406.6, 282.0], 'GSM2610446': [253.3, 1722.9, 270.2, 373.7, 148.1], 'GSM2610447': [217.89999999999998, 1418.9, 266.5, 342.8, 276.8], 'GSM2610448': [220.2, 2376.0, 291.7, 336.8, 210.5], 'GSM2610449': [255.2, 358.90000000000003, 315.5, 321.9, 343.3], 'GSM2610450': [231.0, 318.2, 317.7, 354.2, 180.5], 'GSM2610451': [443.8, 1184.4, 431.6, 606.8, 640.5], 'GSM2610452': [292.79999999999995, 513.6, 400.9, 444.2, 684.2], 'GSM2610453': [300.2, 426.6, 387.1, 422.0, 1020.8], 'GSM2610454': [339.6, 1700.4, 405.1, 488.09999999999997, 770.3], 'GSM2610455': [321.2, 619.5, 409.9, 503.1, 265.7], 'GSM2610456': [301.79999999999995, 425.0, 375.5, 428.8, 238.2], 'GSM2610457': [286.1, 429.7, 356.9, 508.9, 510.7], 'GSM2610458': [331.5, 2544.1, 397.29999999999995, 489.09999999999997, 414.7], 'GSM2610459': [328.3, 761.1, 424.0, 548.1999999999999, 502.3], 'GSM2610460': [268.9, 2103.6, 370.70000000000005, 444.9, 674.9], 'GSM2610461': [344.8, 438.5, 409.6, 521.4, 655.7], 'GSM2610462': [261.7, 3788.0, 387.4, 512.2, 276.1], 'GSM2610463': [284.1, 351.9, 335.3, 449.5, 325.4], 'GSM2610464': [383.70000000000005, 444.6, 436.5, 541.3, 313.5], 'GSM2610465': [342.9, 792.8, 368.0, 492.5, 242.6], 'GSM2610466': [331.2, 409.90000000000003, 429.5, 1173.5, 201.4], 'GSM2610467': [336.8, 2114.5, 493.70000000000005, 528.8, 591.1], 'GSM2610468': [361.6, 1647.6, 477.3, 510.20000000000005, 1403.4], 'GSM2610469': [325.1, 1081.4, 407.8, 516.3, 400.7], 'GSM2610470': [316.1, 2580.1000000000004, 373.6, 523.9000000000001, 600.2], 'GSM2610471': [302.6, 3471.9, 354.1, 452.3, 492.0], 'GSM2610472': [306.3, 1579.2, 346.1, 476.4, 439.7], 'GSM2610473': [273.8, 768.0, 376.9, 546.7, 614.4], 'GSM2610474': [279.6, 509.5, 368.3, 462.2, 288.3], 'GSM2610475': [295.1, 325.6, 361.5, 529.0, 581.6], 'GSM2610476': [272.9, 406.6, 340.5, 472.7, 289.7], 'GSM2610477': [329.5, 410.6, 340.6, 498.5, 747.5], 'GSM2610478': [417.5, 439.90000000000003, 437.5, 531.7, 485.6], 'GSM2610479': [414.6, 958.2, 496.3, 690.2, 397.2], 'GSM2610480': [394.7, 501.4, 472.5, 608.0, 461.5], 'GSM2610481': [347.3, 527.5, 480.2, 580.4, 309.0], 'GSM2610482': [396.20000000000005, 568.7, 558.5, 602.9, 346.8], 'GSM2610483': [416.7, 1108.8, 469.9, 615.0, 212.6], 'GSM2610484': [399.5, 507.9, 485.8, 886.5999999999999, 511.2], 'GSM2610485': [376.8, 579.4, 457.0, 598.9000000000001, 704.2], 'GSM2610486': [342.4, 418.5, 463.1, 588.3, 294.6], 'GSM2610487': [369.4, 446.7, 515.0, 656.7, 507.8], 'GSM2610488': [462.5, 1228.7, 535.4, 764.4, 684.8], 'GSM2610489': [314.1, 2193.8, 526.1, 685.9, 557.1], 'GSM2610490': [401.3, 4018.7, 528.3, 710.1, 764.8], 'GSM2610491': [394.1, 606.2, 466.6, 643.2, 903.6], 'GSM2610492': [402.70000000000005, 476.5, 470.6, 860.9, 308.1], 'GSM2610493': [414.9, 632.0, 475.9, 620.4, 367.8], 'GSM2610494': [429.79999999999995, 527.8, 477.5, 748.6, 378.4], 'GSM2610495': [356.6, 1705.2, 411.4, 532.3, 213.2], 'GSM2610496': [310.1, 2395.9, 422.29999999999995, 511.29999999999995, 356.2], 'GSM2610497': [346.5, 426.2, 363.9, 559.4, 276.8], 'GSM2610498': [301.9, 1792.7, 426.6, 470.4, 511.1], 'GSM2610499': [263.2, 1975.9, 361.7, 423.2, 869.2], 'GSM2610500': [347.9, 698.7, 393.9, 497.2, 423.3], 'GSM2610501': [267.5, 1912.7, 341.6, 496.40000000000003, 175.1], 'GSM2610502': [313.5, 1284.8, 381.0, 548.4, 1024.7], 'GSM2610503': [406.20000000000005, 485.79999999999995, 427.8, 565.8, 255.6], 'GSM2610504': [304.5, 1183.3999999999999, 323.4, 566.4, 310.0], 'GSM2610505': [341.0, 1177.0, 404.4, 573.4, 256.7], 'GSM2610506': [406.1, 457.5, 398.5, 473.0, 263.7], 'GSM2610507': [322.8, 1984.8, 416.4, 565.6, 655.9], 'GSM2610508': [372.8, 456.4, 402.8, 516.1, 342.3], 'GSM2610509': [379.4, 514.9, 381.0, 581.5, 558.4], 'GSM2610510': [263.9, 456.1, 366.6, 474.8, 418.4], 'GSM2610511': [319.0, 5572.6, 400.1, 770.8, 613.8], 'GSM2610512': [337.8, 7739.0, 437.70000000000005, 573.4, 743.6], 'GSM2610513': [377.70000000000005, 433.0, 435.9, 497.90000000000003, 638.6], 'GSM2610514': [297.4, 394.8, 477.3, 545.9, 394.8], 'GSM2610515': [342.6, 460.1, 446.4, 523.0, 135.7], 'GSM2610516': [360.5, 2514.3999999999996, 400.9, 519.1, 1333.5], 'GSM2610517': [340.9, 668.6, 376.0, 536.1, 468.5], 'GSM2610518': [317.6, 2906.0, 392.1, 529.3, 452.5]}\n",
"Mapped gene data shape: (21464, 102)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Normalized gene data shape: (20259, 102)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Stomach_Cancer/gene_data/GSE98708.csv\n"
]
}
],
"source": [
"# 1. Observing the gene identifiers and annotation data:\n",
"# In gene expression data, the indices are in the format \"ILMN_XXXXXXX\"\n",
"# In the gene annotation data, the \"ID\" column contains this same identifier format\n",
"# The \"Symbol\" column appears to contain the gene symbols we need to map to\n",
"\n",
"# 2. Extract the gene mapping dataframe\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
"\n",
"# Print a preview of the mapping\n",
"print(\"Gene mapping preview (probe ID to gene symbol):\")\n",
"print(preview_df(gene_mapping))\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"# This divides probe values among multiple genes and sums contributions for each gene\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Check the resulting gene expression data\n",
"print(\"\\nGene expression data after mapping (first few genes):\")\n",
"print(preview_df(gene_data))\n",
"print(f\"Mapped gene data shape: {gene_data.shape}\")\n",
"\n",
"# Normalize gene symbols (handle case variations and synonyms)\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"\\nNormalized gene data shape: {gene_data.shape}\")\n",
"\n",
"# Save the processed 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": "ffdb41e9",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b85ee988",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T04:03:17.987135Z",
"iopub.status.busy": "2025-03-25T04:03:17.987013Z",
"iopub.status.idle": "2025-03-25T04:03:24.317019Z",
"shell.execute_reply": "2025-03-25T04:03:24.316519Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data shape: (20259, 102)\n",
"First few normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS']\n",
"Loaded clinical data with shape: (1, 1)\n",
"Error in processing clinical data: 102 columns passed, passed data had 1 columns\n",
"Created default clinical features with shape: (1, 102)\n",
"Linked data shape: (102, 20260)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (102, 20260)\n",
"Quartiles for 'Stomach_Cancer':\n",
" 25%: 1.0\n",
" 50% (Median): 1.0\n",
" 75%: 1.0\n",
"Min: 1.0\n",
"Max: 1.0\n",
"The distribution of the feature 'Stomach_Cancer' in this dataset is severely biased.\n",
"\n",
"Data quality check result: Not usable\n",
"Data quality check failed. The dataset is not suitable for association studies.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the obtained gene expression data\n",
"# (This step was already completed in the previous step)\n",
"print(f\"Normalized gene data shape: {gene_data.shape}\")\n",
"print(f\"First few normalized gene symbols: {list(gene_data.index[:10])}\")\n",
"\n",
"# 2. Load the previously saved clinical data and prepare it for linking\n",
"try:\n",
" # Read the clinical CSV file\n",
" clinical_data = pd.read_csv(out_clinical_data_file)\n",
" print(f\"Loaded clinical data with shape: {clinical_data.shape}\")\n",
" \n",
" # Create properly structured clinical features for linking\n",
" # The geo_select_clinical_features function should have created a DataFrame with traits as rows\n",
" # But we'll verify and fix the structure if needed\n",
" if trait not in clinical_data.columns:\n",
" # Create a properly formatted clinical features DataFrame with trait as row\n",
" clinical_features = pd.DataFrame(index=[trait], data=[clinical_data.iloc[0].values], \n",
" columns=gene_data.columns)\n",
" print(f\"Restructured clinical features with shape: {clinical_features.shape}\")\n",
" else:\n",
" # If the column exists, transpose to have traits as rows\n",
" clinical_features = clinical_data.set_index(trait).T\n",
" clinical_features = pd.DataFrame(index=[trait], data=[clinical_features.iloc[0].values], \n",
" columns=gene_data.columns)\n",
"except Exception as e:\n",
" print(f\"Error in processing clinical data: {e}\")\n",
" # Create a DataFrame with all samples classified as cancer (trait = 1)\n",
" # Since the sample characteristics indicated all are gastric cancer\n",
" clinical_features = pd.DataFrame(index=[trait], data=[[1.0] * len(gene_data.columns)], \n",
" columns=gene_data.columns)\n",
" print(f\"Created default clinical features with shape: {clinical_features.shape}\")\n",
"\n",
"# Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# 3. Handle missing values systematically\n",
"# Since we know trait values are all 1, create a direct column\n",
"linked_data[trait] = 1.0 # Explicitly add the trait column\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 4. Evaluate bias in trait and demographic features\n",
"is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 5. Conduct final quality validation and save metadata\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, # We determined earlier that trait data is available (all samples are cancer)\n",
" is_biased=is_trait_biased, \n",
" df=linked_data,\n",
" note=\"Dataset contains gene expression data from gastric cancer samples. All samples are cancer (trait=1).\"\n",
")\n",
"\n",
"# 6. Save the linked data if it's usable\n",
"print(f\"Data quality check result: {'Usable' if is_usable else 'Not usable'}\")\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(f\"Data quality check failed. The dataset is not suitable for association studies.\")"
]
}
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|