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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "6a80dcf7",
"metadata": {
"execution": {
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"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 = \"Stomach_Cancer\"\n",
"cohort = \"GSE147163\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Stomach_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Stomach_Cancer/GSE147163\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Stomach_Cancer/GSE147163.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Stomach_Cancer/gene_data/GSE147163.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Stomach_Cancer/clinical_data/GSE147163.csv\"\n",
"json_path = \"../../output/preprocess/Stomach_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "1849b95b",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3a397c4f",
"metadata": {
"execution": {
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}
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files in the cohort directory:\n",
"['GSE147163_family.soft.gz', 'GSE147163_series_matrix.txt.gz']\n",
"Identified SOFT files: ['GSE147163_family.soft.gz']\n",
"Identified matrix files: ['GSE147163_series_matrix.txt.gz']\n",
"\n",
"Background Information:\n",
"!Series_title\t\"Molecular subtypes in gastric cancer. [III]\"\n",
"!Series_summary\t\"We identified the molecular subtypes and conserved modules in gastric cancer by unsupervised clustering algorithm. We defined six molecular signatrues of gastric cancer associated with the biological heterogeneity of gastric cancer and clinical outcome of patients.\"\n",
"!Series_overall_design\t\"We obtained gene expression profile of gastrectomy samples from 401 gastric cancer patients by HumanHT-12 v3.0 Expression BeadChip array (Illumina). Total RNA was extracted from the fresh-frozen gastrectomy specimens at the Yonsei University Severance Hospital (South Korea) between 2000 and 2010.\"\n",
"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: gastric cancer']}\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": "a958ec33",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a4aefd84",
"metadata": {
"execution": {
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"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# This dataset appears to be a gene expression microarray data (HumanHT-12 v3.0 Expression BeadChip array)\n",
"# from the background information, so gene expression data should be available\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# From the sample characteristics, we only have 'tissue: gastric cancer'\n",
"# This indicates all samples are gastric cancer tissue, without control samples\n",
"# There is no explicit trait variable that differentiates between cases and controls\n",
"# There is also no age or gender information available in the sample characteristics\n",
"\n",
"trait_row = None # No trait variable that differentiates between cases and controls\n",
"age_row = None # No age information available\n",
"gender_row = None # No gender information available\n",
"\n",
"# 2.2 Data Type Conversion (defining functions even though they won't be used in this case)\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait value to binary (0 for control, 1 for case)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # No conversion rule needed as we don't have trait data\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to continuous\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # No conversion rule needed as we don't have age data\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract value after colon if present\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # No conversion rule needed as we don't have gender data\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort info\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 - Skip as trait_row is None\n",
"# No clinical feature extraction is needed as we don't have trait data\n"
]
},
{
"cell_type": "markdown",
"id": "19310415",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4e93b836",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T04:02:07.724773Z",
"iopub.status.busy": "2025-03-25T04:02:07.724667Z",
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"shell.execute_reply": "2025-03-25T04:02:08.025523Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First 20 gene/probe identifiers:\n",
"Index(['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050', 'ILMN_1343052',\n",
" 'ILMN_1343059', 'ILMN_1343061', 'ILMN_1343062', 'ILMN_1343063',\n",
" 'ILMN_1343064', 'ILMN_1343291', 'ILMN_1343295', 'ILMN_1343296',\n",
" 'ILMN_1343297', 'ILMN_1343298', 'ILMN_1343299', 'ILMN_1343301',\n",
" 'ILMN_1343302', 'ILMN_1343303', 'ILMN_1343304', 'ILMN_1343305'],\n",
" dtype='object', name='ID')\n",
"\n",
"Gene expression data shape: (49576, 50)\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": "9965a4bf",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fac0f971",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T04:02:08.027602Z",
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"outputs": [],
"source": [
"# These identifiers with prefix \"ILMN_\" are Illumina BeadArray probe IDs, not human gene symbols.\n",
"# They need to be mapped to human gene symbols for downstream analysis.\n",
"# The \"ILMN_\" prefix indicates these are from Illumina microarray platforms.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "e4c6e35a",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7d1d0685",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T04:02:08.031215Z",
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"shell.execute_reply": "2025-03-25T04:02:13.229171Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['ILMN_1725881', 'ILMN_1910180', 'ILMN_1804174', 'ILMN_1796063', 'ILMN_1811966'], 'nuID': ['rp13_p1x6D80lNLk3c', 'NEX0oqCV8.er4HVfU4', 'KyqQynMZxJcruyylEU', 'xXl7eXuF7sbPEp.KFI', '9ckqJrioiaej9_ajeQ'], 'Species': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Source': ['RefSeq', 'Unigene', 'RefSeq', 'RefSeq', 'RefSeq'], 'Search_Key': ['ILMN_44919', 'ILMN_127219', 'ILMN_139282', 'ILMN_5006', 'ILMN_38756'], 'Transcript': ['ILMN_44919', 'ILMN_127219', 'ILMN_139282', 'ILMN_5006', 'ILMN_38756'], 'ILMN_Gene': ['LOC23117', 'HS.575038', 'FCGR2B', 'TRIM44', 'LOC653895'], 'Source_Reference_ID': ['XM_933824.1', 'Hs.575038', 'XM_938851.1', 'NM_017583.3', 'XM_936379.1'], 'RefSeq_ID': ['XM_933824.1', nan, 'XM_938851.1', 'NM_017583.3', 'XM_936379.1'], 'Unigene_ID': [nan, 'Hs.575038', nan, nan, nan], 'Entrez_Gene_ID': [23117.0, nan, 2213.0, 54765.0, 653895.0], 'GI': [89040007.0, 10437021.0, 88952550.0, 29029528.0, 89033487.0], 'Accession': ['XM_933824.1', 'AK024680', 'XM_938851.1', 'NM_017583.3', 'XM_936379.1'], 'Symbol': ['LOC23117', nan, 'FCGR2B', 'TRIM44', 'LOC653895'], 'Protein_Product': ['XP_938917.1', nan, 'XP_943944.1', 'NP_060053.2', 'XP_941472.1'], 'Array_Address_Id': [1710221.0, 5900364.0, 2480717.0, 1300239.0, 4480719.0], 'Probe_Type': ['I', 'S', 'I', 'S', 'S'], 'Probe_Start': [122.0, 1409.0, 1643.0, 2901.0, 25.0], 'SEQUENCE': ['GGCTCCTCTTTGGGCTCCTACTGGAATTTATCAGCCATCAGTGCATCTCT', 'ACACCTTCAGGAGGGAAGCCCTTATTTCTGGGTTGAACTCCCCTTCCATG', 'TAGGGGCAATAGGCTATACGCTACAGCCTAGGTGTGTAGTAGGCCACACC', 'CCTGCCTGTCTGCCTGTGACCTGTGTACGTATTACAGGCTTTAGGACCAG', 'CTAGCAGGGAGCGGTGAGGGAGAGCGGCTGGATTTCTTGCGGGATCTGCA'], 'Chromosome': ['16', nan, nan, '11', nan], 'Probe_Chr_Orientation': ['-', nan, nan, '+', nan], 'Probe_Coordinates': ['21766363-21766363:21769901-21769949', nan, nan, '35786070-35786119', nan], 'Cytoband': ['16p12.2a', nan, '1q23.3b', '11p13a', '10q11.23b'], 'Definition': ['PREDICTED: Homo sapiens KIAA0220-like protein, transcript variant 11 (LOC23117), mRNA.', 'Homo sapiens cDNA: FLJ21027 fis, clone CAE07110', 'PREDICTED: Homo sapiens Fc fragment of IgG, low affinity IIb, receptor (CD32) (FCGR2B), mRNA.', 'Homo sapiens tripartite motif-containing 44 (TRIM44), mRNA.', 'PREDICTED: Homo sapiens similar to protein geranylgeranyltransferase type I, beta subunit (LOC653895), mRNA.'], 'Ontology_Component': [nan, nan, nan, 'intracellular [goid 5622] [evidence IEA]', nan], 'Ontology_Process': [nan, nan, nan, nan, nan], 'Ontology_Function': [nan, nan, nan, 'zinc ion binding [goid 8270] [evidence IEA]; metal ion binding [goid 46872] [evidence IEA]', nan], 'Synonyms': [nan, nan, nan, 'MGC3490; MC7; HSA249128; DIPB', nan], 'Obsolete_Probe_Id': [nan, nan, nan, 'MGC3490; MC7; HSA249128; DIPB', nan], 'GB_ACC': ['XM_933824.1', 'AK024680', 'XM_938851.1', 'NM_017583.3', 'XM_936379.1']}\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": "af12def0",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ec7a7d34",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T04:02:13.231048Z",
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"shell.execute_reply": "2025-03-25T04:02:13.452588Z"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created gene mapping with 36157 entries.\n",
"First few rows of mapping data:\n",
" ID Gene\n",
"0 ILMN_1725881 LOC23117\n",
"2 ILMN_1804174 FCGR2B\n",
"3 ILMN_1796063 TRIM44\n",
"4 ILMN_1811966 LOC653895\n",
"5 ILMN_1668162 DGAT2L3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Converted to gene expression data with 19120 genes and 50 samples.\n",
"First few genes:\n",
"Index(['A1BG', 'A1CF', 'A26A1', 'A26B1', 'A26C1B', 'A26C3', 'A2BP1', 'A2M',\n",
" 'A2ML1', 'A3GALT2'],\n",
" dtype='object', name='Gene')\n",
"\n",
"Preview of gene expression data:\n",
" GSM4419484 GSM4419485 GSM4419486 GSM4419487 GSM4419488\n",
"Gene \n",
"A1BG 9.853618 10.537068 9.865281 9.937021 9.831023\n",
"A1CF 23.955097 20.615845 15.110226 14.553892 18.103539\n",
"A26A1 9.452464 9.388353 9.605953 9.653673 9.743757\n",
"A26B1 4.776131 4.864010 4.798284 4.826584 4.919554\n",
"A26C1B 4.971237 4.679716 4.754202 4.698224 4.954362\n"
]
}
],
"source": [
"# 1. Determine which columns in the gene annotation contain identifiers and symbols\n",
"# Looking at the gene annotation preview and gene expression data:\n",
"# - The gene expression data index contains probe IDs like 'ILMN_1343048'\n",
"# - The gene annotation has 'ID' column with similar values like 'ILMN_1725881'\n",
"# - The gene symbols appear to be in the 'Symbol' column\n",
"\n",
"# 2. Get gene mapping dataframe\n",
"probe_col = 'ID' # Column containing probe identifiers\n",
"symbol_col = 'Symbol' # Column containing gene symbols\n",
"\n",
"# Extract mapping data and handle potential issues\n",
"mapping_df = get_gene_mapping(gene_annotation, probe_col, symbol_col)\n",
"\n",
"print(f\"Created gene mapping with {len(mapping_df)} entries.\")\n",
"print(\"First few rows of mapping data:\")\n",
"print(mapping_df.head())\n",
"\n",
"# 3. Apply gene mapping to convert probe measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"print(f\"\\nConverted to gene expression data with {len(gene_data)} genes and {gene_data.shape[1]} samples.\")\n",
"print(\"First few genes:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Preview the first few rows of gene expression data\n",
"print(\"\\nPreview of gene expression data:\")\n",
"print(gene_data.iloc[:5, :5])\n"
]
},
{
"cell_type": "markdown",
"id": "afb694ef",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data shape: (18326, 50)\n",
"First few normalized gene symbols: ['A1BG', 'A1CF', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS', 'AACS']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Stomach_Cancer/gene_data/GSE147163.csv\n",
"Created linked data with dummy trait column. Shape: (50, 18327)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (50, 18327)\n",
"Trait distribution assessment: dataset is biased (all samples are gastric cancer)\n",
"Data quality check result: Not usable\n",
"Data not saved due to quality issues (no trait differentiation available).\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the obtained gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"print(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\n",
"\n",
"# Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Create a minimal DataFrame with a dummy trait column since no clinical data is available\n",
"# For datasets where all samples are the same class (all gastric cancer), we need to mark them all the same\n",
"linked_data = normalized_gene_data.T.copy()\n",
"linked_data[trait] = 1 # All samples marked as cases (gastric cancer)\n",
"print(f\"Created linked data with dummy trait column. Shape: {linked_data.shape}\")\n",
"\n",
"# 3. Handle missing values in gene 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",
"# 4. Since all samples have the same trait value (all are gastric cancer), the dataset is biased by definition\n",
"is_trait_biased = True\n",
"print(f\"Trait distribution assessment: dataset is biased (all samples are gastric cancer)\")\n",
"\n",
"# 5. Conduct quality check and save the cohort information\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True,\n",
" is_trait_available=False, # Although we created a dummy trait column, the actual trait data isn't available\n",
" is_biased=is_trait_biased, \n",
" df=linked_data,\n",
" note=\"Dataset contains gene expression data from gastric cancer samples but lacks control samples or trait differentiation.\"\n",
")\n",
"\n",
"# 6. No need to save the linked data as it's not usable for trait association\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 not saved due to quality issues (no trait differentiation available).\")"
]
}
],
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