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{
"cells": [
{
"cell_type": "code",
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"execution": {
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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 = \"Coronary_artery_disease\"\n",
"cohort = \"GSE234398\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Coronary_artery_disease\"\n",
"in_cohort_dir = \"../../input/GEO/Coronary_artery_disease/GSE234398\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Coronary_artery_disease/GSE234398.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Coronary_artery_disease/gene_data/GSE234398.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Coronary_artery_disease/clinical_data/GSE234398.csv\"\n",
"json_path = \"../../output/preprocess/Coronary_artery_disease/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "1c82b7fc",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2c0f82b2",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Analysis of gene expression of LPS-stimulated monocyte from CAD patients\"\n",
"!Series_summary\t\"Data for the publication 'Identification of a Gene Network Driving the Attenuated Monocyte Response to Lipopolysaccharide of Hypertensive Coronary Artery Disease Patients'.\"\n",
"!Series_summary\t\"Dissection of the impact of CVD risk factors on monocyte phenotype at the gene expression level, and in particular on their response to trauma and infection response.\"\n",
"!Series_summary\t\"For any questions about the dataset, please contact Erik Biessen‘s Lab, Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands\"\n",
"!Series_overall_design\t\"Total RNA obtained from LPS stimulated monocytes of CAD patients.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['cell type: monocytes'], 1: ['Sex: male', 'Sex: female'], 2: ['age: 78', 'age: 50', 'age: 67', 'age: 74', 'age: 60', 'age: 72', 'age: 73', 'age: 77', 'age: 56', 'age: 51', 'age: 66', 'age: 65', 'age: 63', 'age: 71', 'age: 57', 'age: 75', 'age: 64', 'age: 39', 'age: 40']}\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": "f5184adf",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b8fcadee",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical Data Preview:\n",
"{'GSM7466724': [1.0, 78.0, 1.0], 'GSM7466725': [1.0, 78.0, 1.0], 'GSM7466726': [1.0, 50.0, 0.0], 'GSM7466727': [1.0, 67.0, 0.0], 'GSM7466728': [1.0, 74.0, 0.0], 'GSM7466729': [1.0, 60.0, 1.0], 'GSM7466730': [1.0, 72.0, 1.0], 'GSM7466731': [1.0, 67.0, 1.0], 'GSM7466732': [1.0, 67.0, 1.0], 'GSM7466733': [1.0, 73.0, 1.0], 'GSM7466734': [1.0, 77.0, 0.0], 'GSM7466735': [1.0, 78.0, 0.0], 'GSM7466736': [1.0, 56.0, 1.0], 'GSM7466737': [1.0, 51.0, 1.0], 'GSM7466738': [1.0, 78.0, 1.0], 'GSM7466739': [1.0, 66.0, 1.0], 'GSM7466740': [1.0, 65.0, 1.0], 'GSM7466741': [1.0, 51.0, 1.0], 'GSM7466742': [1.0, 63.0, 0.0], 'GSM7466743': [1.0, 60.0, 0.0], 'GSM7466744': [1.0, 71.0, 1.0], 'GSM7466745': [1.0, 57.0, 0.0], 'GSM7466746': [1.0, 73.0, 1.0], 'GSM7466747': [1.0, 75.0, 0.0], 'GSM7466748': [1.0, 72.0, 0.0], 'GSM7466749': [1.0, 74.0, 0.0], 'GSM7466750': [1.0, 64.0, 0.0], 'GSM7466751': [1.0, 39.0, 1.0], 'GSM7466752': [1.0, 78.0, 0.0], 'GSM7466753': [1.0, 57.0, 1.0], 'GSM7466754': [1.0, 74.0, 0.0], 'GSM7466755': [1.0, 75.0, 0.0], 'GSM7466756': [1.0, 67.0, 0.0], 'GSM7466757': [1.0, 63.0, 0.0], 'GSM7466758': [1.0, 71.0, 1.0], 'GSM7466759': [1.0, 72.0, 1.0], 'GSM7466760': [1.0, 40.0, 1.0], 'GSM7466761': [1.0, 63.0, 1.0], 'GSM7466762': [1.0, 73.0, 0.0], 'GSM7466763': [1.0, 77.0, 1.0], 'GSM7466764': [1.0, 50.0, 0.0], 'GSM7466765': [1.0, 73.0, 0.0], 'GSM7466766': [1.0, 72.0, 1.0], 'GSM7466767': [1.0, 56.0, 1.0], 'GSM7466768': [1.0, 56.0, 0.0], 'GSM7466769': [1.0, 64.0, 0.0], 'GSM7466770': [1.0, 66.0, 1.0], 'GSM7466771': [1.0, 65.0, 1.0], 'GSM7466772': [1.0, 63.0, 1.0], 'GSM7466773': [1.0, 56.0, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Coronary_artery_disease/clinical_data/GSE234398.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains gene expression data from LPS-stimulated monocytes\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# Trait: From the background info, this dataset is specifically about CAD patients\n",
"# We can use row 0 to represent all samples as having CAD (even though it doesn't explicitly state CAD)\n",
"trait_row = 0 # All samples are CAD patients as per the background information\n",
"\n",
"# Age: This is available in row 2\n",
"age_row = 2\n",
"\n",
"# Gender: This is available in row 1\n",
"gender_row = 1\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" # All samples are CAD patients according to the background info\n",
" return 1 # Binary: 1 = has CAD\n",
"\n",
"def convert_age(value):\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" try:\n",
" return float(value) # Convert to continuous numeric value\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip().lower()\n",
" if value == \"male\":\n",
" return 1\n",
" elif value == \"female\":\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata - initial filtering\n",
"is_trait_available = trait_row is not None\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",
" # Extract clinical features\n",
" clinical_selected = 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 data\n",
" preview = preview_df(clinical_selected)\n",
" print(\"Clinical Data Preview:\")\n",
" print(preview)\n",
" \n",
" # Save to file\n",
" clinical_selected.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "af810ea2",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
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"id": "9ab59c05",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOFT file: ../../input/GEO/Coronary_artery_disease/GSE234398/GSE234398_family.soft.gz\n",
"Matrix file: ../../input/GEO/Coronary_artery_disease/GSE234398/GSE234398_series_matrix.txt.gz\n",
"Found the matrix table marker at line 63\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape: (47231, 50)\n",
"First 20 gene/probe identifiers:\n",
"['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209', 'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229', 'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236', 'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253', 'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262']\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)\n"
]
},
{
"cell_type": "markdown",
"id": "25e3e77f",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
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"outputs": [],
"source": [
"# Examining the gene identifiers\n",
"# The identifiers with prefix 'ILMN_' are Illumina probe IDs, not human gene symbols\n",
"# These are probe IDs from Illumina microarray platforms and need to be mapped to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "2df6206b",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
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{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"Columns in gene annotation: ['ID', 'Species', 'Source', 'Search_Key', 'Transcript', 'ILMN_Gene', 'Source_Reference_ID', 'RefSeq_ID', 'Unigene_ID', 'Entrez_Gene_ID', 'GI', 'Accession', 'Symbol', 'Protein_Product', 'Probe_Id', 'Array_Address_Id', 'Probe_Type', 'Probe_Start', 'SEQUENCE', 'Chromosome', 'Probe_Chr_Orientation', 'Probe_Coordinates', 'Cytoband', 'Definition', 'Ontology_Component', 'Ontology_Process', 'Ontology_Function', 'Synonyms', 'Obsolete_Probe_Id', 'GB_ACC']\n",
"{'ID': ['ILMN_1343048', 'ILMN_1343049', 'ILMN_1343050'], 'Species': [nan, nan, nan], 'Source': [nan, nan, nan], 'Search_Key': [nan, nan, nan], 'Transcript': [nan, nan, nan], 'ILMN_Gene': [nan, nan, nan], 'Source_Reference_ID': [nan, nan, nan], 'RefSeq_ID': [nan, nan, nan], 'Unigene_ID': [nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan], 'GI': [nan, nan, nan], 'Accession': [nan, nan, nan], 'Symbol': ['phage_lambda_genome', 'phage_lambda_genome', 'phage_lambda_genome:low'], 'Protein_Product': [nan, nan, nan], 'Probe_Id': [nan, nan, nan], 'Array_Address_Id': [5090180.0, 6510136.0, 7560739.0], 'Probe_Type': [nan, nan, nan], 'Probe_Start': [nan, nan, nan], 'SEQUENCE': ['GAATAAAGAACAATCTGCTGATGATCCCTCCGTGGATCTGATTCGTGTAA', 'CCATGTGATACGAGGGCGCGTAGTTTGCATTATCGTTTTTATCGTTTCAA', 'CCGACAGATGTATGTAAGGCCAACGTGCTCAAATCTTCATACAGAAAGAT'], 'Chromosome': [nan, nan, nan], 'Probe_Chr_Orientation': [nan, nan, nan], 'Probe_Coordinates': [nan, nan, nan], 'Cytoband': [nan, nan, nan], 'Definition': [nan, nan, nan], 'Ontology_Component': [nan, nan, nan], 'Ontology_Process': [nan, nan, nan], 'Ontology_Function': [nan, nan, nan], 'Synonyms': [nan, nan, nan], 'Obsolete_Probe_Id': [nan, nan, nan], 'GB_ACC': [nan, nan, nan]}\n",
"\n",
"Examining mapping information (first 5 rows):\n",
"Row 0: ID=ILMN_1343048, Symbol=phage_lambda_genome\n",
"Row 1: ID=ILMN_1343049, Symbol=phage_lambda_genome\n",
"Row 2: ID=ILMN_1343050, Symbol=phage_lambda_genome:low\n",
"Row 3: ID=ILMN_1343052, Symbol=phage_lambda_genome:low\n",
"Row 4: ID=ILMN_1343059, Symbol=thrB\n",
"\n",
"Symbol column completeness: 44837/2409707 rows (1.86%)\n",
"\n",
"Columns identified for gene mapping:\n",
"- 'ID': Contains Illumina probe IDs (e.g., ILMN_*)\n",
"- 'Symbol': Contains gene symbols\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\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=3))\n",
"\n",
"# Examine the ID and Symbol columns that appear to contain the mapping information\n",
"print(\"\\nExamining mapping information (first 5 rows):\")\n",
"if 'ID' in gene_annotation.columns and 'Symbol' in gene_annotation.columns:\n",
" for i in range(min(5, len(gene_annotation))):\n",
" print(f\"Row {i}: ID={gene_annotation['ID'].iloc[i]}, Symbol={gene_annotation['Symbol'].iloc[i]}\")\n",
" \n",
" # Check the quality and completeness of the mapping\n",
" non_null_symbols = gene_annotation['Symbol'].notna().sum()\n",
" total_rows = len(gene_annotation)\n",
" print(f\"\\nSymbol column completeness: {non_null_symbols}/{total_rows} rows ({non_null_symbols/total_rows:.2%})\")\n",
" \n",
" # Identify the columns needed for gene mapping\n",
" print(\"\\nColumns identified for gene mapping:\")\n",
" print(\"- 'ID': Contains Illumina probe IDs (e.g., ILMN_*)\")\n",
" print(\"- 'Symbol': Contains gene symbols\")\n",
"else:\n",
" print(\"Error: Required mapping columns ('ID' and/or 'Symbol') not found in annotation data.\")\n",
" print(\"Available columns:\", gene_annotation.columns.tolist())\n"
]
},
{
"cell_type": "markdown",
"id": "f60fdb5b",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
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"execution_count": 7,
"id": "0abd3b48",
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"execution": {
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"shell.execute_reply": "2025-03-25T08:28:18.789454Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping dataframe shape: (44837, 2)\n",
"First 5 rows of gene mapping:\n",
" ID Gene\n",
"0 ILMN_1343048 phage_lambda_genome\n",
"1 ILMN_1343049 phage_lambda_genome\n",
"2 ILMN_1343050 phage_lambda_genome:low\n",
"3 ILMN_1343052 phage_lambda_genome:low\n",
"4 ILMN_1343059 thrB\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data shape: (47231, 50)\n",
"Gene data after mapping shape: (21372, 50)\n",
"First 10 gene symbols after mapping:\n",
"['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1', 'A3GALT2', 'A4GALT', 'A4GNT']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Coronary_artery_disease/gene_data/GSE234398.csv\n"
]
}
],
"source": [
"# 1. Based on the gene annotation data, I see that 'ID' contains Illumina probe IDs (like ILMN_*) which match \n",
"# the gene identifiers in the gene expression data, and 'Symbol' contains the gene symbols we need to map to.\n",
"\n",
"# 2. Extract the two columns from gene annotation for mapping\n",
"# Get the SOFT and matrix files again (for consistency with previous steps)\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Extract the gene annotation data - already done in previous step\n",
"# gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# Get the gene mapping dataframe using the library function\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
"print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n",
"print(\"First 5 rows of gene mapping:\")\n",
"print(gene_mapping.head())\n",
"\n",
"# Extract gene expression data - already done in previous step\n",
"gene_expression = get_genetic_data(matrix_file)\n",
"print(f\"Gene expression data shape: {gene_expression.shape}\")\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_expression, gene_mapping)\n",
"print(f\"Gene data after mapping shape: {gene_data.shape}\")\n",
"print(\"First 10 gene symbols after mapping:\")\n",
"print(gene_data.index[:10].tolist())\n",
"\n",
"# Save the processed gene 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": "deb34098",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d8b04473",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:28:25.771017Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape before normalization: (21372, 50)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data saved to ../../output/preprocess/Coronary_artery_disease/gene_data/GSE234398.csv\n",
"Loaded clinical data shape: (3, 50)\n",
"Initial linked data shape: (50, 21375)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (50, 21375)\n",
"Quartiles for 'Coronary_artery_disease':\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 'Coronary_artery_disease' in this dataset is severely biased.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 60.0\n",
" 50% (Median): 67.0\n",
" 75%: 73.0\n",
"Min: 39.0\n",
"Max: 78.0\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '0.0' with 22 occurrences. This represents 44.00% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is fine.\n",
"\n",
"Data not usable for trait study - not saving final linked data.\n"
]
}
],
"source": [
"# 1. Attempt to load gene data and handle possible issues with normalization\n",
"try:\n",
" # Create output directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
" \n",
" # Check if gene_data (from previous step) has any content\n",
" if gene_data.shape[0] == 0:\n",
" print(\"WARNING: Gene data is empty after normalization in previous step.\")\n",
" print(\"This appears to be miRNA data rather than gene expression data.\")\n",
" \n",
" # Since gene_data is empty, set gene_available to False\n",
" is_gene_available = False\n",
" \n",
" # Create an empty dataframe for metadata purposes\n",
" empty_df = pd.DataFrame()\n",
" \n",
" # Log information about this dataset for future reference\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True, # Consider it biased as we can't use it\n",
" df=empty_df,\n",
" note=\"Dataset appears to contain miRNA data rather than gene expression data. Gene symbols could not be normalized.\"\n",
" )\n",
" \n",
" print(\"Dataset marked as unusable due to lack of valid gene expression data.\")\n",
" else:\n",
" # If gene_data is not empty, proceed with normalization and linking\n",
" print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
" \n",
" # Save the gene data we have, even if it's already normalized\n",
" gene_data.to_csv(out_gene_data_file)\n",
" print(f\"Gene data saved to {out_gene_data_file}\")\n",
" \n",
" # Attempt to link clinical and gene data\n",
" if is_trait_available:\n",
" # Load clinical data\n",
" clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)\n",
" print(f\"Loaded clinical data 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\"Initial linked data shape: {linked_data.shape}\")\n",
" \n",
" # Handle missing values\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" if linked_data.shape[0] > 0:\n",
" # Check for bias in trait and demographic features\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" \n",
" # Validate data quality and save cohort info\n",
" is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=\"Successfully processed gene expression data for coronary artery disease.\"\n",
" )\n",
" \n",
" # 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.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
" else:\n",
" print(\"Data not usable for trait study - not saving final linked data.\")\n",
" else:\n",
" print(\"After handling missing values, no samples remain.\")\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"No valid samples after handling missing values.\"\n",
" )\n",
" else:\n",
" # Cannot proceed with linking if trait data is missing\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True,\n",
" df=pd.DataFrame(),\n",
" note=\"Cannot link data because trait information is not available.\"\n",
" )\n",
"except Exception as e:\n",
" print(f\"Error in data processing: {e}\")\n",
" \n",
" # Log the error and mark the dataset as unusable\n",
" validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=False, # Consider gene data unavailable if we had an error\n",
" is_trait_available=is_trait_available,\n",
" is_biased=True, # Consider it biased as we can't use it\n",
" df=pd.DataFrame(), # Empty dataframe for metadata\n",
" note=f\"Error during normalization or linking: {str(e)}\"\n",
" )"
]
}
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