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{
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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 = \"Congestive_heart_failure\"\n",
"cohort = \"GSE182600\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Congestive_heart_failure\"\n",
"in_cohort_dir = \"../../input/GEO/Congestive_heart_failure/GSE182600\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Congestive_heart_failure/GSE182600.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Congestive_heart_failure/gene_data/GSE182600.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Congestive_heart_failure/clinical_data/GSE182600.csv\"\n",
"json_path = \"../../output/preprocess/Congestive_heart_failure/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "58903b36",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Gene Expression of Cardiogenic Shock Patients under Extracorporeal Membrane Oxygenation\"\n",
"!Series_summary\t\"Prognosis for cardiogenic shock patients under ECMO was our study goal. Success defined as survived more than 7 days after ECMO installation and failure died or had multiple organ failure in 7 days. Total 34 cases were enrolled, 17 success and 17 failure.\"\n",
"!Series_summary\t\"Peripheral blood mononuclear cells collected at ECMO installation 0hr, 2hr and removal were used analyzed.\"\n",
"!Series_overall_design\t\"Analysis of the cardiogenic shock patients at extracorporeal membrane oxygenation treatment by genome-wide gene expression. Transcriptomic profiling between successful and failure groups were analyzed.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['disease state: Acute myocarditis', 'disease state: Acute myocardial infarction', 'disease state: Dilated cardiomyopathy, DCMP', 'disease state: Congestive heart failure', 'disease state: Dilated cardiomyopathy', 'disease state: Arrhythmia', 'disease state: Aortic dissection'], 1: ['age: 33.4', 'age: 51.2', 'age: 51.9', 'age: 47.8', 'age: 41.5', 'age: 67.3', 'age: 52.8', 'age: 16.1', 'age: 78.9', 'age: 53.2', 'age: 70.9', 'age: 59.9', 'age: 21.9', 'age: 45.2', 'age: 52.4', 'age: 32.3', 'age: 55.8', 'age: 47', 'age: 57.3', 'age: 31.7', 'age: 49.3', 'age: 66.1', 'age: 55.9', 'age: 49.1', 'age: 63', 'age: 21', 'age: 53.6', 'age: 50.1', 'age: 37.4', 'age: 71.5'], 2: ['gender: F', 'gender: M'], 3: ['outcome: Success', 'outcome: Failure', 'outcome: failure'], 4: ['cell type: PBMC'], 5: ['time: 0hr', 'time: 2hr', 'time: Removal']}\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": "936da96f",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0f6ebafa",
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"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",
"# Based on the background information, this dataset appears to contain gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# For trait (Congestive heart failure)\n",
"# Looking at the sample characteristics, outcome (row 3) indicates success or failure of ECMO treatment\n",
"# which is directly related to the trait of congestive heart failure\n",
"trait_row = 3\n",
"\n",
"def convert_trait(value):\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",
" # Convert to binary: 1 for failure (worse outcome), 0 for success\n",
" if value.lower() in ['failure']:\n",
" return 1\n",
" elif value.lower() == 'success':\n",
" return 0\n",
" return None\n",
"\n",
"# For age\n",
"age_row = 1\n",
"\n",
"def convert_age(value):\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",
" try:\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"# For gender\n",
"gender_row = 2\n",
"\n",
"def convert_gender(value):\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",
" # Convert to binary: 0 for female, 1 for male\n",
" if value.upper() == 'F':\n",
" return 0\n",
" elif value.upper() == 'M':\n",
" return 1\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Initial filtering - check if both gene and trait data are available\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",
"# I'll skip this part for now as we don't have the proper clinical data structure\n",
"# This will be handled in the next step when we have the appropriate data format\n"
]
},
{
"cell_type": "markdown",
"id": "fcdc3b78",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "41dc21d1",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:25:15.519718Z"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix file found: ../../input/GEO/Congestive_heart_failure/GSE182600/GSE182600_series_matrix.txt.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape: (29363, 78)\n",
"First 20 gene/probe identifiers:\n",
"Index(['ILMN_1343291', 'ILMN_1651209', 'ILMN_1651228', 'ILMN_1651229',\n",
" 'ILMN_1651235', 'ILMN_1651236', 'ILMN_1651237', 'ILMN_1651238',\n",
" 'ILMN_1651254', 'ILMN_1651260', 'ILMN_1651262', 'ILMN_1651268',\n",
" 'ILMN_1651278', 'ILMN_1651282', 'ILMN_1651285', 'ILMN_1651286',\n",
" 'ILMN_1651292', 'ILMN_1651303', 'ILMN_1651309', 'ILMN_1651315'],\n",
" dtype='object', name='ID')\n"
]
}
],
"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": "b3230465",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "35182ced",
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"execution": {
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"outputs": [],
"source": [
"# The gene identifiers start with 'ILMN_', which indicates these are Illumina array probe IDs,\n",
"# not standard human gene symbols. These IDs need to be mapped to official gene symbols\n",
"# for proper biological interpretation.\n",
"\n",
"# Illumina probe IDs like ILMN_1343291 need to be converted to their corresponding gene symbols\n",
"# using annotation information typically provided in platform files or through bioinformatics\n",
"# databases.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "b394d6e1",
"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', 'Transcript', 'Species', 'Source', 'Search_Key', 'ILMN_Gene', 'Source_Reference_ID', 'RefSeq_ID', 'Entrez_Gene_ID', 'GI', 'Accession', 'Symbol', 'Protein_Product', '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_3166687', 'ILMN_3165566', 'ILMN_3164811', 'ILMN_3165363', 'ILMN_3166511'], 'Transcript': ['ILMN_333737', 'ILMN_333646', 'ILMN_333584', 'ILMN_333628', 'ILMN_333719'], 'Species': ['ILMN Controls', 'ILMN Controls', 'ILMN Controls', 'ILMN Controls', 'ILMN Controls'], 'Source': ['ILMN_Controls', 'ILMN_Controls', 'ILMN_Controls', 'ILMN_Controls', 'ILMN_Controls'], 'Search_Key': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'ILMN_Gene': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'Source_Reference_ID': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'RefSeq_ID': [nan, nan, nan, nan, nan], 'Entrez_Gene_ID': [nan, nan, nan, nan, nan], 'GI': [nan, nan, nan, nan, nan], 'Accession': ['DQ516750', 'DQ883654', 'DQ668364', 'DQ516785', 'DQ854995'], 'Symbol': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144'], 'Protein_Product': [nan, nan, nan, nan, nan], 'Array_Address_Id': [5270161.0, 4260594.0, 7610424.0, 5260356.0, 2030196.0], 'Probe_Type': ['S', 'S', 'S', 'S', 'S'], 'Probe_Start': [12.0, 224.0, 868.0, 873.0, 130.0], 'SEQUENCE': ['CCCATGTGTCCAATTCTGAATATCTTTCCAGCTAAGTGCTTCTGCCCACC', 'GGATTAACTGCTGTGGTGTGTCATACTCGGCTACCTCCTGGTTTGGCGTC', 'GACCACGCCTTGTAATCGTATGACACGCGCTTGACACGACTGAATCCAGC', 'CTGCAATGCCATTAACAACCTTAGCACGGTATTTCCAGTAGCTGGTGAGC', 'CGTGCAGACAGGGATCGTAAGGCGATCCAGCCGGTATACCTTAGTCACAT'], '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': ['Methanocaldococcus jannaschii spike-in control MJ-500-33 genomic sequence', 'Synthetic construct clone NISTag13 external RNA control sequence', 'Synthetic construct clone TagJ microarray control', 'Methanocaldococcus jannaschii spike-in control MJ-1000-68 genomic sequence', 'Synthetic construct clone AG006.1100 external RNA control sequence'], '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': ['DQ516750', 'DQ883654', 'DQ668364', 'DQ516785', 'DQ854995']}\n",
"\n",
"Analyzing SPOT_ID.1 column for gene symbols:\n",
"\n",
"Gene data ID prefix: ILMN\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'ID' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'Transcript' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'Species' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'Source' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Checking for columns containing transcript or gene related terms:\n",
"Column 'Transcript' may contain gene-related information\n",
"Sample values: ['ILMN_333737', 'ILMN_333646', 'ILMN_333584']\n",
"Column 'ILMN_Gene' may contain gene-related information\n",
"Sample values: ['ERCC-00162', 'ERCC-00071', 'ERCC-00009']\n",
"Column 'Entrez_Gene_ID' may contain gene-related information\n",
"Sample values: [nan, nan, nan]\n",
"Column 'Symbol' may contain gene-related information\n",
"Sample values: ['ERCC-00162', 'ERCC-00071', 'ERCC-00009']\n"
]
}
],
"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 for gene information in the SPOT_ID.1 column which appears to contain gene names\n",
"print(\"\\nAnalyzing SPOT_ID.1 column for gene symbols:\")\n",
"if 'SPOT_ID.1' in gene_annotation.columns:\n",
" # Extract a few sample values\n",
" sample_values = gene_annotation['SPOT_ID.1'].head(3).tolist()\n",
" for i, value in enumerate(sample_values):\n",
" print(f\"Sample {i+1} excerpt: {value[:200]}...\") # Print first 200 chars\n",
" # Test the extract_human_gene_symbols function on these values\n",
" symbols = extract_human_gene_symbols(value)\n",
" print(f\" Extracted gene symbols: {symbols}\")\n",
"\n",
"# Try to find the probe IDs in the gene annotation\n",
"gene_data_id_prefix = gene_data.index[0].split('_')[0] # Get prefix of first gene ID\n",
"print(f\"\\nGene data ID prefix: {gene_data_id_prefix}\")\n",
"\n",
"# Look for columns that might match the gene data IDs\n",
"for col in gene_annotation.columns:\n",
" if gene_annotation[col].astype(str).str.contains(gene_data_id_prefix).any():\n",
" print(f\"Column '{col}' contains values matching gene data ID pattern\")\n",
"\n",
"# Check if there's any column that might contain transcript or gene IDs\n",
"print(\"\\nChecking for columns containing transcript or gene related terms:\")\n",
"for col in gene_annotation.columns:\n",
" if any(term in col.upper() for term in ['GENE', 'TRANSCRIPT', 'SYMBOL', 'NAME', 'DESCRIPTION']):\n",
" print(f\"Column '{col}' may contain gene-related information\")\n",
" # Show sample values\n",
" print(f\"Sample values: {gene_annotation[col].head(3).tolist()}\")\n"
]
},
{
"cell_type": "markdown",
"id": "0e6cfa72",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping dataframe sample:\n",
" ID Gene\n",
"0 ILMN_3166687 ERCC-00162\n",
"1 ILMN_3165566 ERCC-00071\n",
"2 ILMN_3164811 ERCC-00009\n",
"3 ILMN_3165363 ERCC-00053\n",
"4 ILMN_3166511 ERCC-00144\n",
"Mapping dataframe shape: (29377, 2)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data shape after mapping: (20206, 78)\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/Congestive_heart_failure/gene_data/GSE182600.csv\n"
]
}
],
"source": [
"# 1. Identify the columns in gene_annotation that correspond to gene identifiers and gene symbols\n",
"# From the previous output, we can see:\n",
"# - The 'ID' column contains values matching the ILMN_ pattern used in gene_data index\n",
"# - The 'Symbol' column appears to contain gene symbols\n",
"\n",
"# 2. Get a gene mapping dataframe using the get_gene_mapping function\n",
"probe_col = 'ID'\n",
"gene_col = 'Symbol'\n",
"gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
"\n",
"# Preview the mapping dataframe\n",
"print(\"Gene mapping dataframe sample:\")\n",
"print(gene_mapping.head())\n",
"print(f\"Mapping dataframe shape: {gene_mapping.shape}\")\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level data to gene-level data\n",
"# The apply_gene_mapping function handles the many-to-many mapping as specified\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Print information about the resulting gene expression dataframe\n",
"print(f\"\\nGene expression data shape after mapping: {gene_data.shape}\")\n",
"print(\"First 10 gene symbols after mapping:\")\n",
"print(gene_data.index[:10].tolist())\n",
"\n",
"# Save the gene data to a CSV file\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": "aa84763a",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape before normalization: (20206, 78)\n",
"Gene data shape after normalization: (19445, 78)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data saved to ../../output/preprocess/Congestive_heart_failure/gene_data/GSE182600.csv\n",
"Original clinical data preview:\n",
" !Sample_geo_accession GSM5532093 \\\n",
"0 !Sample_characteristics_ch1 disease state: Acute myocarditis \n",
"1 !Sample_characteristics_ch1 age: 33.4 \n",
"2 !Sample_characteristics_ch1 gender: F \n",
"3 !Sample_characteristics_ch1 outcome: Success \n",
"4 !Sample_characteristics_ch1 cell type: PBMC \n",
"\n",
" GSM5532094 GSM5532095 \\\n",
"0 disease state: Acute myocarditis disease state: Acute myocarditis \n",
"1 age: 51.2 age: 51.9 \n",
"2 gender: M gender: F \n",
"3 outcome: Success outcome: Failure \n",
"4 cell type: PBMC cell type: PBMC \n",
"\n",
" GSM5532096 \\\n",
"0 disease state: Acute myocardial infarction \n",
"1 age: 47.8 \n",
"2 gender: M \n",
"3 outcome: Success \n",
"4 cell type: PBMC \n",
"\n",
" GSM5532097 \\\n",
"0 disease state: Acute myocarditis \n",
"1 age: 41.5 \n",
"2 gender: F \n",
"3 outcome: Failure \n",
"4 cell type: PBMC \n",
"\n",
" GSM5532098 \\\n",
"0 disease state: Acute myocardial infarction \n",
"1 age: 67.3 \n",
"2 gender: M \n",
"3 outcome: Failure \n",
"4 cell type: PBMC \n",
"\n",
" GSM5532099 \\\n",
"0 disease state: Acute myocardial infarction \n",
"1 age: 52.8 \n",
"2 gender: M \n",
"3 outcome: Success \n",
"4 cell type: PBMC \n",
"\n",
" GSM5532100 \\\n",
"0 disease state: Dilated cardiomyopathy, DCMP \n",
"1 age: 16.1 \n",
"2 gender: M \n",
"3 outcome: Failure \n",
"4 cell type: PBMC \n",
"\n",
" GSM5532101 ... \\\n",
"0 disease state: Acute myocardial infarction ... \n",
"1 age: 78.9 ... \n",
"2 gender: M ... \n",
"3 outcome: Failure ... \n",
"4 cell type: PBMC ... \n",
"\n",
" GSM5532161 \\\n",
"0 disease state: Acute myocardial infarction \n",
"1 age: 52.8 \n",
"2 gender: M \n",
"3 outcome: Success \n",
"4 cell type: PBMC \n",
"\n",
" GSM5532162 \\\n",
"0 disease state: Acute myocardial infarction \n",
"1 age: 53.2 \n",
"2 gender: M \n",
"3 outcome: Success \n",
"4 cell type: PBMC \n",
"\n",
" GSM5532163 GSM5532164 \\\n",
"0 disease state: Acute myocarditis disease state: Arrhythmia \n",
"1 age: 21.9 age: 55.8 \n",
"2 gender: F gender: M \n",
"3 outcome: Success outcome: Success \n",
"4 cell type: PBMC cell type: PBMC \n",
"\n",
" GSM5532165 \\\n",
"0 disease state: Dilated cardiomyopathy \n",
"1 age: 47 \n",
"2 gender: M \n",
"3 outcome: Success \n",
"4 cell type: PBMC \n",
"\n",
" GSM5532166 \\\n",
"0 disease state: Acute myocardial infarction \n",
"1 age: 49.3 \n",
"2 gender: M \n",
"3 outcome: Success \n",
"4 cell type: PBMC \n",
"\n",
" GSM5532167 \\\n",
"0 disease state: Congestive heart failure \n",
"1 age: 66.1 \n",
"2 gender: M \n",
"3 outcome: Success \n",
"4 cell type: PBMC \n",
"\n",
" GSM5532168 \\\n",
"0 disease state: Acute myocardial infarction \n",
"1 age: 53.6 \n",
"2 gender: M \n",
"3 outcome: Success \n",
"4 cell type: PBMC \n",
"\n",
" GSM5532169 \\\n",
"0 disease state: Acute myocardial infarction \n",
"1 age: 50.1 \n",
"2 gender: F \n",
"3 outcome: Success \n",
"4 cell type: PBMC \n",
"\n",
" GSM5532170 \n",
"0 disease state: Congestive heart failure \n",
"1 age: 56.5 \n",
"2 gender: M \n",
"3 outcome: Success \n",
"4 cell type: PBMC \n",
"\n",
"[5 rows x 79 columns]\n",
"Selected clinical data shape: (3, 78)\n",
"Clinical data preview:\n",
" GSM5532093 GSM5532094 GSM5532095 GSM5532096 \\\n",
"Congestive_heart_failure 0.0 0.0 1.0 0.0 \n",
"Age 33.4 51.2 51.9 47.8 \n",
"Gender 0.0 1.0 0.0 1.0 \n",
"\n",
" GSM5532097 GSM5532098 GSM5532099 GSM5532100 \\\n",
"Congestive_heart_failure 1.0 1.0 0.0 1.0 \n",
"Age 41.5 67.3 52.8 16.1 \n",
"Gender 0.0 1.0 1.0 1.0 \n",
"\n",
" GSM5532101 GSM5532102 ... GSM5532161 GSM5532162 \\\n",
"Congestive_heart_failure 1.0 0.0 ... 0.0 0.0 \n",
"Age 78.9 53.2 ... 52.8 53.2 \n",
"Gender 1.0 1.0 ... 1.0 1.0 \n",
"\n",
" GSM5532163 GSM5532164 GSM5532165 GSM5532166 \\\n",
"Congestive_heart_failure 0.0 0.0 0.0 0.0 \n",
"Age 21.9 55.8 47.0 49.3 \n",
"Gender 0.0 1.0 1.0 1.0 \n",
"\n",
" GSM5532167 GSM5532168 GSM5532169 GSM5532170 \n",
"Congestive_heart_failure 0.0 0.0 0.0 0.0 \n",
"Age 66.1 53.6 50.1 56.5 \n",
"Gender 1.0 1.0 0.0 1.0 \n",
"\n",
"[3 rows x 78 columns]\n",
"Linked data shape before processing: (78, 19448)\n",
"Linked data preview (first 5 rows, 5 columns):\n",
" Congestive_heart_failure Age Gender A1BG A1BG-AS1\n",
"GSM5532093 0.0 33.4 0.0 123.145500 1284.286536\n",
"GSM5532094 0.0 51.2 1.0 134.323626 2123.843378\n",
"GSM5532095 1.0 51.9 0.0 100.294706 1088.857429\n",
"GSM5532096 0.0 47.8 1.0 130.315854 1074.517347\n",
"GSM5532097 1.0 41.5 0.0 106.890941 1070.809003\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (78, 19448)\n",
"For the feature 'Congestive_heart_failure', the least common label is '1.0' with 31 occurrences. This represents 39.74% of the dataset.\n",
"Quartiles for 'Age':\n",
" 25%: 47.0\n",
" 50% (Median): 52.15\n",
" 75%: 56.35\n",
"Min: 16.1\n",
"Max: 78.9\n",
"For the feature 'Gender', the least common label is '0.0' with 24 occurrences. This represents 30.77% of the dataset.\n",
"A new JSON file was created at: ../../output/preprocess/Congestive_heart_failure/cohort_info.json\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Congestive_heart_failure/GSE182600.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"# Use normalize_gene_symbols_in_index to standardize gene symbols\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
"print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
"\n",
"# Save the normalized gene data to file\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 expression data saved to {out_gene_data_file}\")\n",
"\n",
"# Load the actual clinical data from the matrix file that was previously obtained in Step 1\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
"\n",
"# Get preview of clinical data to understand its structure\n",
"print(\"Original clinical data preview:\")\n",
"print(clinical_data.head())\n",
"\n",
"# 2. If we have trait data available, proceed with linking\n",
"if trait_row is not None:\n",
" # Extract clinical features using the original clinical data\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",
" print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n",
" print(\"Clinical data preview:\")\n",
" print(selected_clinical_df.head())\n",
"\n",
" # Link the clinical and genetic data\n",
" linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
" print(f\"Linked data shape before processing: {linked_data.shape}\")\n",
" print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
" print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Empty dataframe\")\n",
"\n",
" # 3. Handle missing values\n",
" try:\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
" except Exception as e:\n",
" print(f\"Error handling missing values: {e}\")\n",
" linked_data = pd.DataFrame() # Create empty dataframe if error occurs\n",
"\n",
" # 4. Check for bias in features\n",
" if not linked_data.empty and linked_data.shape[0] > 0:\n",
" # Check if trait is biased\n",
" trait_type = 'binary' if len(linked_data[trait].unique()) <= 2 else 'continuous'\n",
" if trait_type == \"binary\":\n",
" is_biased = judge_binary_variable_biased(linked_data, trait)\n",
" else:\n",
" is_biased = judge_continuous_variable_biased(linked_data, trait)\n",
" \n",
" # Remove biased demographic features\n",
" if \"Age\" in linked_data.columns:\n",
" age_biased = judge_continuous_variable_biased(linked_data, 'Age')\n",
" if age_biased:\n",
" linked_data = linked_data.drop(columns='Age')\n",
" \n",
" if \"Gender\" in linked_data.columns:\n",
" gender_biased = judge_binary_variable_biased(linked_data, 'Gender')\n",
" if gender_biased:\n",
" linked_data = linked_data.drop(columns='Gender')\n",
" else:\n",
" is_biased = True\n",
" print(\"Cannot check for bias as dataframe is empty or has no rows after missing value handling\")\n",
"\n",
" # 5. Validate and save cohort information\n",
" note = \"\"\n",
" if linked_data.empty or linked_data.shape[0] == 0:\n",
" note = \"Dataset contains gene expression data related to atrial fibrillation after cardiac surgery, but linking clinical and genetic data failed, possibly due to mismatched sample IDs.\"\n",
" else:\n",
" note = \"Dataset contains gene expression data for atrial fibrillation after cardiac surgery, which is relevant to arrhythmia research.\"\n",
" \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,\n",
" note=note\n",
" )\n",
"\n",
" # 6. Save the linked data if 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(\"Dataset is not usable for analysis. No linked data file saved.\")\n",
"else:\n",
" # If no trait data available, validate with trait_available=False\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,\n",
" is_biased=True, # Set to True since we can't use data without trait\n",
" df=pd.DataFrame(), # Empty DataFrame\n",
" note=\"Dataset contains gene expression data but lacks proper clinical trait information for arrhythmia analysis.\"\n",
" )\n",
" \n",
" print(\"Dataset is not usable for arrhythmia analysis due to lack of clinical trait data. No linked data file saved.\")"
]
}
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