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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 = \"GSE93101\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Congestive_heart_failure\"\n",
"in_cohort_dir = \"../../input/GEO/Congestive_heart_failure/GSE93101\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Congestive_heart_failure/GSE93101.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Congestive_heart_failure/gene_data/GSE93101.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Congestive_heart_failure/clinical_data/GSE93101.csv\"\n",
"json_path = \"../../output/preprocess/Congestive_heart_failure/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "6f2b768c",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "09762be8",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Molecular Prognosis 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 were used analyzed.\"\n",
"!Series_overall_design\t\"Analysis of the cardiogenic shock patients at extracorporeal membrane oxygenation treatment by genome-wide expression and methylation. Transcriptomic profiling and DNA methylation between successful and failure groups were analyzed.\"\n",
"!Series_overall_design\t\"This submission represents the transcriptome data.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['course: Acute myocarditis', 'course: Acute myocardial infarction', 'course: Dilated cardiomyopathy, DCMP', 'course: Congestive heart failure', 'course: Dilated cardiomyopathy', 'course: Arrhythmia', 'course: 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']}\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": "7add2ced",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f8c921ed",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of clinical features:\n",
"{'GSM2443799': [0.0, 33.4, 0.0], 'GSM2443800': [0.0, 51.2, 1.0], 'GSM2443801': [0.0, 51.9, 0.0], 'GSM2443802': [0.0, 47.8, 1.0], 'GSM2443803': [0.0, 41.5, 0.0], 'GSM2443804': [0.0, 67.3, 1.0], 'GSM2443805': [0.0, 52.8, 1.0], 'GSM2443806': [0.0, 16.1, 1.0], 'GSM2443807': [0.0, 78.9, 1.0], 'GSM2443808': [0.0, 53.2, 1.0], 'GSM2443809': [0.0, 70.9, 1.0], 'GSM2443810': [0.0, 59.9, 1.0], 'GSM2443811': [0.0, 21.9, 0.0], 'GSM2443812': [1.0, 45.2, 0.0], 'GSM2443813': [0.0, 52.4, 1.0], 'GSM2443814': [0.0, 32.3, 1.0], 'GSM2443815': [0.0, 52.8, 1.0], 'GSM2443816': [0.0, 55.8, 1.0], 'GSM2443817': [0.0, 47.0, 1.0], 'GSM2443818': [0.0, 55.8, 1.0], 'GSM2443819': [0.0, 57.3, 0.0], 'GSM2443820': [0.0, 31.7, 0.0], 'GSM2443821': [0.0, 49.3, 1.0], 'GSM2443822': [1.0, 66.1, 1.0], 'GSM2443823': [0.0, 55.9, 1.0], 'GSM2443824': [0.0, 49.1, 0.0], 'GSM2443825': [0.0, 63.0, 1.0], 'GSM2443826': [0.0, 21.0, 1.0], 'GSM2443827': [0.0, 53.6, 1.0], 'GSM2443828': [0.0, 50.1, 0.0], 'GSM2443829': [0.0, 37.4, 1.0], 'GSM2443830': [0.0, 71.5, 0.0], 'GSM2443831': [1.0, 56.5, 1.0]}\n",
"Clinical data saved to ../../output/preprocess/Congestive_heart_failure/clinical_data/GSE93101.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset contains transcriptome data\n",
"# \"This submission represents the transcriptome data.\"\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Data Availability\n",
"\n",
"# Trait: Congestive heart failure\n",
"# Looking at the sample characteristics, key 0 contains \"course: Congestive heart failure\"\n",
"# This suggests patients have different conditions, and we're interested in those with CHF\n",
"trait_row = 0\n",
"\n",
"# Age: Available in key 1\n",
"age_row = 1\n",
"\n",
"# Gender: Available in key 2\n",
"gender_row = 2\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait value to binary (0 or 1)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if \":\" in value:\n",
" condition = value.split(\":\", 1)[1].strip()\n",
" else:\n",
" condition = value.strip()\n",
" \n",
" # Check if the condition is congestive heart failure (case insensitive)\n",
" if condition.lower() == \"congestive heart failure\":\n",
" return 1\n",
" else:\n",
" return 0\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to continuous (float)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if \":\" in value:\n",
" age_str = value.split(\":\", 1)[1].strip()\n",
" else:\n",
" age_str = value.strip()\n",
" \n",
" try:\n",
" return float(age_str)\n",
" except (ValueError, TypeError):\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 the value after the colon\n",
" if \":\" in value:\n",
" gender = value.split(\":\", 1)[1].strip()\n",
" else:\n",
" gender = value.strip()\n",
" \n",
" if gender.upper() == \"F\":\n",
" return 0\n",
" elif gender.upper() == \"M\":\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Check if trait data is available (trait_row is not None)\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort info for 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 (if trait_row is not None)\n",
"if trait_row is not None:\n",
" # Extract clinical features\n",
" clinical_features_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the extracted clinical features\n",
" preview = preview_df(clinical_features_df)\n",
" print(\"Preview of clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save clinical data to CSV\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "1fac0d62",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
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"name": "stdout",
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"text": [
"Matrix file found: ../../input/GEO/Congestive_heart_failure/GSE93101/GSE93101_series_matrix.txt.gz\n",
"Gene data shape: (29363, 33)\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": "50eb9800",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "97245137",
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"source": [
"# The identifiers starting with \"ILMN_\" are Illumina probe IDs, not human gene symbols.\n",
"# These are specific to Illumina microarray platforms and need to be mapped to standard gene symbols.\n",
"# ILMN_ prefix indicates Illumina's proprietary probe identifiers from their microarray platforms.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "49d4be7c",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
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{
"name": "stdout",
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"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",
"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": "57195f28",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene mapping preview:\n",
"Gene mapping shape: (29377, 2)\n",
"{'ID': ['ILMN_3166687', 'ILMN_3165566', 'ILMN_3164811', 'ILMN_3165363', 'ILMN_3166511'], 'Gene': ['ERCC-00162', 'ERCC-00071', 'ERCC-00009', 'ERCC-00053', 'ERCC-00144']}\n",
"\n",
"Gene expression data after mapping:\n",
"Shape: (20206, 33)\n",
"First 5 gene symbols: ['A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1']\n",
"Number of unique gene symbols: 20206\n",
"Common genes found: ['TP53', 'BRCA1', 'EGFR', 'TNF', 'IL6']\n"
]
}
],
"source": [
"# 1. Based on the preview, we can see that:\n",
"# - 'ID' column in the gene annotation contains the same ILMN_ identifiers used in gene expression data\n",
"# - 'Symbol' column contains gene symbols we need to map to\n",
"\n",
"# 2. Get gene mapping dataframe using the function from the library\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
"\n",
"# Examine the mapping to verify it has the expected format\n",
"print(\"\\nGene mapping preview:\")\n",
"print(f\"Gene mapping shape: {gene_mapping.shape}\")\n",
"print(preview_df(gene_mapping, n=5))\n",
"\n",
"# 3. Apply gene mapping to convert probe-level expression to gene-level expression\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Preview the resulting gene expression data\n",
"print(\"\\nGene expression data after mapping:\")\n",
"print(f\"Shape: {gene_data.shape}\")\n",
"print(f\"First 5 gene symbols: {gene_data.index[:5].tolist()}\")\n",
"\n",
"# Examine number of unique gene symbols\n",
"print(f\"Number of unique gene symbols: {len(gene_data.index.unique())}\")\n",
"\n",
"# Check if standard gene symbols are present by looking for common genes\n",
"common_genes = [\"TP53\", \"BRCA1\", \"EGFR\", \"TNF\", \"IL6\"]\n",
"found_genes = [gene for gene in common_genes if gene in gene_data.index]\n",
"print(f\"Common genes found: {found_genes}\")\n"
]
},
{
"cell_type": "markdown",
"id": "a23bc703",
"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, 33)\n",
"Gene data shape after normalization: (19445, 33)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data saved to ../../output/preprocess/Congestive_heart_failure/gene_data/GSE93101.csv\n",
"Original clinical data preview:\n",
" !Sample_geo_accession GSM2443799 \\\n",
"0 !Sample_characteristics_ch1 course: 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",
"\n",
" GSM2443800 GSM2443801 \\\n",
"0 course: Acute myocarditis course: Acute myocarditis \n",
"1 age: 51.2 age: 51.9 \n",
"2 gender: M gender: F \n",
"3 outcome: Success outcome: Failure \n",
"\n",
" GSM2443802 GSM2443803 \\\n",
"0 course: Acute myocardial infarction course: Acute myocarditis \n",
"1 age: 47.8 age: 41.5 \n",
"2 gender: M gender: F \n",
"3 outcome: Success outcome: Failure \n",
"\n",
" GSM2443804 GSM2443805 \\\n",
"0 course: Acute myocardial infarction course: Acute myocardial infarction \n",
"1 age: 67.3 age: 52.8 \n",
"2 gender: M gender: M \n",
"3 outcome: Failure outcome: Success \n",
"\n",
" GSM2443806 GSM2443807 \\\n",
"0 course: Dilated cardiomyopathy, DCMP course: Acute myocardial infarction \n",
"1 age: 16.1 age: 78.9 \n",
"2 gender: M gender: M \n",
"3 outcome: Failure outcome: Failure \n",
"\n",
" ... GSM2443822 GSM2443823 \\\n",
"0 ... course: Congestive heart failure course: Aortic dissection \n",
"1 ... age: 66.1 age: 55.9 \n",
"2 ... gender: M gender: M \n",
"3 ... outcome: Success outcome: Failure \n",
"\n",
" GSM2443824 GSM2443825 \\\n",
"0 course: Dilated cardiomyopathy, DCMP course: Acute myocardial infarction \n",
"1 age: 49.1 age: 63 \n",
"2 gender: F gender: M \n",
"3 outcome: Failure outcome: Failure \n",
"\n",
" GSM2443826 GSM2443827 \\\n",
"0 course: Dilated cardiomyopathy, DCMP course: Acute myocardial infarction \n",
"1 age: 21 age: 53.6 \n",
"2 gender: M gender: M \n",
"3 outcome: Failure outcome: Success \n",
"\n",
" GSM2443828 GSM2443829 \\\n",
"0 course: Acute myocardial infarction course: Acute myocardial infarction \n",
"1 age: 50.1 age: 37.4 \n",
"2 gender: F gender: M \n",
"3 outcome: Success outcome: Failure \n",
"\n",
" GSM2443830 GSM2443831 \n",
"0 course: Acute myocarditis course: Congestive heart failure \n",
"1 age: 71.5 age: 56.5 \n",
"2 gender: F gender: M \n",
"3 outcome: Success outcome: Success \n",
"\n",
"[4 rows x 34 columns]\n",
"Selected clinical data shape: (3, 33)\n",
"Clinical data preview:\n",
" GSM2443799 GSM2443800 GSM2443801 GSM2443802 \\\n",
"Congestive_heart_failure 0.0 0.0 0.0 0.0 \n",
"Age 33.4 51.2 51.9 47.8 \n",
"Gender 0.0 1.0 0.0 1.0 \n",
"\n",
" GSM2443803 GSM2443804 GSM2443805 GSM2443806 \\\n",
"Congestive_heart_failure 0.0 0.0 0.0 0.0 \n",
"Age 41.5 67.3 52.8 16.1 \n",
"Gender 0.0 1.0 1.0 1.0 \n",
"\n",
" GSM2443807 GSM2443808 ... GSM2443822 GSM2443823 \\\n",
"Congestive_heart_failure 0.0 0.0 ... 1.0 0.0 \n",
"Age 78.9 53.2 ... 66.1 55.9 \n",
"Gender 1.0 1.0 ... 1.0 1.0 \n",
"\n",
" GSM2443824 GSM2443825 GSM2443826 GSM2443827 \\\n",
"Congestive_heart_failure 0.0 0.0 0.0 0.0 \n",
"Age 49.1 63.0 21.0 53.6 \n",
"Gender 0.0 1.0 1.0 1.0 \n",
"\n",
" GSM2443828 GSM2443829 GSM2443830 GSM2443831 \n",
"Congestive_heart_failure 0.0 0.0 0.0 1.0 \n",
"Age 50.1 37.4 71.5 56.5 \n",
"Gender 0.0 1.0 0.0 1.0 \n",
"\n",
"[3 rows x 33 columns]\n",
"Linked data shape before processing: (33, 19448)\n",
"Linked data preview (first 5 rows, 5 columns):\n",
" Congestive_heart_failure Age Gender A1BG A1BG-AS1\n",
"GSM2443799 0.0 33.4 0.0 129.442547 1330.542639\n",
"GSM2443800 0.0 51.2 1.0 142.061233 2177.610030\n",
"GSM2443801 0.0 51.9 0.0 103.958331 1130.866630\n",
"GSM2443802 0.0 47.8 1.0 137.556161 1116.450458\n",
"GSM2443803 0.0 41.5 0.0 111.260768 1112.964973\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (33, 19448)\n",
"For the feature 'Congestive_heart_failure', the least common label is '1.0' with 3 occurrences. This represents 9.09% of the dataset.\n",
"Quartiles for 'Age':\n",
" 25%: 45.2\n",
" 50% (Median): 52.4\n",
" 75%: 56.5\n",
"Min: 16.1\n",
"Max: 78.9\n",
"For the feature 'Gender', the least common label is '0.0' with 10 occurrences. This represents 30.30% of the dataset.\n",
"Dataset is not usable for analysis. No linked data file saved.\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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