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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "27a327e3",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T06:47:06.287025Z"
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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 = \"Atrial_Fibrillation\"\n",
"cohort = \"GSE235307\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Atrial_Fibrillation\"\n",
"in_cohort_dir = \"../../input/GEO/Atrial_Fibrillation/GSE235307\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Atrial_Fibrillation/GSE235307.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Atrial_Fibrillation/gene_data/GSE235307.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Atrial_Fibrillation/clinical_data/GSE235307.csv\"\n",
"json_path = \"../../output/preprocess/Atrial_Fibrillation/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "b05578ac",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "686f22a4",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:47:06.288827Z",
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"shell.execute_reply": "2025-03-25T06:47:06.724771Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Gene expression and atrial fibrillation prediction\"\n",
"!Series_summary\t\"The aim of this study was to identify a blood gene expression profile that predicts atrial fibrillation in heart failure patients\"\n",
"!Series_overall_design\t\"Cardiac blood samples were obtained from the coronary sinus during CRT-D (Cardiac Resynchronization Therapy - Defibrillator) placement in heart failure patients. Patients were followed during 1 year.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: Whole blood'], 1: ['gender: Male', 'gender: Female'], 2: ['age: 63', 'age: 60', 'age: 72', 'age: 66', 'age: 70', 'age: 64', 'age: 61', 'age: 44', 'age: 54', 'age: 50', 'age: 79', 'age: 51', 'age: 55', 'age: 67', 'age: 52', 'age: 73', 'age: 76', 'age: 43', 'age: 68', 'age: 78', 'age: 69', 'age: 57', 'age: 59', 'age: 53', 'age: 65', 'age: 56', 'age: 74', 'age: 38', 'age: 71', 'age: 37'], 3: ['cardiopathy: ischemic', 'cardiopathy: non ischemic', 'cardiopathy: mixed'], 4: ['cardiac rhythm at start of the study: Sinus rhythm'], 5: ['cardiac rhythm after 1 year follow-up: Sinus rhythm', 'cardiac rhythm after 1 year follow-up: Atrial fibrillation']}\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": "3c10713e",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b0115d97",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:47:06.727170Z",
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"shell.execute_reply": "2025-03-25T06:47:06.743713Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of extracted clinical features:\n",
"{'GSM7498589': [0.0, 63.0, 1.0], 'GSM7498590': [0.0, 60.0, 1.0], 'GSM7498591': [0.0, 60.0, 1.0], 'GSM7498592': [0.0, 72.0, 1.0], 'GSM7498593': [0.0, 63.0, 1.0], 'GSM7498594': [0.0, 66.0, 0.0], 'GSM7498595': [0.0, 70.0, 1.0], 'GSM7498596': [0.0, 64.0, 1.0], 'GSM7498597': [0.0, 63.0, 1.0], 'GSM7498598': [0.0, 61.0, 1.0], 'GSM7498599': [0.0, 70.0, 0.0], 'GSM7498600': [0.0, 64.0, 1.0], 'GSM7498601': [0.0, 63.0, 1.0], 'GSM7498602': [0.0, 44.0, 1.0], 'GSM7498603': [0.0, 54.0, 1.0], 'GSM7498604': [0.0, 44.0, 1.0], 'GSM7498605': [0.0, 50.0, 1.0], 'GSM7498606': [1.0, 79.0, 1.0], 'GSM7498607': [0.0, 63.0, 1.0], 'GSM7498608': [0.0, 63.0, 0.0], 'GSM7498609': [1.0, 64.0, 1.0], 'GSM7498610': [0.0, 60.0, 1.0], 'GSM7498611': [0.0, 51.0, 1.0], 'GSM7498612': [0.0, 55.0, 1.0], 'GSM7498613': [0.0, 55.0, 1.0], 'GSM7498614': [1.0, 67.0, 1.0], 'GSM7498615': [0.0, 52.0, 1.0], 'GSM7498616': [0.0, 70.0, 0.0], 'GSM7498617': [0.0, 54.0, 1.0], 'GSM7498618': [0.0, 54.0, 1.0], 'GSM7498619': [0.0, 73.0, 1.0], 'GSM7498620': [0.0, 54.0, 0.0], 'GSM7498621': [0.0, 76.0, 1.0], 'GSM7498622': [0.0, 76.0, 1.0], 'GSM7498623': [0.0, 43.0, 0.0], 'GSM7498624': [0.0, 64.0, 1.0], 'GSM7498625': [0.0, 64.0, 1.0], 'GSM7498626': [0.0, 68.0, 0.0], 'GSM7498627': [0.0, 43.0, 1.0], 'GSM7498628': [1.0, 54.0, 1.0], 'GSM7498629': [0.0, 72.0, 0.0], 'GSM7498630': [0.0, 51.0, 1.0], 'GSM7498631': [0.0, 68.0, 0.0], 'GSM7498632': [0.0, 50.0, 0.0], 'GSM7498633': [0.0, 78.0, 1.0], 'GSM7498634': [1.0, 69.0, 1.0], 'GSM7498635': [0.0, 64.0, 0.0], 'GSM7498636': [0.0, 54.0, 1.0], 'GSM7498637': [0.0, 54.0, 1.0], 'GSM7498638': [0.0, 57.0, 1.0], 'GSM7498639': [0.0, 55.0, 0.0], 'GSM7498640': [0.0, 60.0, 1.0], 'GSM7498641': [0.0, 59.0, 1.0], 'GSM7498642': [0.0, 54.0, 1.0], 'GSM7498643': [0.0, 54.0, 1.0], 'GSM7498644': [0.0, 54.0, 1.0], 'GSM7498645': [0.0, 54.0, 1.0], 'GSM7498646': [0.0, 53.0, 1.0], 'GSM7498647': [0.0, 52.0, 0.0], 'GSM7498648': [0.0, 68.0, 1.0], 'GSM7498649': [0.0, 72.0, 0.0], 'GSM7498650': [0.0, 70.0, 1.0], 'GSM7498651': [0.0, 65.0, 1.0], 'GSM7498652': [0.0, 64.0, 1.0], 'GSM7498653': [0.0, 56.0, 0.0], 'GSM7498654': [0.0, 56.0, 0.0], 'GSM7498655': [0.0, 63.0, 1.0], 'GSM7498656': [0.0, 57.0, 1.0], 'GSM7498657': [0.0, 63.0, 1.0], 'GSM7498658': [0.0, 68.0, 1.0], 'GSM7498659': [0.0, 66.0, 0.0], 'GSM7498660': [0.0, 74.0, 0.0], 'GSM7498661': [0.0, 38.0, 1.0], 'GSM7498662': [0.0, 56.0, 1.0], 'GSM7498663': [0.0, 57.0, 1.0], 'GSM7498664': [0.0, 71.0, 0.0], 'GSM7498665': [1.0, 78.0, 0.0], 'GSM7498666': [0.0, 51.0, 1.0], 'GSM7498667': [0.0, 50.0, 1.0], 'GSM7498668': [0.0, 37.0, 1.0], 'GSM7498669': [0.0, 37.0, 1.0], 'GSM7498670': [0.0, 70.0, 0.0], 'GSM7498671': [0.0, 72.0, 0.0], 'GSM7498672': [0.0, 73.0, 1.0], 'GSM7498673': [0.0, 69.0, 0.0], 'GSM7498674': [0.0, 69.0, 0.0], 'GSM7498675': [1.0, 63.0, 1.0], 'GSM7498676': [0.0, 62.0, 0.0], 'GSM7498677': [0.0, 59.0, 0.0], 'GSM7498678': [0.0, 67.0, 1.0], 'GSM7498679': [0.0, 76.0, 1.0], 'GSM7498680': [0.0, 63.0, 1.0], 'GSM7498681': [0.0, 55.0, 1.0], 'GSM7498682': [0.0, 57.0, 1.0], 'GSM7498683': [0.0, 53.0, 1.0], 'GSM7498684': [0.0, 59.0, 1.0], 'GSM7498685': [1.0, 77.0, 1.0], 'GSM7498686': [0.0, 54.0, 1.0], 'GSM7498687': [1.0, 64.0, 1.0], 'GSM7498688': [0.0, 75.0, 0.0], 'GSM7498689': [0.0, 75.0, 0.0], 'GSM7498690': [0.0, 72.0, 0.0], 'GSM7498691': [0.0, 58.0, 0.0], 'GSM7498692': [0.0, 75.0, 1.0], 'GSM7498693': [0.0, 78.0, 1.0], 'GSM7498694': [0.0, 58.0, 1.0], 'GSM7498695': [0.0, 64.0, 1.0], 'GSM7498696': [0.0, 63.0, 1.0], 'GSM7498697': [0.0, 61.0, 1.0], 'GSM7498698': [0.0, 60.0, 1.0], 'GSM7498699': [0.0, 59.0, 0.0], 'GSM7498700': [0.0, 68.0, 1.0], 'GSM7498701': [0.0, 77.0, 1.0], 'GSM7498702': [1.0, 57.0, 1.0], 'GSM7498703': [0.0, 62.0, 0.0], 'GSM7498704': [1.0, 66.0, 1.0], 'GSM7498705': [1.0, 57.0, 1.0], 'GSM7498706': [1.0, 65.0, 1.0], 'GSM7498707': [0.0, 59.0, 1.0]}\n",
"Clinical features saved to ../../output/preprocess/Atrial_Fibrillation/clinical_data/GSE235307.csv\n"
]
}
],
"source": [
"# 1. Determine gene expression data availability\n",
"# This dataset appears to be about gene expression in blood samples from heart failure patients\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Identify keys in the sample characteristics dictionary\n",
"\n",
"# For trait (Atrial_Fibrillation): appears in sample characteristics key 5\n",
"# We can see patients had either \"Sinus rhythm\" or \"Atrial fibrillation\" after 1 year follow-up\n",
"trait_row = 5\n",
"\n",
"# For age: appears in sample characteristics key 2\n",
"age_row = 2\n",
"\n",
"# For gender: appears in sample characteristics key 1\n",
"gender_row = 1\n",
"\n",
"# 2.2 Define conversion functions\n",
"\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait value to binary (0 or 1)\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" value = value.lower()\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"atrial fibrillation\" in value:\n",
" return 1\n",
" elif \"sinus rhythm\" in value:\n",
" return 0\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age value to continuous numeric\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except (ValueError, TypeError):\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
" if not isinstance(value, str):\n",
" return None\n",
" \n",
" value = value.lower()\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" if \"female\" in value:\n",
" return 0\n",
" elif \"male\" in value:\n",
" return 1\n",
" return None\n",
"\n",
"# 3. Save metadata for 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. Extract clinical features if trait data is available\n",
"if trait_row is not None:\n",
" # Assume clinical_data variable exists from previous step\n",
" clinical_features = 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",
" print(\"Preview of extracted clinical features:\")\n",
" print(preview_df(clinical_features))\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save clinical features to CSV file\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "b44a916d",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5276f61c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:47:06.745793Z",
"iopub.status.busy": "2025-03-25T06:47:06.745682Z",
"iopub.status.idle": "2025-03-25T06:47:07.527259Z",
"shell.execute_reply": "2025-03-25T06:47:07.526603Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix file found: ../../input/GEO/Atrial_Fibrillation/GSE235307/GSE235307_series_matrix.txt.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape: (58717, 119)\n",
"First 20 gene/probe identifiers:\n",
"Index(['4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16',\n",
" '17', '18', '19', '20', '21', '22', '23'],\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": "fc8d44fe",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c2574985",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:47:07.529111Z",
"iopub.status.busy": "2025-03-25T06:47:07.528972Z",
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"shell.execute_reply": "2025-03-25T06:47:07.530938Z"
}
},
"outputs": [],
"source": [
"# The identifiers '4', '5', '6', etc. are numeric values that do not correspond to human gene symbols\n",
"# These appear to be row indices or probe IDs that need to be mapped to actual gene symbols\n",
"# In human genomics, gene symbols would typically be alphanumeric identifiers like \"BRCA1\", \"TP53\", etc.\n",
"\n",
"# Since these are numeric identifiers and not recognizable gene symbols,\n",
"# they will require mapping to standard gene symbols for meaningful analysis\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "aba8ee38",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e72b5cbc",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:47:07.533119Z",
"iopub.status.busy": "2025-03-25T06:47:07.532993Z",
"iopub.status.idle": "2025-03-25T06:48:01.201314Z",
"shell.execute_reply": "2025-03-25T06:48:01.200639Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"Columns in gene annotation: ['ID', 'COL', 'ROW', 'NAME', 'SPOT_ID', 'CONTROL_TYPE', 'REFSEQ', 'GB_ACC', 'LOCUSLINK_ID', 'GENE_SYMBOL', 'GENE_NAME', 'UNIGENE_ID', 'ENSEMBL_ID', 'ACCESSION_STRING', 'CHROMOSOMAL_LOCATION', 'CYTOBAND', 'DESCRIPTION', 'GO_ID', 'SEQUENCE']\n",
"{'ID': ['1', '2', '3', '4', '5'], 'COL': ['192', '192', '192', '192', '192'], 'ROW': [328.0, 326.0, 324.0, 322.0, 320.0], 'NAME': ['GE_BrightCorner', 'DarkCorner', 'DarkCorner', 'A_23_P117082', 'A_33_P3246448'], 'SPOT_ID': ['CONTROL', 'CONTROL', 'CONTROL', 'A_23_P117082', 'A_33_P3246448'], 'CONTROL_TYPE': ['pos', 'pos', 'pos', 'FALSE', 'FALSE'], 'REFSEQ': [nan, nan, nan, 'NM_015987', 'NM_080671'], 'GB_ACC': [nan, nan, nan, 'NM_015987', 'NM_080671'], 'LOCUSLINK_ID': [nan, nan, nan, 50865.0, 23704.0], 'GENE_SYMBOL': [nan, nan, nan, 'HEBP1', 'KCNE4'], 'GENE_NAME': [nan, nan, nan, 'heme binding protein 1', 'potassium voltage-gated channel, Isk-related family, member 4'], 'UNIGENE_ID': [nan, nan, nan, 'Hs.642618', 'Hs.348522'], 'ENSEMBL_ID': [nan, nan, nan, 'ENST00000014930', 'ENST00000281830'], 'ACCESSION_STRING': [nan, nan, nan, 'ref|NM_015987|ens|ENST00000014930|gb|AF117615|gb|BC016277', 'ref|NM_080671|ens|ENST00000281830|tc|THC2655788'], 'CHROMOSOMAL_LOCATION': [nan, nan, nan, 'chr12:13127906-13127847', 'chr2:223920197-223920256'], 'CYTOBAND': [nan, nan, nan, 'hs|12p13.1', 'hs|2q36.1'], 'DESCRIPTION': [nan, nan, nan, 'Homo sapiens heme binding protein 1 (HEBP1), mRNA [NM_015987]', 'Homo sapiens potassium voltage-gated channel, Isk-related family, member 4 (KCNE4), mRNA [NM_080671]'], 'GO_ID': [nan, nan, nan, 'GO:0005488(binding)|GO:0005576(extracellular region)|GO:0005737(cytoplasm)|GO:0005739(mitochondrion)|GO:0005829(cytosol)|GO:0007623(circadian rhythm)|GO:0020037(heme binding)', 'GO:0005244(voltage-gated ion channel activity)|GO:0005249(voltage-gated potassium channel activity)|GO:0006811(ion transport)|GO:0006813(potassium ion transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0016324(apical plasma membrane)'], 'SEQUENCE': [nan, nan, nan, 'AAGGGGGAAAATGTGATTTGTGCCTGATCTTTCATCTGTGATTCTTATAAGAGCTTTGTC', 'GCAAGTCTCTCTGCACCTATTAAAAAGTGATGTATATACTTCCTTCTTATTCTGTTGAGT']}\n",
"\n",
"Analyzing SPOT_ID.1 column for gene symbols:\n",
"\n",
"Gene data ID prefix: 4\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'ID' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'COL' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'ROW' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'NAME' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'SPOT_ID' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'REFSEQ' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'GB_ACC' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'LOCUSLINK_ID' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'GENE_SYMBOL' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'GENE_NAME' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'UNIGENE_ID' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'ENSEMBL_ID' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'ACCESSION_STRING' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'CHROMOSOMAL_LOCATION' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'CYTOBAND' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'DESCRIPTION' contains values matching gene data ID pattern\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column 'GO_ID' 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 'NAME' may contain gene-related information\n",
"Sample values: ['GE_BrightCorner', 'DarkCorner', 'DarkCorner']\n",
"Column 'GENE_SYMBOL' may contain gene-related information\n",
"Sample values: [nan, nan, nan]\n",
"Column 'GENE_NAME' may contain gene-related information\n",
"Sample values: [nan, nan, nan]\n",
"Column 'UNIGENE_ID' may contain gene-related information\n",
"Sample values: [nan, nan, nan]\n",
"Column 'DESCRIPTION' may contain gene-related information\n",
"Sample values: [nan, nan, nan]\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"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": "1ea40721",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9afd6753",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:48:01.203235Z",
"iopub.status.busy": "2025-03-25T06:48:01.203093Z",
"iopub.status.idle": "2025-03-25T06:48:04.555497Z",
"shell.execute_reply": "2025-03-25T06:48:04.554852Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Sample values in the mapping columns:\n",
"Probe column 'ID' values: ['1', '2', '3', '4', '5']\n",
"Gene column 'GENE_SYMBOL' values: [nan, nan, nan, 'HEBP1', 'KCNE4']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene mapping shape: (54295, 2)\n",
"Gene mapping preview:\n",
"{'ID': ['4', '5', '6', '7', '8'], 'Gene': ['HEBP1', 'KCNE4', 'BPIFA3', 'LOC100129869', 'IRG1']}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data after mapping, shape: (20353, 119)\n",
"First 5 gene symbols after mapping:\n",
"['A1BG', 'A1BG-AS1', 'A1CF', 'A2LD1', 'A2M']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene expression data saved to ../../output/preprocess/Atrial_Fibrillation/gene_data/GSE235307.csv\n"
]
}
],
"source": [
"# 1. Identify the key columns for mapping\n",
"prob_col = 'ID' # The numeric identifiers in gene_data (4, 5, 6, etc.)\n",
"gene_col = 'GENE_SYMBOL' # The gene symbols (HEBP1, KCNE4, etc.)\n",
"\n",
"# Let's verify the structure of our gene annotation dataframe\n",
"print(\"\\nSample values in the mapping columns:\")\n",
"print(f\"Probe column '{prob_col}' values: {gene_annotation[prob_col].head().tolist()}\")\n",
"print(f\"Gene column '{gene_col}' values: {gene_annotation[gene_col].head().tolist()}\")\n",
"\n",
"# 2. Get the gene mapping dataframe\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
"print(f\"\\nGene mapping shape: {gene_mapping.shape}\")\n",
"print(\"Gene mapping preview:\")\n",
"print(preview_df(gene_mapping))\n",
"\n",
"# 3. Convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(f\"\\nGene expression data after mapping, shape: {gene_data.shape}\")\n",
"print(\"First 5 gene symbols after mapping:\")\n",
"print(gene_data.index[:5].tolist())\n",
"\n",
"# Save the gene expression data to a CSV file\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "210d9635",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3e13de63",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T06:48:04.557478Z",
"iopub.status.busy": "2025-03-25T06:48:04.557345Z",
"iopub.status.idle": "2025-03-25T06:48:20.674067Z",
"shell.execute_reply": "2025-03-25T06:48:20.673392Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape before normalization: (20353, 119)\n",
"Gene data shape after normalization: (19847, 119)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data saved to ../../output/preprocess/Atrial_Fibrillation/gene_data/GSE235307.csv\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original clinical data preview:\n",
" !Sample_geo_accession \\\n",
"0 !Sample_characteristics_ch1 \n",
"1 !Sample_characteristics_ch1 \n",
"2 !Sample_characteristics_ch1 \n",
"3 !Sample_characteristics_ch1 \n",
"4 !Sample_characteristics_ch1 \n",
"\n",
" GSM7498589 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 63 \n",
"3 cardiopathy: ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498590 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 60 \n",
"3 cardiopathy: ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498591 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 60 \n",
"3 cardiopathy: non ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498592 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 72 \n",
"3 cardiopathy: ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498593 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 63 \n",
"3 cardiopathy: ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498594 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Female \n",
"2 age: 66 \n",
"3 cardiopathy: non ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498595 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 70 \n",
"3 cardiopathy: ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498596 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 64 \n",
"3 cardiopathy: non ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498597 ... \\\n",
"0 tissue: Whole blood ... \n",
"1 gender: Male ... \n",
"2 age: 63 ... \n",
"3 cardiopathy: ischemic ... \n",
"4 cardiac rhythm at start of the study: Sinus rh... ... \n",
"\n",
" GSM7498698 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 60 \n",
"3 cardiopathy: ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498699 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Female \n",
"2 age: 59 \n",
"3 cardiopathy: non ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498700 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 68 \n",
"3 cardiopathy: ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498701 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 77 \n",
"3 cardiopathy: non ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498702 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 57 \n",
"3 cardiopathy: ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498703 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Female \n",
"2 age: 62 \n",
"3 cardiopathy: non ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498704 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 66 \n",
"3 cardiopathy: ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498705 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 57 \n",
"3 cardiopathy: ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498706 \\\n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 65 \n",
"3 cardiopathy: ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
" GSM7498707 \n",
"0 tissue: Whole blood \n",
"1 gender: Male \n",
"2 age: 59 \n",
"3 cardiopathy: ischemic \n",
"4 cardiac rhythm at start of the study: Sinus rh... \n",
"\n",
"[5 rows x 120 columns]\n",
"Selected clinical data shape: (3, 119)\n",
"Clinical data preview:\n",
" GSM7498589 GSM7498590 GSM7498591 GSM7498592 \\\n",
"Atrial_Fibrillation 0.0 0.0 0.0 0.0 \n",
"Age 63.0 60.0 60.0 72.0 \n",
"Gender 1.0 1.0 1.0 1.0 \n",
"\n",
" GSM7498593 GSM7498594 GSM7498595 GSM7498596 \\\n",
"Atrial_Fibrillation 0.0 0.0 0.0 0.0 \n",
"Age 63.0 66.0 70.0 64.0 \n",
"Gender 1.0 0.0 1.0 1.0 \n",
"\n",
" GSM7498597 GSM7498598 ... GSM7498698 GSM7498699 \\\n",
"Atrial_Fibrillation 0.0 0.0 ... 0.0 0.0 \n",
"Age 63.0 61.0 ... 60.0 59.0 \n",
"Gender 1.0 1.0 ... 1.0 0.0 \n",
"\n",
" GSM7498700 GSM7498701 GSM7498702 GSM7498703 \\\n",
"Atrial_Fibrillation 0.0 0.0 1.0 0.0 \n",
"Age 68.0 77.0 57.0 62.0 \n",
"Gender 1.0 1.0 1.0 0.0 \n",
"\n",
" GSM7498704 GSM7498705 GSM7498706 GSM7498707 \n",
"Atrial_Fibrillation 1.0 1.0 1.0 0.0 \n",
"Age 66.0 57.0 65.0 59.0 \n",
"Gender 1.0 1.0 1.0 1.0 \n",
"\n",
"[3 rows x 119 columns]\n",
"Linked data shape before processing: (119, 19850)\n",
"Linked data preview (first 5 rows, 5 columns):\n",
" Atrial_Fibrillation Age Gender A1BG A1BG-AS1\n",
"GSM7498589 0.0 63.0 1.0 1215.921532 167.933502\n",
"GSM7498590 0.0 60.0 1.0 1042.240181 156.514231\n",
"GSM7498591 0.0 60.0 1.0 860.505266 153.778492\n",
"GSM7498592 0.0 72.0 1.0 1016.786080 164.688762\n",
"GSM7498593 0.0 63.0 1.0 930.371907 153.624856\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (119, 19850)\n",
"For the feature 'Atrial_Fibrillation', the least common label is '1.0' with 13 occurrences. This represents 10.92% of the dataset.\n",
"Quartiles for 'Age':\n",
" 25%: 55.0\n",
" 50% (Median): 63.0\n",
" 75%: 68.0\n",
"Min: 37.0\n",
"Max: 79.0\n",
"For the feature 'Gender', the least common label is '0.0' with 32 occurrences. This represents 26.89% of the dataset.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Atrial_Fibrillation/GSE235307.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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|