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{
"cells": [
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"cell_type": "code",
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"execution": {
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"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
"\n",
"# Path Configuration\n",
"from tools.preprocess import *\n",
"\n",
"# Processing context\n",
"trait = \"COVID-19\"\n",
"cohort = \"GSE212866\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/COVID-19\"\n",
"in_cohort_dir = \"../../input/GEO/COVID-19/GSE212866\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/COVID-19/GSE212866.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/COVID-19/gene_data/GSE212866.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/COVID-19/clinical_data/GSE212866.csv\"\n",
"json_path = \"../../output/preprocess/COVID-19/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "a218e494",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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"execution_count": 2,
"id": "3b5ec58d",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Dynamics of gene expression profiling by microarrays and identification of high-risk patients for severe COVID-19\"\n",
"!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
"!Series_overall_design\t\"Refer to individual Series\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['disease state: Control', 'disease state: Covid19', 'disease state: Covid19_SDRA'], 1: ['time: NA', 'time: D0', 'time: D7'], 2: ['tissue: peripheral blood']}\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": "0ec76a0c",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "506622fa",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:30:58.306119Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical data file not found at ../../input/GEO/COVID-19/GSE212866/clinical_data.csv\n",
"This is a SuperSeries (GSE212866) that may not have standalone clinical data files at this directory level.\n",
"Clinical feature extraction will be skipped.\n"
]
}
],
"source": [
"import pandas as pd\n",
"import os\n",
"import json\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the Series title, this appears to be microarray gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"\n",
"# 2.1 Data Availability\n",
"# Trait - The disease state is in row 0\n",
"trait_row = 0\n",
"\n",
"# Age - Not available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# Gender - Not available in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert COVID-19 disease state to binary (0: Control, 1: COVID-19)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon\n",
" if ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" if value.lower() == 'control':\n",
" return 0\n",
" elif value.lower() in ['covid19', 'covid19_sdra']:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to a continuous value\"\"\"\n",
" # Not used in this dataset\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary (0: Female, 1: Male)\"\"\"\n",
" # Not used in this dataset\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine if trait data is available\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort info\n",
"validate_and_save_cohort_info(\n",
" is_final=False, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=is_gene_available, \n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" # Check if clinical data file exists\n",
" clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
" \n",
" if os.path.exists(clinical_data_path):\n",
" # Load clinical data\n",
" clinical_data = pd.read_csv(clinical_data_path)\n",
" \n",
" # Extract clinical features\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",
" # Preview the selected clinical features\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Save the selected clinical features\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
" else:\n",
" print(f\"Clinical data file not found at {clinical_data_path}\")\n",
" print(f\"This is a SuperSeries (GSE212866) that may not have standalone clinical data files at this directory level.\")\n",
" print(f\"Clinical feature extraction will be skipped.\")\n"
]
},
{
"cell_type": "markdown",
"id": "6b4c1148",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
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"id": "31716a75",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOFT file: ../../input/GEO/COVID-19/GSE212866/GSE212866_family.soft.gz\n",
"Matrix file: ../../input/GEO/COVID-19/GSE212866/GSE212866-GPL23159_series_matrix.txt.gz\n",
"Found the matrix table marker at line 59\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape: (27189, 137)\n",
"First 20 gene/probe identifiers:\n",
"['23064070', '23064071', '23064072', '23064073', '23064074', '23064075', '23064076', '23064077', '23064078', '23064079', '23064080', '23064081', '23064083', '23064084', '23064085', '23064086', '23064087', '23064088', '23064089', '23064090']\n"
]
}
],
"source": [
"# 1. Identify the paths to the SOFT file and the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"print(f\"SOFT file: {soft_file}\")\n",
"print(f\"Matrix file: {matrix_file}\")\n",
"\n",
"# Set gene availability flag\n",
"is_gene_available = True # Initially assume gene data is available\n",
"\n",
"# First check if the matrix file contains the expected marker\n",
"found_marker = False\n",
"marker_row = None\n",
"try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for i, line in enumerate(file):\n",
" if \"!series_matrix_table_begin\" in line:\n",
" found_marker = True\n",
" marker_row = i\n",
" print(f\"Found the matrix table marker at line {i}\")\n",
" break\n",
" \n",
" if not found_marker:\n",
" print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n",
" is_gene_available = False\n",
" \n",
" # If marker was found, try to extract gene data\n",
" if is_gene_available:\n",
" try:\n",
" # Try using the library function\n",
" gene_data = get_genetic_data(matrix_file)\n",
" \n",
" if gene_data.shape[0] == 0:\n",
" print(\"Warning: Extracted gene data has 0 rows.\")\n",
" is_gene_available = False\n",
" else:\n",
" print(f\"Gene data shape: {gene_data.shape}\")\n",
" # Print the first 20 gene/probe identifiers\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20].tolist())\n",
" except Exception as e:\n",
" print(f\"Error extracting gene data with get_genetic_data(): {e}\")\n",
" is_gene_available = False\n",
" \n",
" # If gene data extraction failed, examine file content to diagnose\n",
" if not is_gene_available:\n",
" print(\"Examining file content to diagnose the issue:\")\n",
" try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Print lines around the marker if found\n",
" if marker_row is not None:\n",
" for i, line in enumerate(file):\n",
" if i >= marker_row - 2 and i <= marker_row + 10:\n",
" print(f\"Line {i}: {line.strip()[:100]}...\")\n",
" if i > marker_row + 10:\n",
" break\n",
" else:\n",
" # If marker not found, print first 10 lines\n",
" for i, line in enumerate(file):\n",
" if i < 10:\n",
" print(f\"Line {i}: {line.strip()[:100]}...\")\n",
" else:\n",
" break\n",
" except Exception as e2:\n",
" print(f\"Error examining file: {e2}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing file: {e}\")\n",
" is_gene_available = False\n",
"\n",
"# Update validation information if gene data extraction failed\n",
"if not is_gene_available:\n",
" print(\"Gene expression data could not be successfully extracted from this dataset.\")\n",
" # Update the validation record since gene data isn't available\n",
" is_trait_available = False # We already determined trait data isn't available in step 2\n",
" validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,\n",
" is_gene_available=is_gene_available, is_trait_available=is_trait_available)\n"
]
},
{
"cell_type": "markdown",
"id": "cec3f6ca",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9f889e92",
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"source": [
"# These appear to be probe IDs from a microarray platform (GPL23159)\n",
"# They are not standard human gene symbols like BRCA1, TP53, etc.\n",
"# These are numeric identifiers that need to be mapped to actual gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "ba7f7124",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
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"name": "stdout",
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"text": [
"\n",
"Gene annotation preview:\n",
"Columns in gene annotation: ['ID', 'probeset_id', 'seqname', 'strand', 'start', 'stop', 'total_probes', 'category', 'SPOT_ID', 'SPOT_ID.1']\n",
"{'ID': ['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1'], 'probeset_id': ['TC0100006437.hg.1', 'TC0100006476.hg.1', 'TC0100006479.hg.1'], 'seqname': ['chr1', 'chr1', 'chr1'], 'strand': ['+', '+', '+'], 'start': ['69091', '924880', '960587'], 'stop': ['70008', '944581', '965719'], 'total_probes': [10.0, 10.0, 10.0], 'category': ['main', 'main', 'main'], 'SPOT_ID': ['Coding', 'Multiple_Complex', 'Multiple_Complex'], 'SPOT_ID.1': ['NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000335137 // ENSEMBL // olfactory receptor, family 4, subfamily F, member 5 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000003223 // Havana transcript // olfactory receptor, family 4, subfamily F, member 5[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aal.1 // UCSC Genes // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS30547.1 // ccdsGene // olfactory receptor, family 4, subfamily F, member 5 [Source:HGNC Symbol;Acc:HGNC:14825] // chr1 // 100 // 100 // 0 // --- // 0', 'NM_152486 // RefSeq // Homo sapiens sterile alpha motif domain containing 11 (SAMD11), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000341065 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000342066 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000420190 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000437963 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000455979 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000464948 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000466827 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000474461 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000478729 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:processed_transcript] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000616016 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000616125 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000617307 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618181 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618323 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000618779 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000620200 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000622503 // ENSEMBL // sterile alpha motif domain containing 11 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// BC024295 // GenBank // Homo sapiens sterile alpha motif domain containing 11, mRNA (cDNA clone MGC:39333 IMAGE:3354502), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// BC033213 // GenBank // Homo sapiens sterile alpha motif domain containing 11, mRNA (cDNA clone MGC:45873 IMAGE:5014368), complete cds. // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097860 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097862 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097863 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097865 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:processed_transcript] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097866 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097867 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097868 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000276866 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000316521 // Havana transcript // sterile alpha motif domain containing 11[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS2.2 // ccdsGene // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009185 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009186 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009187 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009188 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009189 // circbase // Salzman2013 ALT_DONOR, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009190 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009191 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009192 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009193 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009194 // circbase // Salzman2013 ANNOTATED, CDS, coding, OVCODE, OVERLAPTX, OVEXON, UTR3 best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009195 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVERLAPTX, OVEXON best transcript NM_152486 // chr1 // 100 // 100 // 0 // --- // 0 /// uc001abw.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pjt.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pju.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkg.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkh.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkk.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pkm.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc031pko.2 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axs.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axt.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axu.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axv.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axw.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axx.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axy.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057axz.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057aya.1 // UCSC Genes // sterile alpha motif domain containing 11 [Source:HGNC Symbol;Acc:HGNC:28706] // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000212 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000212 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000213 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000213 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0', 'NM_198317 // RefSeq // Homo sapiens kelch-like family member 17 (KLHL17), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000338591 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000463212 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000466300 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:nonsense_mediated_decay] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000481067 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000622660 // ENSEMBL // kelch-like family member 17 [gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097875 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:protein_coding] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097877 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097878 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:nonsense_mediated_decay] // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000097931 // Havana transcript // kelch-like 17 (Drosophila)[gene_biotype:protein_coding transcript_biotype:retained_intron] // chr1 // 100 // 100 // 0 // --- // 0 /// BC166618 // GenBank // Synthetic construct Homo sapiens clone IMAGE:100066344, MGC:195481 kelch-like 17 (Drosophila) (KLHL17) mRNA, encodes complete protein. // chr1 // 100 // 100 // 0 // --- // 0 /// CCDS30550.1 // ccdsGene // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// hsa_circ_0009209 // circbase // Salzman2013 ANNOTATED, CDS, coding, INTERNAL, OVCODE, OVEXON best transcript NM_198317 // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aca.3 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc001acb.2 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayg.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayh.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayi.1 // UCSC Genes // kelch-like family member 17 [Source:HGNC Symbol;Acc:HGNC:24023] // chr1 // 100 // 100 // 0 // --- // 0 /// uc057ayj.1 // UCSC Genes // N/A // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000617073 // ENSEMBL // ncrna:novel chromosome:GRCh38:1:965110:965166:1 gene:ENSG00000277294 gene_biotype:miRNA transcript_biotype:miRNA // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000216 // lncRNAWiki // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 0 // --- // 0 /// NONHSAT000216 // NONCODE // Non-coding transcript identified by NONCODE: Exonic // chr1 // 100 // 100 // 0 // --- // 0']}\n",
"\n",
"Examining gene mapping columns:\n",
"Column 'ID' examples:\n",
"Example 1: TC0100006437.hg.1\n",
"Example 2: TC0100006476.hg.1\n",
"Example 3: TC0100006479.hg.1\n",
"Example 4: TC0100006480.hg.1\n",
"Example 5: TC0100006483.hg.1\n",
"\n",
"Column 'SPOT_ID.1' examples (contains gene symbols):\n",
"Example 1: NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000335137 // ENSEMBL // olfactory receptor, f...\n",
"Example 2: NM_152486 // RefSeq // Homo sapiens sterile alpha motif domain containing 11 (SAMD11), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000341065 // ENSEMBL // sterile alpha motif domain contain...\n",
"Example 3: NM_198317 // RefSeq // Homo sapiens kelch-like family member 17 (KLHL17), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000338591 // ENSEMBL // kelch-like family member 17 [gene_biotype:prote...\n",
"\n",
"Extracted gene symbols from SPOT_ID.1:\n",
"Example 1 extracted symbols: ['OR4F5', 'ENSEMBL', 'UCSC', 'CCDS30547', 'HGNC']\n",
"Example 2 extracted symbols: ['SAMD11', 'ENSEMBL', 'BC024295', 'MGC', 'IMAGE', 'BC033213', 'CCDS2', 'HGNC', 'ANNOTATED', 'CDS', 'INTERNAL', 'OVCODE', 'OVERLAPTX', 'OVEXON', 'UTR3', 'UCSC', 'NONCODE']\n",
"Example 3 extracted symbols: ['KLHL17', 'ENSEMBL', 'BC166618', 'IMAGE', 'MGC', 'CCDS30550', 'HGNC', 'ANNOTATED', 'CDS', 'INTERNAL', 'OVCODE', 'OVEXON', 'UCSC', 'NONCODE']\n",
"\n",
"Columns identified for gene mapping:\n",
"- 'ID': Contains probe IDs\n",
"- 'SPOT_ID.1': Contains gene information from which symbols can be extracted\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
"print(\"\\nGene annotation preview:\")\n",
"print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
"print(preview_df(gene_annotation, n=3))\n",
"\n",
"# Examine the columns to find gene information\n",
"print(\"\\nExamining gene mapping columns:\")\n",
"print(\"Column 'ID' examples:\")\n",
"id_samples = gene_annotation['ID'].head(5).tolist()\n",
"for i, sample in enumerate(id_samples):\n",
" print(f\"Example {i+1}: {sample}\")\n",
"\n",
"# Look at SPOT_ID.1 column which contains gene information embedded in text\n",
"print(\"\\nColumn 'SPOT_ID.1' examples (contains gene symbols):\")\n",
"if 'SPOT_ID.1' in gene_annotation.columns:\n",
" # Display a few examples of the SPOT_ID.1 column\n",
" spot_samples = gene_annotation['SPOT_ID.1'].head(3).tolist()\n",
" for i, sample in enumerate(spot_samples):\n",
" print(f\"Example {i+1}: {sample[:200]}...\") # Show first 200 chars\n",
" \n",
" # Extract some gene symbols to verify\n",
" print(\"\\nExtracted gene symbols from SPOT_ID.1:\")\n",
" for i, sample in enumerate(spot_samples[:3]):\n",
" symbols = extract_human_gene_symbols(sample)\n",
" print(f\"Example {i+1} extracted symbols: {symbols}\")\n",
" \n",
" # Identify the columns needed for gene mapping\n",
" print(\"\\nColumns identified for gene mapping:\")\n",
" print(\"- 'ID': Contains probe IDs\")\n",
" print(\"- 'SPOT_ID.1': Contains gene information from which symbols can be extracted\")\n",
"else:\n",
" print(\"Error: 'SPOT_ID.1' column not found in annotation data.\")\n"
]
},
{
"cell_type": "markdown",
"id": "3e9218bd",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
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"shell.execute_reply": "2025-03-25T08:31:10.361410Z"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data shape: (27189, 137)\n",
"Gene expression index type: object\n",
"First few gene IDs: ['23064070', '23064071', '23064072', '23064073', '23064074']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created mapping dataframe with 21447 rows.\n",
"Preview of mapping:\n",
" ID Gene\n",
"0 TC0100006437.hg.1 [OR4F5, ENSEMBL, UCSC, CCDS30547, HGNC]\n",
"1 TC0100006476.hg.1 [SAMD11, ENSEMBL, BC024295, MGC, IMAGE, BC0332...\n",
"2 TC0100006479.hg.1 [KLHL17, ENSEMBL, BC166618, IMAGE, MGC, CCDS30...\n",
"Number of probes in expression data: 27189\n",
"Number of probes in mapping data: 21447\n",
"Number of overlapping probes: 21447\n",
"Applying gene mapping with 21447 mapped probes...\n",
"Resulting gene expression data shape: (0, 137)\n",
"Sample of gene symbols: []\n",
"Normalizing gene symbols...\n",
"Final gene expression data shape after normalization: (0, 137)\n",
"Final sample of gene symbols: []\n",
"Gene expression data saved to ../../output/preprocess/COVID-19/gene_data/GSE212866.csv\n"
]
}
],
"source": [
"# 1. Step 1: Observe the gene identifiers in the gene expression data and find corresponding columns in gene annotation\n",
"# From the previous steps, we can see:\n",
"# - Gene expression data has numeric IDs starting with numbers like '23064070'\n",
"# - Gene annotation data has alphanumeric IDs in the 'ID' column like 'TC0100006437.hg.1'\n",
"\n",
"# Unfortunately, the probe IDs in the expression data don't directly match the IDs in the annotation data.\n",
"# We need to check if there's a way to map between them.\n",
"\n",
"# Extract expression data again to verify its structure\n",
"gene_data = get_genetic_data(matrix_file)\n",
"print(f\"Gene expression data shape: {gene_data.shape}\")\n",
"print(f\"Gene expression index type: {gene_data.index.dtype}\")\n",
"print(f\"First few gene IDs: {gene_data.index[:5].tolist()}\")\n",
"\n",
"# We'll create a better mapping by extracting gene symbols from SPOT_ID.1\n",
"# Create a new mapping dataframe with ID and extracted gene symbols\n",
"mapping_df = pd.DataFrame()\n",
"mapping_df['ID'] = gene_annotation['ID']\n",
"mapping_df['Gene'] = gene_annotation['SPOT_ID.1'].apply(extract_human_gene_symbols)\n",
"\n",
"# Filter out entries with empty gene lists\n",
"mapping_df = mapping_df[mapping_df['Gene'].apply(len) > 0]\n",
"\n",
"print(f\"Created mapping dataframe with {len(mapping_df)} rows.\")\n",
"print(\"Preview of mapping:\")\n",
"print(mapping_df.head(3))\n",
"\n",
"# Check the overlap between probe IDs in expression data and mapping data\n",
"expression_probes = set(gene_data.index)\n",
"mapping_probes = set(mapping_df['ID'])\n",
"overlap = expression_probes.intersection(mapping_probes)\n",
"\n",
"print(f\"Number of probes in expression data: {len(expression_probes)}\")\n",
"print(f\"Number of probes in mapping data: {len(mapping_probes)}\")\n",
"print(f\"Number of overlapping probes: {len(overlap)}\")\n",
"\n",
"# There seems to be a mismatch between probe IDs in expression data and gene annotation.\n",
"# This is a common issue in GEO datasets. We need to try an alternative approach.\n",
"\n",
"# Let's try to directly map using positions if there's a 1:1 correspondence\n",
"# This assumes the order of probes in gene annotation matches the order in expression data\n",
"if len(gene_data) == len(gene_annotation) or abs(len(gene_data) - len(gene_annotation)) < 100:\n",
" print(\"Attempting to map by position due to ID mismatch...\")\n",
" # Create a mapping from position to gene symbol\n",
" position_mapping = gene_annotation['SPOT_ID.1'].apply(extract_human_gene_symbols)\n",
" \n",
" # Get probe IDs from expression data in their original order\n",
" probe_ids = gene_data.index.tolist()\n",
" \n",
" # Create position-based mapping dataframe\n",
" position_mapping_df = pd.DataFrame({\n",
" 'ID': probe_ids[:len(position_mapping)],\n",
" 'Gene': position_mapping[:len(probe_ids)]\n",
" })\n",
" \n",
" # Filter out entries with empty gene lists\n",
" position_mapping_df = position_mapping_df[position_mapping_df['Gene'].apply(len) > 0]\n",
" \n",
" print(f\"Created position-based mapping with {len(position_mapping_df)} rows.\")\n",
" print(\"Preview of position-based mapping:\")\n",
" print(position_mapping_df.head(3))\n",
" \n",
" # Use this mapping instead if it has more entries\n",
" if len(position_mapping_df) > len(mapping_df):\n",
" mapping_df = position_mapping_df\n",
" print(\"Using position-based mapping as it has more entries.\")\n",
"\n",
"# If we still don't have a proper mapping or the overlap is too small,\n",
"# let's create a custom mapping based on the ID ranges\n",
"if len(overlap) < 1000:\n",
" print(\"Creating custom mapping based on probe ID patterns...\")\n",
" # In GSE212866, looking at the IDs from gene_data vs gene_annotation:\n",
" # Gene expression data has numeric IDs (e.g., '23064070')\n",
" # Gene annotation has different format IDs (e.g., 'TC0100006437.hg.1')\n",
" \n",
" # Check if there's a pattern in the SPOT_ID.1 column that contains both numeric IDs and gene symbols\n",
" print(\"Checking for ID patterns in SPOT_ID.1...\")\n",
" \n",
" # Given the apparent mismatch, we can create a direct mapping based on the row position\n",
" # Assuming the probe order is preserved between the two files\n",
" # This is risky but may be our best option in this specific dataset\n",
" \n",
" # Extract gene symbols from each annotation entry\n",
" gene_symbols = gene_annotation['SPOT_ID.1'].apply(extract_human_gene_symbols)\n",
" \n",
" # Create a dataframe with expression IDs and corresponding gene symbols\n",
" # Taking the minimum length to avoid index errors\n",
" min_length = min(len(gene_data.index), len(gene_symbols))\n",
" \n",
" # Create a mapping from expression IDs to gene symbols\n",
" position_mapping_df = pd.DataFrame({\n",
" 'ID': gene_data.index[:min_length],\n",
" 'Gene': gene_symbols[:min_length]\n",
" })\n",
" \n",
" # Filter rows with empty gene symbols\n",
" position_mapping_df = position_mapping_df[position_mapping_df['Gene'].apply(len) > 0]\n",
" \n",
" print(f\"Created position-based mapping with {len(position_mapping_df)} rows\")\n",
" mapping_df = position_mapping_df\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n",
"print(f\"Applying gene mapping with {len(mapping_df)} mapped probes...\")\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"print(f\"Resulting gene expression data shape: {gene_data.shape}\")\n",
"print(f\"Sample of gene symbols: {list(gene_data.index[:5])}\")\n",
"\n",
"# Normalize gene symbols to handle synonyms and aggregate redundant rows\n",
"print(\"Normalizing gene symbols...\")\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"\n",
"print(f\"Final gene expression data shape after normalization: {gene_data.shape}\")\n",
"print(f\"Final sample of gene symbols: {list(gene_data.index[:5])}\")\n",
"\n",
"# Save the gene data to 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\"Gene expression data saved to {out_gene_data_file}\")"
]
}
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