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{
"cells": [
{
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"metadata": {
"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 = \"GSE185658\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/COVID-19\"\n",
"in_cohort_dir = \"../../input/GEO/COVID-19/GSE185658\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/COVID-19/GSE185658.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/COVID-19/gene_data/GSE185658.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/COVID-19/clinical_data/GSE185658.csv\"\n",
"json_path = \"../../output/preprocess/COVID-19/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "362d1a18",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ebb746c0",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Rhinovirus-induced epithelial RIG-I inflammasome suppresses antiviral immunity and promotes inflammation in asthma and COVID-19\"\n",
"!Series_summary\t\"Balanced immune responses in airways of patients with asthma are crucial to succesful clearance of viral infection and proper asthma control.\"\n",
"!Series_summary\t\"We used microarrays to detail the global programme of gene expression data from bronchial brushings from control individuals and patients with asthma after rhinovirus infection in vivo.\"\n",
"!Series_overall_design\t\"Bronchial brushings from control individuals and patients with asthma around two weeks before (day -14) and four days after (day 4) experimental in vivo rhinovirus infection were used for RNA isolation and hybrydyzation with Affymetric microarrays.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['time: DAY14', 'time: DAY4'], 1: ['group: AsthmaHDM', 'group: AsthmaHDMNeg', 'group: Healthy'], 2: ['donor: DJ144', 'donor: DJ113', 'donor: DJ139', 'donor: DJ129', 'donor: DJ134', 'donor: DJ114', 'donor: DJ81', 'donor: DJ60', 'donor: DJ73', 'donor: DJ136', 'donor: DJ92', 'donor: DJ47', 'donor: DJ125', 'donor: DJ148', 'donor: DJ121', 'donor: DJ116', 'donor: DJ86', 'donor: DJ126', 'donor: DJ48', 'donor: DJ67', 'donor: DJ56', 'donor: DJ61', 'donor: DJ75', 'donor: DJ101']}\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": "4128a79e",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "753acb1a",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A new JSON file was created at: ../../output/preprocess/COVID-19/cohort_info.json\n"
]
},
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this is microarray data from bronchial brushings\n",
"# which indicates gene expression data is available\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# After reviewing the data, it's clear this dataset is about asthma and rhinovirus, not COVID-19\n",
"# Therefore, the COVID-19 trait we're interested in is not available in this dataset\n",
"trait_row = None # COVID-19 trait information is not available\n",
"age_row = None # Age information is not available\n",
"gender_row = None # Gender information is not available\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait information to binary values for COVID-19\"\"\"\n",
" # Since the trait is not available, this function won't be used\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age information to continuous values\"\"\"\n",
" # Not applicable as age data is not available\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender information to binary values\"\"\"\n",
" # Not applicable as gender data is not available\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Conduct initial filtering and save metadata\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",
"# Skip this step since trait_row is None (COVID-19 trait data is not available)\n"
]
},
{
"cell_type": "markdown",
"id": "d74c5573",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "df48e103",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T08:30:08.660574Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOFT file: ../../input/GEO/COVID-19/GSE185658/GSE185658_family.soft.gz\n",
"Matrix file: ../../input/GEO/COVID-19/GSE185658/GSE185658_series_matrix.txt.gz\n",
"Found the matrix table marker at line 63\n",
"Gene data shape: (32321, 48)\n",
"First 20 gene/probe identifiers:\n",
"['7892501', '7892502', '7892503', '7892504', '7892505', '7892506', '7892507', '7892508', '7892509', '7892510', '7892511', '7892512', '7892513', '7892514', '7892515', '7892516', '7892517', '7892518', '7892519', '7892520']\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": "1a30a9dc",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bbd75804",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# These don't appear to be human gene symbols but rather probe identifiers from a microarray platform\n",
"# They are numeric identifiers that likely need to be mapped to gene symbols\n",
"# Based on my biomedical knowledge, human gene symbols are typically alphanumeric (like BRCA1, TP53, etc.)\n",
"# These look like Illumina BeadChip probe IDs which require mapping to standard gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "9f5d354a",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "20684cd7",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gene annotation preview:\n",
"Columns in gene annotation: ['ID', 'GB_LIST', 'SPOT_ID', 'seqname', 'RANGE_GB', 'RANGE_STRAND', 'RANGE_START', 'RANGE_STOP', 'total_probes', 'gene_assignment', 'mrna_assignment', 'category']\n",
"{'ID': ['7896736', '7896738', '7896740'], 'GB_LIST': [nan, nan, 'NM_001004195,NM_001005240,NM_001005484,BC136848,BC136867,BC136907,BC136908'], 'SPOT_ID': ['chr1:53049-54936', 'chr1:63015-63887', 'chr1:69091-70008'], 'seqname': ['chr1', 'chr1', 'chr1'], 'RANGE_GB': ['NC_000001.10', 'NC_000001.10', 'NC_000001.10'], 'RANGE_STRAND': ['+', '+', '+'], 'RANGE_START': ['53049', '63015', '69091'], 'RANGE_STOP': ['54936', '63887', '70008'], 'total_probes': [7.0, 31.0, 24.0], 'gene_assignment': ['---', 'ENST00000328113 // OR4G2P // olfactory receptor, family 4, subfamily G, member 2 pseudogene // --- // --- /// ENST00000492842 // OR4G11P // olfactory receptor, family 4, subfamily G, member 11 pseudogene // --- // --- /// ENST00000588632 // OR4G1P // olfactory receptor, family 4, subfamily G, member 1 pseudogene // --- // ---', 'NM_001004195 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// NM_001005240 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000318050 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// ENST00000326183 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000585993 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136848 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136867 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 /// BC136907 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// BC136908 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682'], 'mrna_assignment': ['NONHSAT060105 // NONCODE // Non-coding transcript identified by NONCODE // chr1 // 100 // 100 // 7 // 7 // 0', 'ENST00000328113 // ENSEMBL // havana:known chromosome:GRCh38:15:101926805:101927707:-1 gene:ENSG00000183909 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000492842 // ENSEMBL // havana:known chromosome:GRCh38:1:62948:63887:1 gene:ENSG00000240361 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// ENST00000588632 // ENSEMBL // havana:known chromosome:GRCh38:19:104535:105471:1 gene:ENSG00000267310 gene_biotype:unprocessed_pseudogene transcript_biotype:unprocessed_pseudogene // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT000016 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT051704 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0 /// NONHSAT060106 // NONCODE // Non-coding transcript identified by NONCODE: Linc // chr1 // 100 // 100 // 31 // 31 // 0', 'NM_001004195 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 4 (OR4F4), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001005240 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 17 (OR4F17), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000318050 // ENSEMBL // ensembl:known chromosome:GRCh38:19:110643:111696:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000326183 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:15:101922042:101923095:-1 gene:ENSG00000177693 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000335137 // ENSEMBL // ensembl_havana_transcript:known chromosome:GRCh38:1:69091:70008:1 gene:ENSG00000186092 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000585993 // ENSEMBL // havana:known chromosome:GRCh38:19:107461:111696:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136848 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168462 IMAGE:9020839), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136867 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 17, mRNA (cDNA clone MGC:168481 IMAGE:9020858), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136907 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168521 IMAGE:9020898), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// BC136908 // GenBank // Homo sapiens olfactory receptor, family 4, subfamily F, member 4, mRNA (cDNA clone MGC:168522 IMAGE:9020899), complete cds. // chr1 // 100 // 100 // 24 // 24 // 0 /// ENST00000618231 // ENSEMBL // havana:known chromosome:GRCh38:19:110613:111417:1 gene:ENSG00000176695 gene_biotype:protein_coding transcript_biotype:retained_intron // chr1 // 100 // 88 // 21 // 21 // 0'], 'category': ['main', 'main', 'main']}\n",
"\n",
"Examining gene mapping columns:\n",
"Column 'ID' examples:\n",
"Example 1: 7896736\n",
"Example 2: 7896738\n",
"Example 3: 7896740\n",
"Example 4: 7896742\n",
"Example 5: 7896744\n",
"\n",
"Column 'gene_assignment' examples (contains gene symbols):\n",
"Example 1: ---...\n",
"Example 2: ENST00000328113 // OR4G2P // olfactory receptor, family 4, subfamily G, member 2 pseudogene // --- // --- /// ENST00000492842 // OR4G11P // olfactory receptor, family 4, subfamily G, member 11 pseudog...\n",
"Example 3: NM_001004195 // OR4F4 // olfactory receptor, family 4, subfamily F, member 4 // 15q26.3 // 26682 /// NM_001005240 // OR4F17 // olfactory receptor, family 4, subfamily F, member 17 // 19p13.3 // 81099 ...\n",
"\n",
"Extracted gene symbols from gene_assignment:\n",
"Example 1 extracted symbols: []\n",
"Example 2 extracted symbols: ['OR4G2P', 'OR4G11P', 'OR4G1P']\n",
"Example 3 extracted symbols: ['OR4F4', 'OR4F17', 'OR4F5', 'BC136848', 'BC136867', 'BC136907', 'BC136908']\n",
"\n",
"Columns identified for gene mapping:\n",
"- 'ID': Contains probe IDs\n",
"- 'gene_assignment': 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 gene_assignment column which contains gene symbols embedded in text\n",
"print(\"\\nColumn 'gene_assignment' examples (contains gene symbols):\")\n",
"if 'gene_assignment' in gene_annotation.columns:\n",
" # Display a few examples of the gene_assignment column\n",
" gene_samples = gene_annotation['gene_assignment'].head(3).tolist()\n",
" for i, sample in enumerate(gene_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 gene_assignment:\")\n",
" for i, sample in enumerate(gene_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(\"- 'gene_assignment': Contains gene information from which symbols can be extracted\")\n",
"else:\n",
" print(\"Error: 'gene_assignment' column not found in annotation data.\")\n"
]
},
{
"cell_type": "markdown",
"id": "702952b7",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Sample gene mapping (before filtering):\n",
" ID Gene\n",
"0 7896736 []\n",
"1 7896738 [OR4G2P, OR4G11P, OR4G1P]\n",
"2 7896740 [OR4F4, OR4F17, OR4F5, OR4F17, OR4F4, OR4F5, O...\n",
"3 7896742 [LOC728323, LOC101060626, LOC101060626, LOC101...\n",
"4 7896744 [OR4F29, OR4F3, OR4F16, OR4F21, OR4F21, OR4F3,...\n",
"Mapping entries with gene symbols: 25293\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of probe IDs in mapping that match expression data: 24520\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original expression data shape: (32321, 48)\n",
"Gene mapping entries: 25293\n",
"Resulting gene expression data shape: (25745, 48)\n",
"First 10 gene symbols: ['MT-TM', 'FAM87B', 'FAM87A', 'LINC01128', 'SAMD11', 'KLHL17', 'PLEKHN1', 'ISG15', 'AGRN', 'MIR200B']\n"
]
}
],
"source": [
"# 1. Analyze the gene identifiers in the expression data and gene annotation data\n",
"# Based on the preview, the 'ID' column in gene_annotation corresponds to the probe IDs in gene_data\n",
"# The gene symbols are in the 'gene_assignment' column and need to be extracted\n",
"\n",
"# Define a more specific extraction function for this dataset format\n",
"def extract_genes_from_assignment(text):\n",
" \"\"\"Extract gene symbols from gene_assignment field with specific format handling for this dataset\"\"\"\n",
" if not isinstance(text, str) or text == '---':\n",
" return []\n",
" \n",
" genes = []\n",
" # Gene symbols appear after '//' in the format \"ID // GENE // description\"\n",
" parts = text.split('///')\n",
" for part in parts:\n",
" subparts = part.split('//')\n",
" if len(subparts) > 1 and len(subparts[1].strip()) > 0:\n",
" gene = subparts[1].strip()\n",
" if gene != '---':\n",
" genes.append(gene)\n",
" return genes\n",
"\n",
"# 2. Create the gene mapping dataframe\n",
"# We'll use the 'ID' column and extract gene symbols from 'gene_assignment' column\n",
"mapping_df = gene_annotation[['ID', 'gene_assignment']].copy()\n",
"\n",
"# Process the mapping dataframe\n",
"mapping_df = mapping_df.dropna(subset=['gene_assignment']) # Drop rows without gene assignments\n",
"\n",
"# Use our custom extraction function instead of the generic one\n",
"mapping_df['Gene'] = mapping_df['gene_assignment'].apply(extract_genes_from_assignment)\n",
"\n",
"# Check intermediate results\n",
"print(\"Sample gene mapping (before filtering):\")\n",
"print(mapping_df[['ID', 'Gene']].head(5))\n",
"\n",
"# Only keep rows that have at least one gene symbol\n",
"mapping_df = mapping_df[mapping_df['Gene'].apply(len) > 0]\n",
"print(f\"Mapping entries with gene symbols: {len(mapping_df)}\")\n",
"\n",
"# Make sure IDs are strings\n",
"mapping_df['ID'] = mapping_df['ID'].astype(str)\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"expression_df = get_genetic_data(matrix_file)\n",
"\n",
"# Check if our probe IDs match the expression data index\n",
"common_ids = set(mapping_df['ID']) & set(expression_df.index.astype(str))\n",
"print(f\"Number of probe IDs in mapping that match expression data: {len(common_ids)}\")\n",
"\n",
"# Create a custom mapping function for debugging\n",
"def custom_map_probes_to_genes():\n",
" # Dictionary to store summed expression values for each gene\n",
" gene_expr = {}\n",
" \n",
" # Process each probe\n",
" for idx, row in mapping_df.iterrows():\n",
" probe_id = row['ID']\n",
" genes = row['Gene']\n",
" \n",
" # Skip if probe not in expression data\n",
" if probe_id not in expression_df.index:\n",
" continue\n",
" \n",
" # Skip if no genes to map to\n",
" if len(genes) == 0:\n",
" continue\n",
" \n",
" # Get probe expression values\n",
" probe_values = expression_df.loc[probe_id].to_dict()\n",
" \n",
" # Distribute expression values among genes\n",
" weight = 1.0 / len(genes)\n",
" for gene in genes:\n",
" if gene not in gene_expr:\n",
" gene_expr[gene] = {col: 0 for col in expression_df.columns}\n",
" \n",
" # Add weighted expression to each gene\n",
" for col, val in probe_values.items():\n",
" gene_expr[gene][col] += val * weight\n",
" \n",
" # Convert to dataframe\n",
" result = pd.DataFrame.from_dict(gene_expr, orient='index')\n",
" return result\n",
"\n",
"# Try our custom mapping approach for more control over the process\n",
"gene_data = custom_map_probes_to_genes()\n",
"\n",
"# Print information about the mapping and resulting gene expression data\n",
"print(f\"Original expression data shape: {expression_df.shape}\")\n",
"print(f\"Gene mapping entries: {len(mapping_df)}\")\n",
"print(f\"Resulting gene expression data shape: {gene_data.shape}\")\n",
"if gene_data.shape[0] > 0:\n",
" print(f\"First 10 gene symbols: {gene_data.index[:10].tolist()}\")\n",
"else:\n",
" print(\"No gene symbols were mapped successfully.\")\n",
"\n",
"# If custom mapping failed, try using the library function with explicit exploded mapping\n",
"if gene_data.shape[0] == 0:\n",
" print(\"Trying alternative mapping approach with exploded mapping dataframe...\")\n",
" \n",
" # Create an exploded version with one gene per row\n",
" exploded_df = mapping_df.explode('Gene')\n",
" exploded_df = exploded_df[exploded_df['Gene'] != ''] # Remove empty strings\n",
" \n",
" # Apply the gene mapping using the library function\n",
" gene_data = apply_gene_mapping(expression_df, exploded_df[['ID', 'Gene']])\n",
" \n",
" print(f\"Resulting gene expression data shape with exploded mapping: {gene_data.shape}\")\n",
" if gene_data.shape[0] > 0:\n",
" print(f\"First 10 gene symbols: {gene_data.index[:10].tolist()}\")\n"
]
},
{
"cell_type": "markdown",
"id": "e989347a",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
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"id": "b80d42b3",
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape after normalization: (24776, 48)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/COVID-19/gene_data/GSE185658.csv\n",
"No clinical data available for the trait of interest.\n",
"Abnormality detected in the cohort: GSE185658. Preprocessing failed.\n",
"Linked data not saved due to quality issues.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
"\n",
"# Create output directory if it doesn't exist\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"\n",
"# Save the normalized gene data\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Check if clinical data is available before trying to extract features\n",
"if trait_row is not None:\n",
" # Extract clinical features only if trait_row exists\n",
" clinical_features = geo_select_clinical_features(\n",
" 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",
" # Save clinical features\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
" \n",
" # Link clinical and genetic data\n",
" linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
" \n",
" # Handle missing values and check for bias\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"else:\n",
" # No clinical data available\n",
" print(\"No clinical data available for the trait of interest.\")\n",
" linked_data = pd.DataFrame() # Empty dataframe\n",
" is_biased = True # Dataset is biased since we have no trait data\n",
"\n",
"# 6. Validate and save cohort info\n",
"is_trait_available = trait_row is not None\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available,\n",
" is_biased=is_biased,\n",
" df=linked_data,\n",
" note=\"Dataset contains gene expression data but lacks COVID-19 trait information.\"\n",
")\n",
"\n",
"# 7. Save the linked data if it's usable\n",
"if is_usable:\n",
" # Create output directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" \n",
" # Save the linked data\n",
" linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Linked data not saved due to quality issues.\")"
]
}
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