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{
"cells": [
{
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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 = \"Breast_Cancer\"\n",
"cohort = \"GSE270721\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Breast_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Breast_Cancer/GSE270721\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Breast_Cancer/GSE270721.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Breast_Cancer/gene_data/GSE270721.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Breast_Cancer/clinical_data/GSE270721.csv\"\n",
"json_path = \"../../output/preprocess/Breast_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "2042a430",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ce8df06b",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"LncRNAs expressed in triple negative breast cancer of Mexican patients\"\n",
"!Series_summary\t\"We provide a detailed analysis of the expression of lncRNAs in TNBC versus Luminal tumors of breast cancer patients\"\n",
"!Series_overall_design\t\"We employed HTA 2.0 microarrays to analyze the transcriptome of TNBC and Luminal tumors.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: Formalin-fixed paraffin-embedded tissue sections'], 1: ['population: Mexican Patient'], 2: ['age: 78.00', 'age: 74.00', 'age: 48.00', 'age: not available', 'age: 49.00', 'age: 50.00', 'age: 40.00', 'age: 55.00', 'age: 70.00', 'age: 63.00', 'age: 42.00', 'age: 64.00', 'age: 38.00', 'age: 82.00', 'age: 45.00', 'age: 36.00', 'age: 44.00', 'age: 65.00', 'age: 66.00', 'age: 73.00']}\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": "f438e0f7",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a707fc90",
"metadata": {
"execution": {
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"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# From the Series_title and Series_summary, this appears to be a microarray study of lncRNA expressions\n",
"# in breast cancer. HTA 2.0 microarrays were used which should contain gene expression data.\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# For trait: There's no direct mention of breast cancer subtypes in the sample characteristics\n",
"# but the background info mentions TNBC vs Luminal tumors, suggesting trait data should be available somewhere else\n",
"trait_row = None # Not available in the sample characteristics dictionary\n",
"\n",
"# For age: We can see age data in key 2\n",
"age_row = 2\n",
"\n",
"# For gender: No gender information in sample characteristics, but likely all female as it's breast cancer\n",
"gender_row = None # Not available\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" # Not used since trait_row is None\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" try:\n",
" # Extract value after colon and strip whitespace\n",
" if ':' in value:\n",
" age_str = value.split(':', 1)[1].strip()\n",
" if age_str.lower() == 'not available':\n",
" return None\n",
" # Convert to float (continuous variable)\n",
" return float(age_str)\n",
" return None\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" # Not used since gender_row is None\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Trait data is not available in sample characteristics, so we set is_trait_available to False\n",
"is_trait_available = False if trait_row is None else True\n",
"\n",
"# Initial validation of dataset usability\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",
"# We skip this step since trait_row is None (clinical trait data not available in the sample characteristics)\n"
]
},
{
"cell_type": "markdown",
"id": "b0d90d2f",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8bf24a60",
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"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOFT file: ../../input/GEO/Breast_Cancer/GSE270721/GSE270721_family.soft.gz\n",
"Matrix file: ../../input/GEO/Breast_Cancer/GSE270721/GSE270721_series_matrix.txt.gz\n",
"Found the matrix table marker in the file.\n",
"Gene data shape: (67528, 30)\n",
"First 20 gene/probe identifiers:\n",
"['TC01000001.hg.1', 'TC01000002.hg.1', 'TC01000003.hg.1', 'TC01000004.hg.1', 'TC01000005.hg.1', 'TC01000006.hg.1', 'TC01000007.hg.1', 'TC01000008.hg.1', 'TC01000009.hg.1', 'TC01000010.hg.1', 'TC01000011.hg.1', 'TC01000012.hg.1', 'TC01000013.hg.1', 'TC01000014.hg.1', 'TC01000015.hg.1', 'TC01000016.hg.1', 'TC01000017.hg.1', 'TC01000018.hg.1', 'TC01000019.hg.1', 'TC01000020.hg.1']\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",
"try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for line in file:\n",
" if \"!series_matrix_table_begin\" in line:\n",
" found_marker = True\n",
" break\n",
" \n",
" if found_marker:\n",
" print(\"Found the matrix table marker in the file.\")\n",
" else:\n",
" print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n",
" \n",
" # Try to extract gene data from the matrix file\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",
" \n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n",
" is_gene_available = False\n",
" \n",
" # Try to diagnose the file format\n",
" print(\"Examining file content to diagnose the issue:\")\n",
" try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" for i, line in enumerate(file):\n",
" if i < 10: # Print first 10 lines to diagnose\n",
" print(f\"Line {i}: {line.strip()[:100]}...\") # Print first 100 chars of each line\n",
" else:\n",
" break\n",
" except Exception as e2:\n",
" print(f\"Error examining file: {e2}\")\n",
"\n",
"if not is_gene_available:\n",
" print(\"Gene expression data could not be successfully extracted from this dataset.\")\n"
]
},
{
"cell_type": "markdown",
"id": "89361f17",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "661afcba",
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"source": [
"# The identifiers like 'TC01000001.hg.1' are not standard human gene symbols\n",
"# These appear to be Affymetrix/Thermo Fisher probe IDs which need to be mapped to gene symbols\n",
"# Standard human gene symbols would look like BRCA1, TP53, etc.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "752a2fc4",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
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"execution": {
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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', 'gene_assignment', 'mrna_assignment', 'swissprot', 'unigene', 'category', 'locus type', 'notes', 'SPOT_ID']\n",
"{'ID': ['TC01000001.hg.1', 'TC01000002.hg.1', 'TC01000003.hg.1'], 'probeset_id': ['TC01000001.hg.1', 'TC01000002.hg.1', 'TC01000003.hg.1'], 'seqname': ['chr1', 'chr1', 'chr1'], 'strand': ['+', '+', '+'], 'start': ['11869', '29554', '69091'], 'stop': ['14409', '31109', '70008'], 'total_probes': [49.0, 60.0, 30.0], 'gene_assignment': ['NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000456328 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // 9p24.3 // 100287596 /// ENST00000456328 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102', 'ENST00000408384 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000408384 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000408384 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000408384 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// ENST00000469289 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000469289 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000469289 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000469289 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// ENST00000473358 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000473358 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000473358 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000473358 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// OTTHUMT00000002841 // OTTHUMG00000000959 // NULL // --- // --- /// OTTHUMT00000002841 // RP11-34P13.3 // NULL // --- // --- /// OTTHUMT00000002840 // OTTHUMG00000000959 // NULL // --- // --- /// OTTHUMT00000002840 // RP11-34P13.3 // NULL // --- // ---', 'NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// OTTHUMT00000003223 // OR4F5 // NULL // --- // ---'], 'mrna_assignment': ['NR_046018 // RefSeq // Homo sapiens DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 (DDX11L1), non-coding RNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000456328 // ENSEMBL // cdna:known chromosome:GRCh37:1:11869:14409:1 gene:ENSG00000223972 gene_biotype:pseudogene transcript_biotype:processed_transcript // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aaa.3 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc010nxq.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// uc010nxr.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0', 'ENST00000408384 // ENSEMBL // ncrna:miRNA chromosome:GRCh37:1:30366:30503:1 gene:ENSG00000221311 gene_biotype:miRNA transcript_biotype:miRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000469289 // ENSEMBL // havana:lincRNA chromosome:GRCh37:1:30267:31109:1 gene:ENSG00000243485 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000473358 // ENSEMBL // havana:lincRNA chromosome:GRCh37:1:29554:31097:1 gene:ENSG00000243485 gene_biotype:lincRNA transcript_biotype:lincRNA // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000002841 // Havana transcript // cdna:all chromosome:VEGA52:1:30267:31109:1 Gene:OTTHUMG00000000959 // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000002840 // Havana transcript // cdna:all chromosome:VEGA52:1:29554:31097:1 Gene:OTTHUMG00000000959 // chr1 // 100 // 100 // 0 // --- // 0', 'NM_001005484 // RefSeq // Homo sapiens olfactory receptor, family 4, subfamily F, member 5 (OR4F5), mRNA. // chr1 // 100 // 100 // 0 // --- // 0 /// ENST00000335137 // ENSEMBL // cdna:known chromosome:GRCh37:1:69091:70008:1 gene:ENSG00000186092 gene_biotype:protein_coding transcript_biotype:protein_coding // chr1 // 100 // 100 // 0 // --- // 0 /// uc001aal.1 // UCSC Genes // --- // chr1 // 100 // 100 // 0 // --- // 0 /// OTTHUMT00000003223 // Havana transcript // cdna:all chromosome:VEGA52:1:69091:70008:1 Gene:OTTHUMG00000001094 // chr1 // 100 // 100 // 0 // --- // 0'], 'swissprot': ['NR_046018 // B7ZGX0 /// NR_046018 // B7ZGX2 /// NR_046018 // B7ZGX7 /// NR_046018 // B7ZGX8 /// ENST00000456328 // B7ZGX0 /// ENST00000456328 // B7ZGX2 /// ENST00000456328 // B7ZGX3 /// ENST00000456328 // B7ZGX7 /// ENST00000456328 // B7ZGX8 /// ENST00000456328 // Q6ZU42', '---', 'NM_001005484 // Q8NH21 /// ENST00000335137 // Q8NH21'], 'unigene': ['NR_046018 // Hs.714157 // testis| normal| adult /// ENST00000456328 // Hs.719844 // brain| testis| normal /// ENST00000456328 // Hs.714157 // testis| normal| adult /// ENST00000456328 // Hs.618434 // testis| normal', 'ENST00000469289 // Hs.622486 // eye| normal| adult /// ENST00000469289 // Hs.729632 // testis| normal /// ENST00000469289 // Hs.742718 // testis /// ENST00000473358 // Hs.622486 // eye| normal| adult /// ENST00000473358 // Hs.729632 // testis| normal /// ENST00000473358 // Hs.742718 // testis', 'NM_001005484 // Hs.554500 // --- /// ENST00000335137 // Hs.554500 // ---'], 'category': ['main', 'main', 'main'], 'locus type': ['Coding', 'Coding', 'Coding'], 'notes': ['---', '---', '---'], 'SPOT_ID': ['chr1(+):11869-14409', 'chr1(+):29554-31109', 'chr1(+):69091-70008']}\n",
"\n",
"Examining gene_assignment column format (first 3 rows):\n",
"Row 0: NR_046018 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102 /// ENST00000456328 // DDX11L5 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 5 // 9p24.3 // 100287596 /// ENST00000456328 // DDX11L1 // DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 like 1 // 1p36.33 // 100287102\n",
"Row 1: ENST00000408384 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000408384 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000408384 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000408384 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// ENST00000469289 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000469289 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000469289 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000469289 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// ENST00000473358 // MIR1302-11 // microRNA 1302-11 // --- // 100422919 /// ENST00000473358 // MIR1302-10 // microRNA 1302-10 // --- // 100422834 /// ENST00000473358 // MIR1302-9 // microRNA 1302-9 // --- // 100422831 /// ENST00000473358 // MIR1302-2 // microRNA 1302-2 // --- // 100302278 /// OTTHUMT00000002841 // OTTHUMG00000000959 // NULL // --- // --- /// OTTHUMT00000002841 // RP11-34P13.3 // NULL // --- // --- /// OTTHUMT00000002840 // OTTHUMG00000000959 // NULL // --- // --- /// OTTHUMT00000002840 // RP11-34P13.3 // NULL // --- // ---\n",
"Row 2: NM_001005484 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// ENST00000335137 // OR4F5 // olfactory receptor, family 4, subfamily F, member 5 // 1p36.33 // 79501 /// OTTHUMT00000003223 // OR4F5 // NULL // --- // ---\n",
"\n",
"Gene assignment column completeness: 70753/2096623 rows (3.37%)\n",
"\n",
"Attempting to extract gene symbols from first few entries:\n",
"Row 0: Extracted gene symbol: DDX11L1\n",
"Row 1: Extracted gene symbol: MIR1302-11\n",
"Row 2: Extracted gene symbol: OR4F5\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",
"# Looking at the output, it appears the gene symbols are in the 'gene_assignment' column\n",
"# Examine the gene_assignment column which appears to contain gene symbols\n",
"print(\"\\nExamining gene_assignment column format (first 3 rows):\")\n",
"if 'gene_assignment' in gene_annotation.columns:\n",
" for i in range(min(3, len(gene_annotation))):\n",
" print(f\"Row {i}: {gene_annotation['gene_assignment'].iloc[i]}\")\n",
"\n",
" # Check the quality and completeness of the mapping\n",
" non_null_assignments = gene_annotation['gene_assignment'].notna().sum()\n",
" total_rows = len(gene_annotation)\n",
" print(f\"\\nGene assignment column completeness: {non_null_assignments}/{total_rows} rows ({non_null_assignments/total_rows:.2%})\")\n",
" \n",
" # Try to extract some gene symbols from the gene_assignment column\n",
" print(\"\\nAttempting to extract gene symbols from first few entries:\")\n",
" for i in range(min(3, len(gene_annotation))):\n",
" assignment = gene_annotation['gene_assignment'].iloc[i]\n",
" if isinstance(assignment, str):\n",
" # The format appears to be: accession_id // gene_symbol // description // location // gene_id\n",
" parts = assignment.split('//')\n",
" if len(parts) > 1:\n",
" gene_symbol = parts[1].strip()\n",
" print(f\"Row {i}: Extracted gene symbol: {gene_symbol}\")\n"
]
},
{
"cell_type": "markdown",
"id": "c773e467",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
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"id": "2a5cba96",
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"execution": {
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"shell.execute_reply": "2025-03-25T07:02:44.316555Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mapping dataframe shape before cleaning: (70753, 2)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape after mapping: (71527, 30)\n",
"First 5 gene symbols:\n",
"['A-', 'A-2', 'A-52', 'A-575C2', 'A-E']\n",
"\n",
"First 5 expression values for sample GSM8350629:\n",
"Gene\n",
"A- 20.84375\n",
"A-2 1.31400\n",
"A-52 4.69000\n",
"A-575C2 2.84000\n",
"A-E 1.73500\n",
"Name: GSM8350629, dtype: float64\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Breast_Cancer/gene_data/GSE270721.csv\n"
]
}
],
"source": [
"# 1. Based on observation, the probe IDs are in the 'ID' column of the gene annotation data\n",
"# and gene symbols are in the 'gene_assignment' column, but need to be extracted from a specific format\n",
"\n",
"# First, load the data files\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"gene_data_probes = get_genetic_data(matrix_file)\n",
"\n",
"# 2. Create a mapping dataframe\n",
"# We'll use 'ID' for probe identifiers, and rename 'gene_assignment' to 'Gene' for the mapping function\n",
"mapping_df = gene_annotation[['ID', 'gene_assignment']].copy()\n",
"mapping_df = mapping_df.dropna() # Remove rows with missing gene assignments\n",
"mapping_df = mapping_df.rename(columns={'gene_assignment': 'Gene'}) # Rename column to match the function expectation\n",
"\n",
"# Print the mapping dataframe shape to track progress\n",
"print(f\"Mapping dataframe shape before cleaning: {mapping_df.shape}\")\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"# The extract_human_gene_symbols function will parse the gene_assignment field to get gene symbols\n",
"gene_data = apply_gene_mapping(gene_data_probes, mapping_df)\n",
"\n",
"# Print the shape of the gene expression data\n",
"print(f\"Gene data shape after mapping: {gene_data.shape}\")\n",
"\n",
"# Preview first few rows of gene data\n",
"print(\"First 5 gene symbols:\")\n",
"print(gene_data.index[:5].tolist())\n",
"\n",
"# Preview the first few values for a sample (column)\n",
"if gene_data.shape[1] > 0:\n",
" first_col = gene_data.columns[0]\n",
" print(f\"\\nFirst 5 expression values for sample {first_col}:\")\n",
" print(gene_data[first_col].head())\n",
"\n",
"# Save the gene data to file\n",
"# Create directory if it doesn't exist\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}\")\n"
]
},
{
"cell_type": "markdown",
"id": "ae3f3dfd",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a0291dcd",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T07:02:44.833613Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene data shape before normalization: (71527, 30)\n",
"Gene data shape after normalization: (24018, 30)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Breast_Cancer/gene_data/GSE270721.csv\n",
"No trait data (Breast_Cancer) available in this dataset based on previous analysis.\n",
"Cannot proceed with data linking due to missing trait or gene data.\n",
"Abnormality detected in the cohort: GSE270721. Preprocessing failed.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"try:\n",
" # Make sure the directory exists\n",
" os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
" \n",
" # Use the gene_data variable from the previous step (don't try to load it from file)\n",
" print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
" \n",
" # Apply normalization to gene symbols\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",
" # 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",
" # Use the normalized data for further processing\n",
" gene_data = normalized_gene_data\n",
" is_gene_available = True\n",
"except Exception as e:\n",
" print(f\"Error normalizing gene data: {e}\")\n",
" is_gene_available = False\n",
"\n",
"# 2. Load clinical data - respecting the analysis from Step 2\n",
"# From Step 2, we determined:\n",
"# trait_row = None # No Breast Cancer subtype data available\n",
"# age_row = 2\n",
"# gender_row = None\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Skip clinical feature extraction when trait_row is None\n",
"if is_trait_available:\n",
" try:\n",
" # Load the clinical data from file\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",
" # Extract clinical features\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",
" gender_row=gender_row,\n",
" convert_gender=convert_gender,\n",
" age_row=age_row,\n",
" convert_age=convert_age\n",
" )\n",
" \n",
" print(f\"Extracted clinical data shape: {clinical_features.shape}\")\n",
" print(\"Preview of clinical data (first 5 samples):\")\n",
" print(clinical_features.iloc[:, :5])\n",
" \n",
" # Save the properly extracted clinical data\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 data saved to {out_clinical_data_file}\")\n",
" except Exception as e:\n",
" print(f\"Error extracting clinical data: {e}\")\n",
" is_trait_available = False\n",
"else:\n",
" print(f\"No trait data ({trait}) available in this dataset based on previous analysis.\")\n",
"\n",
"# 3. Link clinical and genetic data if both are available\n",
"if is_trait_available and is_gene_available:\n",
" try:\n",
" # Debug the column names to ensure they match\n",
" print(f\"Gene data columns (first 5): {gene_data.columns[:5].tolist()}\")\n",
" print(f\"Clinical data columns (first 5): {clinical_features.columns[:5].tolist()}\")\n",
" \n",
" # Check for common sample IDs\n",
" common_samples = set(gene_data.columns).intersection(clinical_features.columns)\n",
" print(f\"Found {len(common_samples)} common samples between gene and clinical data\")\n",
" \n",
" if len(common_samples) > 0:\n",
" # Link the clinical and genetic data\n",
" linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n",
" print(f\"Initial linked data shape: {linked_data.shape}\")\n",
" \n",
" # Debug the trait values before handling missing values\n",
" print(\"Preview of linked data (first 5 rows, first 5 columns):\")\n",
" print(linked_data.iloc[:5, :5])\n",
" \n",
" # Handle missing values\n",
" linked_data = handle_missing_values(linked_data, trait)\n",
" print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
" \n",
" if linked_data.shape[0] > 0:\n",
" # Check for bias in trait and demographic features\n",
" is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
" \n",
" # Validate the data quality and save cohort info\n",
" note = \"Dataset contains gene expression data from triple negative breast cancer vs. luminal tumors, but no explicit breast cancer subtype labels in the sample characteristics.\"\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=note\n",
" )\n",
" \n",
" # Save the linked data if it's 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(\"Data not usable for the trait study - not saving final linked data.\")\n",
" else:\n",
" print(\"After handling missing values, no samples remain.\")\n",
" 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=True,\n",
" df=pd.DataFrame(),\n",
" note=\"No valid samples after handling missing values.\"\n",
" )\n",
" else:\n",
" print(\"No common samples found between gene expression and clinical data.\")\n",
" 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=True,\n",
" df=pd.DataFrame(),\n",
" note=\"No common samples between gene expression and clinical data.\"\n",
" )\n",
" except Exception as e:\n",
" print(f\"Error linking or processing data: {e}\")\n",
" 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=True, # Assume biased if there's an error\n",
" df=pd.DataFrame(), # Empty dataframe for metadata\n",
" note=f\"Error in data processing: {str(e)}\"\n",
" )\n",
"else:\n",
" # Create an empty DataFrame for metadata purposes\n",
" empty_df = pd.DataFrame()\n",
" \n",
" # We can't proceed with linking if either trait or gene data is missing\n",
" print(\"Cannot proceed with data linking due to missing trait or gene data.\")\n",
" 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=True, # Data is unusable if we're missing components\n",
" df=empty_df, # Empty dataframe for metadata\n",
" note=\"Dataset contains gene expression data from triple negative breast cancer vs. luminal tumors, but no explicit breast cancer subtype labels in the sample characteristics.\"\n",
" )"
]
}
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