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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 = \"Stomach_Cancer\"\n",
"cohort = \"GSE172197\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Stomach_Cancer\"\n",
"in_cohort_dir = \"../../input/GEO/Stomach_Cancer/GSE172197\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Stomach_Cancer/GSE172197.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Stomach_Cancer/gene_data/GSE172197.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Stomach_Cancer/clinical_data/GSE172197.csv\"\n",
"json_path = \"../../output/preprocess/Stomach_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "79aaa325",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Files in the cohort directory:\n",
"['GSE172197_family.soft.gz', 'GSE172197_series_matrix.txt.gz']\n",
"Identified SOFT files: ['GSE172197_family.soft.gz']\n",
"Identified matrix files: ['GSE172197_series_matrix.txt.gz']\n",
"\n",
"Background Information:\n",
"!Series_title\t\"mRNA expression profiles of newly established 49 gastric cancer cell lines.\"\n",
"!Series_summary\t\"Establishment and molecular characterization of 49 peritoneally-metastatic gastric cancer cell lines from 18 patients’ ascites.\"\n",
"!Series_summary\t\"We performed comprehensive transcriptome analyses using microarrays of our established gastric cancer cell lines.\"\n",
"!Series_overall_design\t\"49 cancer cell lines\"\n",
"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['cell line: NSC-10C', 'cell line: NSC-10X1A', 'cell line: NSC-10X1aA', 'cell line: NSC-10X1aF', 'cell line: NSC-10X1aX1', 'cell line: NSC-10X1aX1a', 'cell line: NSC-10X1F', 'cell line: NSC-11C', 'cell line: NSC-11X1', 'cell line: NSC-11X1a', 'cell line: NSC-15CA', 'cell line: NSC-15CF', 'cell line: NSC-16C', 'cell line: NSC-16CX1F', 'cell line: NSC-17CA', 'cell line: NSC-17CF', 'cell line: NSC-18C-1', 'cell line: NSC-18C-2', 'cell line: NSC-18C-3', 'cell line: NSC-20C', 'cell line: NSC-20CX1', 'cell line: NSC-20CX1a', 'cell line: NSC-20CX2', 'cell line: NSC-20CX2a', 'cell line: NSC-24C', 'cell line: NSC-24CX1a', 'cell line: NSC-26C-1', 'cell line: NSC-26C-2', 'cell line: NSC-28C', 'cell line: NSC-28CX1']}\n"
]
}
],
"source": [
"# 1. Let's first list the directory contents to understand what files are available\n",
"import os\n",
"\n",
"print(\"Files in the cohort directory:\")\n",
"files = os.listdir(in_cohort_dir)\n",
"print(files)\n",
"\n",
"# Adapt file identification to handle different naming patterns\n",
"soft_files = [f for f in files if 'soft' in f.lower() or '.soft' in f.lower() or '_soft' in f.lower()]\n",
"matrix_files = [f for f in files if 'matrix' in f.lower() or '.matrix' in f.lower() or '_matrix' in f.lower()]\n",
"\n",
"# If no files with these patterns are found, look for alternative file types\n",
"if not soft_files:\n",
" soft_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n",
"if not matrix_files:\n",
" matrix_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n",
"\n",
"print(\"Identified SOFT files:\", soft_files)\n",
"print(\"Identified matrix files:\", matrix_files)\n",
"\n",
"# Use the first files found, if any\n",
"if len(soft_files) > 0 and len(matrix_files) > 0:\n",
" soft_file = os.path.join(in_cohort_dir, soft_files[0])\n",
" matrix_file = os.path.join(in_cohort_dir, matrix_files[0])\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(\"\\nBackground Information:\")\n",
" print(background_info)\n",
" print(\"\\nSample Characteristics Dictionary:\")\n",
" print(sample_characteristics_dict)\n",
"else:\n",
" print(\"No appropriate files found in the directory.\")\n"
]
},
{
"cell_type": "markdown",
"id": "8f030b18",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
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"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Analyze dataset based on background information and sample characteristics\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# From the background information, this dataset contains \"mRNA expression profiles\" of gastric cancer cell lines\n",
"# and mentions \"comprehensive transcriptome analyses using microarrays\", indicating gene expression data\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# From the sample characteristics, there's no explicit disease/control status, age, or gender info\n",
"# The cell lines are derived from gastric cancer, but they're all cancer cell lines without healthy controls\n",
"trait_row = None # No trait data (cancer vs. control) available\n",
"age_row = None # No age data available\n",
"gender_row = None # No gender data available\n",
"\n",
"# 2.2 Data Type Conversion Functions (even though we don't have the data, we define these for completeness)\n",
"def convert_trait(value):\n",
" # Since there's no trait data, this function won't be used\n",
" if value is None:\n",
" return None\n",
" \n",
" value = value.split(\":\", 1)[1].strip() if \":\" in value else value.strip()\n",
" \n",
" if \"cancer\" in value.lower():\n",
" return 1\n",
" elif \"normal\" in value.lower() or \"control\" in value.lower() or \"healthy\" in value.lower():\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" # Since there's no age data, this function won't be used\n",
" if value is None:\n",
" return None\n",
" \n",
" value = value.split(\":\", 1)[1].strip() if \":\" in value else value.strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except ValueError:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" # Since there's no gender data, this function won't be used\n",
" if value is None:\n",
" return None\n",
" \n",
" value = value.split(\":\", 1)[1].strip() if \":\" in value else value.strip()\n",
" \n",
" if value.lower() in [\"female\", \"f\"]:\n",
" return 0\n",
" elif value.lower() in [\"male\", \"m\"]:\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# The dataset has gene expression data but no trait data (no control samples)\n",
"is_trait_available = trait_row is not None\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"# Since trait_row is None, skip this substep\n"
]
},
{
"cell_type": "markdown",
"id": "576a56bf",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e3cf9d0b",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First 20 gene/probe identifiers:\n",
"Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n",
" '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n",
" '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n",
" '1552263_at', '1552264_a_at', '1552266_at'],\n",
" dtype='object', name='ID')\n",
"\n",
"Gene expression data shape: (54675, 49)\n"
]
}
],
"source": [
"# Use the helper function to get the proper file paths\n",
"soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Extract gene expression data\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file_path)\n",
" \n",
" # Print the first 20 row IDs (gene or probe identifiers)\n",
" print(\"First 20 gene/probe identifiers:\")\n",
" print(gene_data.index[:20])\n",
" \n",
" # Print shape to understand the dataset dimensions\n",
" print(f\"\\nGene expression data shape: {gene_data.shape}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {e}\")\n"
]
},
{
"cell_type": "markdown",
"id": "a244f25a",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5a6e963e",
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"execution": {
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"outputs": [],
"source": [
"# Review gene identifiers\n",
"\n",
"# The identifiers in this dataset (like '1007_s_at', '1053_at', etc.) are Affymetrix probe IDs\n",
"# from a microarray platform, not human gene symbols.\n",
"# These are probe set IDs that need to be mapped to official gene symbols.\n",
"\n",
"# Microarray platforms like Affymetrix use these probe IDs which need to be converted\n",
"# to standard gene symbols before analysis.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "e241eb5d",
"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": [
"Gene annotation preview:\n",
"{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"try:\n",
" # Use the correct variable name from previous steps\n",
" gene_annotation = get_gene_annotation(soft_file_path)\n",
" \n",
" # 2. Preview the gene annotation dataframe\n",
" print(\"Gene annotation preview:\")\n",
" print(preview_df(gene_annotation))\n",
" \n",
"except UnicodeDecodeError as e:\n",
" print(f\"Unicode decoding error: {e}\")\n",
" print(\"Trying alternative approach...\")\n",
" \n",
" # Read the file with Latin-1 encoding which is more permissive\n",
" import gzip\n",
" import pandas as pd\n",
" \n",
" # Manually read the file line by line with error handling\n",
" data_lines = []\n",
" with gzip.open(soft_file_path, 'rb') as f:\n",
" for line in f:\n",
" # Skip lines starting with prefixes we want to filter out\n",
" line_str = line.decode('latin-1')\n",
" if not line_str.startswith('^') and not line_str.startswith('!') and not line_str.startswith('#'):\n",
" data_lines.append(line_str)\n",
" \n",
" # Create dataframe from collected lines\n",
" if data_lines:\n",
" gene_data_str = '\\n'.join(data_lines)\n",
" gene_annotation = pd.read_csv(pd.io.common.StringIO(gene_data_str), sep='\\t', low_memory=False)\n",
" print(\"Gene annotation preview (alternative method):\")\n",
" print(preview_df(gene_annotation))\n",
" else:\n",
" print(\"No valid gene annotation data found after filtering.\")\n",
" gene_annotation = pd.DataFrame()\n",
" \n",
"except Exception as e:\n",
" print(f\"Error extracting gene annotation data: {e}\")\n",
" gene_annotation = pd.DataFrame()\n"
]
},
{
"cell_type": "markdown",
"id": "8de95f89",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dea324f0",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T04:02:35.499041Z",
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"shell.execute_reply": "2025-03-25T04:02:35.759978Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Will map from ID to Gene Symbol\n",
"Gene mapping dataframe shape: (45782, 2)\n",
"First few rows of mapping dataframe:\n",
" ID Gene\n",
"0 1007_s_at DDR1 /// MIR4640\n",
"1 1053_at RFC2\n",
"2 117_at HSPA6\n",
"3 121_at PAX8\n",
"4 1255_g_at GUCA1A\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data shape after mapping: (21278, 49)\n",
"First few genes and their expression values:\n",
" GSM5243830 GSM5243831 GSM5243832 GSM5243833 GSM5243834 \\\n",
"Gene \n",
"A1BG 3.573000 68.113460 90.104184 30.680121 93.146829 \n",
"A1BG-AS1 40.920908 9.646156 32.501140 99.996111 5.923232 \n",
"A1CF 7221.275999 854.425711 3318.552915 1904.586094 2881.262597 \n",
"A2M 139.724070 200.880079 188.067771 241.584986 145.263539 \n",
"A2M-AS1 74.847679 152.428161 216.075561 211.613157 191.989181 \n",
"\n",
" GSM5243835 GSM5243836 GSM5243844 GSM5243845 GSM5243846 \\\n",
"Gene \n",
"A1BG 16.142043 38.469045 18.348572 241.560810 147.073023 \n",
"A1BG-AS1 9.977724 16.494795 16.934687 13.573422 17.666532 \n",
"A1CF 2768.575985 2619.561153 4297.496644 4920.868032 3166.096344 \n",
"A2M 128.468536 295.821907 130.798072 182.676172 273.020101 \n",
"A2M-AS1 96.828453 564.454099 40.530955 111.246631 56.651065 \n",
"\n",
" ... GSM5243876 GSM5243877 GSM5243878 GSM5243879 GSM5243880 \\\n",
"Gene ... \n",
"A1BG ... 11.048915 6.432920 4.895026 152.673836 57.916281 \n",
"A1BG-AS1 ... 15.102019 8.124654 9.748983 22.763570 13.154513 \n",
"A1CF ... 4122.816498 4534.454489 609.147122 862.557757 709.461012 \n",
"A2M ... 162.091288 153.870047 142.185765 168.487475 223.531843 \n",
"A2M-AS1 ... 56.846255 62.474780 29.885526 265.306989 98.838131 \n",
"\n",
" GSM5243881 GSM5243882 GSM5243883 GSM5243884 GSM5243885 \n",
"Gene \n",
"A1BG 120.050563 73.365358 2.113031 41.790723 23.842575 \n",
"A1BG-AS1 10.089153 126.689486 5.354573 6.207188 4.940293 \n",
"A1CF 505.929431 5760.558573 4233.161054 188.188680 128.923380 \n",
"A2M 97.507943 305.550971 238.681936 226.748801 72.531452 \n",
"A2M-AS1 66.334940 105.798793 58.951741 237.566572 70.327629 \n",
"\n",
"[5 rows x 49 columns]\n",
"Number of unique genes after mapping: 21278\n"
]
}
],
"source": [
"# Looking at the annotation data from the previous step, we can see:\n",
"# 'ID' column contains probe IDs like '1007_s_at' which match the gene expression data indices\n",
"# 'Gene Symbol' column contains the gene symbols we need for mapping\n",
"\n",
"# 1. Identify the columns for mapping\n",
"probe_id_column = 'ID'\n",
"gene_symbol_column = 'Gene Symbol'\n",
"\n",
"print(f\"Will map from {probe_id_column} to {gene_symbol_column}\")\n",
"\n",
"# 2. Get a gene mapping dataframe\n",
"mapping_df = get_gene_mapping(gene_annotation, probe_id_column, gene_symbol_column)\n",
"print(f\"Gene mapping dataframe shape: {mapping_df.shape}\")\n",
"print(\"First few rows of mapping dataframe:\")\n",
"print(mapping_df.head())\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
"print(\"First few genes and their expression values:\")\n",
"print(gene_data.head())\n",
"\n",
"# Check the number of unique genes after mapping\n",
"print(f\"Number of unique genes after mapping: {len(gene_data.index.unique())}\")\n"
]
},
{
"cell_type": "markdown",
"id": "9e8363b3",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7d043830",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T04:02:35.761394Z",
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"shell.execute_reply": "2025-03-25T04:02:42.558542Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data shape: (19845, 49)\n",
"First few normalized gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Stomach_Cancer/gene_data/GSE172197.csv\n",
"Clinical data saved to ../../output/preprocess/Stomach_Cancer/clinical_data/GSE172197.csv\n",
"Linked data shape: (49, 19846)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (49, 19846)\n",
"All samples are gastric cancer cell lines without controls, marking trait as biased.\n",
"Data quality check result: Not usable\n",
"Data quality check failed. The dataset contains only gastric cancer samples without controls, making it unsuitable for association studies.\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the obtained gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"print(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\n",
"\n",
"# Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Create a minimal clinical DataFrame with sample IDs\n",
"# We don't have trait data as determined in Step 2, but we still need to follow the process\n",
"sample_ids = normalized_gene_data.columns\n",
"clinical_features = pd.DataFrame(index=sample_ids)\n",
"\n",
"# Add placeholder for trait column (all labeled as 1 since all samples are gastric cancer)\n",
"clinical_features[trait] = 1 # All samples are gastric cancer cell lines\n",
"\n",
"# Save the clinical data even though it's minimal\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",
"\n",
"# Link the clinical and genetic data (even though clinical data is minimal)\n",
"linked_data = geo_link_clinical_genetic_data(clinical_features.T, normalized_gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# 3. Handle missing values\n",
"# Since all our samples are cancer cell lines with the same trait value (1),\n",
"# and we don't have age/gender data, we can just check for missing values in gene data\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 4. Determine whether the trait is biased\n",
"# Since all samples are cancer cell lines (no controls), the trait is completely biased\n",
"is_trait_biased = True\n",
"print(\"All samples are gastric cancer cell lines without controls, marking trait as biased.\")\n",
"\n",
"# 5. Conduct quality check and save the cohort information\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=True, # We have trait data (all cancer), though it's biased\n",
" is_biased=is_trait_biased, \n",
" df=linked_data,\n",
" note=\"Dataset contains gene expression data from gastric cancer cell lines but lacks control samples for comparison.\"\n",
")\n",
"\n",
"# 6. We've determined the data is not usable for association studies due to biased trait\n",
"print(f\"Data quality check result: {'Usable' if is_usable else 'Not usable'}\")\n",
"if is_usable:\n",
" # This block likely won't execute but included for completeness\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(f\"Data quality check failed. The dataset contains only gastric cancer samples without controls, making it unsuitable for association studies.\")"
]
}
],
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