{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "4bbf7eff", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:22:00.531946Z", "iopub.status.busy": "2025-03-25T08:22:00.531572Z", "iopub.status.idle": "2025-03-25T08:22:00.695759Z", "shell.execute_reply": "2025-03-25T08:22:00.695451Z" } }, "outputs": [], "source": [ "import sys\n", "import os\n", "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n", "\n", "# Path Configuration\n", "from tools.preprocess import *\n", "\n", "# Processing context\n", "trait = \"Chronic_obstructive_pulmonary_disease_(COPD)\"\n", "cohort = \"GSE64599\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Chronic_obstructive_pulmonary_disease_(COPD)\"\n", "in_cohort_dir = \"../../input/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64599\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64599.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE64599.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE64599.csv\"\n", "json_path = \"../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "44327683", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "5c7eaf98", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:22:00.697141Z", "iopub.status.busy": "2025-03-25T08:22:00.697002Z", "iopub.status.idle": "2025-03-25T08:22:00.828588Z", "shell.execute_reply": "2025-03-25T08:22:00.828233Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"The Role of Interleukin-23 in the Early Development of Emphysema in HIV1+ Smokers\"\n", "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n", "!Series_overall_design\t\"Refer to individual Series\"\n", "Sample Characteristics Dictionary:\n", "{0: ['smoking status: smoker'], 1: ['disease state: HIV+', 'disease state: HIV-'], 2: ['cell type: alveolar macrophage']}\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": "47bd27f7", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "1e04a76d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:22:00.829758Z", "iopub.status.busy": "2025-03-25T08:22:00.829648Z", "iopub.status.idle": "2025-03-25T08:22:00.849244Z", "shell.execute_reply": "2025-03-25T08:22:00.848984Z" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Analyzing dataset GSE64599 for the trait: Chronic_obstructive_pulmonary_disease_(COPD)\n", "\n", "# 1. Determine gene expression data availability\n", "# Based on the background information, this appears to be a study about HIV+ smokers and emphysema\n", "# However, there is no explicit indication that it contains gene expression data\n", "# The series is described as a \"SuperSeries composed of SubSeries\"\n", "# Without clear evidence of gene expression data, we'll set is_gene_available to False\n", "is_gene_available = False\n", "\n", "# 2. Variable availability and data type conversion\n", "\n", "# 2.1 Data Availability\n", "# From the sample characteristics dictionary:\n", "# Row 0: 'smoking status: smoker' - all are smokers, so this is a constant feature\n", "# Row 1: 'disease state: HIV+', 'disease state: HIV-' - this could be related to our trait (COPD)\n", "# Row 2: 'cell type: alveolar macrophage' - all are the same cell type, so this is a constant feature\n", "\n", "# For COPD trait, we might infer it from the HIV status, but this is not a direct indication of COPD\n", "# The background mentions \"emphysema\" which is related to COPD, but we don't have explicit COPD status\n", "trait_row = None # No clear indication of COPD status\n", "\n", "# Age and gender are not present in the sample characteristics\n", "age_row = None\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion\n", "# Since none of the variables are available, we'll define placeholder conversion functions\n", "\n", "def convert_trait(value):\n", " if value is None:\n", " return None\n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " # No clear mapping for COPD from the available data\n", " return None\n", "\n", "def convert_age(value):\n", " if value is None:\n", " return None\n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " try:\n", " return float(value) # Convert to continuous numeric value\n", " except:\n", " return None\n", "\n", "def convert_gender(value):\n", " if value is None:\n", " return None\n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip().lower()\n", " # Binary coding: female=0, male=1\n", " if 'female' in value or 'f' == value:\n", " return 0\n", " elif 'male' in value or 'm' == value:\n", " return 1\n", " return None\n", "\n", "# 3. Save metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Initial filtering and save cohort info\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "# Since trait_row is None, we skip this substep\n" ] }, { "cell_type": "markdown", "id": "5252e0a1", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "25e77cfa", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:22:00.850274Z", "iopub.status.busy": "2025-03-25T08:22:00.850174Z", "iopub.status.idle": "2025-03-25T08:22:01.007958Z", "shell.execute_reply": "2025-03-25T08:22:01.007590Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix file found: ../../input/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64599/GSE64599-GPL570_series_matrix.txt.gz\n", "Gene data shape: (54675, 34)\n", "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" ] } ], "source": [ "# 1. Get the SOFT and matrix file paths again \n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "print(f\"Matrix file found: {matrix_file}\")\n", "\n", "# 2. Use the get_genetic_data function from the library to get the gene_data\n", "try:\n", " gene_data = get_genetic_data(matrix_file)\n", " print(f\"Gene data shape: {gene_data.shape}\")\n", " \n", " # 3. Print the first 20 row IDs (gene or probe identifiers)\n", " print(\"First 20 gene/probe identifiers:\")\n", " print(gene_data.index[:20])\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n" ] }, { "cell_type": "markdown", "id": "3f5e57b1", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "7b6ec2db", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:22:01.009169Z", "iopub.status.busy": "2025-03-25T08:22:01.009061Z", "iopub.status.idle": "2025-03-25T08:22:01.010881Z", "shell.execute_reply": "2025-03-25T08:22:01.010616Z" } }, "outputs": [], "source": [ "# Examine gene identifiers to determine type\n", "# The identifiers like '1007_s_at', '1053_at', etc. are Affymetrix probe IDs\n", "# from GPL570 platform (HG-U133_Plus_2), not standard human gene symbols\n", "# They need to be mapped to gene symbols for proper analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "0100665b", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "f44e4664", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:22:01.011958Z", "iopub.status.busy": "2025-03-25T08:22:01.011861Z", "iopub.status.idle": "2025-03-25T08:22:04.552923Z", "shell.execute_reply": "2025-03-25T08:22:04.552360Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Gene annotation preview:\n", "Columns in gene annotation: ['ID', 'GB_ACC', 'SPOT_ID', 'Species Scientific Name', 'Annotation Date', 'Sequence Type', 'Sequence Source', 'Target Description', 'Representative Public ID', 'Gene Title', 'Gene Symbol', 'ENTREZ_GENE_ID', 'RefSeq Transcript ID', 'Gene Ontology Biological Process', 'Gene Ontology Cellular Component', 'Gene Ontology Molecular Function']\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", "\n", "Searching for platform information in SOFT file:\n", "!Series_platform_id = GPL570\n", "\n", "Searching for gene symbol information in SOFT file:\n", "Found references to gene symbols:\n", "!Platform_relation = Alternative to: GPL19918 (Gene symbol version, 10K)\n", "!Platform_relation = Alternative to: GPL20182 (Gene Symbol Version)\n", "#Gene Symbol = A gene symbol, when one is available (from UniGene).\n", "ID\tGB_ACC\tSPOT_ID\tSpecies Scientific Name\tAnnotation Date\tSequence Type\tSequence Source\tTarget Description\tRepresentative Public ID\tGene Title\tGene Symbol\tENTREZ_GENE_ID\tRefSeq Transcript ID\tGene Ontology Biological Process\tGene Ontology Cellular Component\tGene Ontology Molecular Function\n", "\n", "Checking for additional annotation files in the directory:\n", "['GSE64599-GPL570_series_matrix.txt.gz']\n" ] } ], "source": [ "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n", "print(\"\\nGene annotation preview:\")\n", "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n", "print(preview_df(gene_annotation, n=5))\n", "\n", "# Let's look for platform information in the SOFT file to understand the annotation better\n", "print(\"\\nSearching for platform information in SOFT file:\")\n", "with gzip.open(soft_file, 'rt') as f:\n", " for i, line in enumerate(f):\n", " if '!Series_platform_id' in line:\n", " print(line.strip())\n", " break\n", " if i > 100: # Limit search to first 100 lines\n", " print(\"Platform ID not found in first 100 lines\")\n", " break\n", "\n", "# Check if the SOFT file includes any reference to gene symbols\n", "print(\"\\nSearching for gene symbol information in SOFT file:\")\n", "with gzip.open(soft_file, 'rt') as f:\n", " gene_symbol_lines = []\n", " for i, line in enumerate(f):\n", " if 'GENE_SYMBOL' in line or 'gene_symbol' in line.lower() or 'symbol' in line.lower():\n", " gene_symbol_lines.append(line.strip())\n", " if i > 1000 and len(gene_symbol_lines) > 0: # Limit search but ensure we found something\n", " break\n", " \n", " if gene_symbol_lines:\n", " print(\"Found references to gene symbols:\")\n", " for line in gene_symbol_lines[:5]: # Show just first 5 matches\n", " print(line)\n", " else:\n", " print(\"No explicit gene symbol references found in first 1000 lines\")\n", "\n", "# Look for alternative annotation files or references in the directory\n", "print(\"\\nChecking for additional annotation files in the directory:\")\n", "all_files = os.listdir(in_cohort_dir)\n", "print([f for f in all_files if 'annotation' in f.lower() or 'platform' in f.lower() or 'gpl' in f.lower()])\n" ] }, { "cell_type": "markdown", "id": "4f736879", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "dcb23ce5", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:22:04.554880Z", "iopub.status.busy": "2025-03-25T08:22:04.554723Z", "iopub.status.idle": "2025-03-25T08:22:04.768656Z", "shell.execute_reply": "2025-03-25T08:22:04.768007Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping shape: (45782, 2)\n", "\n", "Gene mapping preview (first 5 rows):\n", "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'Gene': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A']}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Converted gene expression data shape: (21278, 34)\n", "\n", "Gene expression data preview (first 5 genes):\n", "{'GSM1575085': [92.1398, 79.0887, 65.27392, 5640.59, 95.5121], 'GSM1575086': [87.1394, 29.9709, 146.892, 10535.9921, 66.9938], 'GSM1575087': [136.88, 26.5951, 157.2644, 5956.396000000001, 72.5051], 'GSM1575088': [169.799, 97.2438, 117.82857, 4601.3861, 78.371], 'GSM1575089': [149.572, 10.3971, 98.7029, 10920.6929, 102.916], 'GSM1575090': [137.513, 86.5203, 93.7718, 2494.5275, 68.8274], 'GSM1575091': [110.333, 100.256, 174.49509999999998, 8309.0367, 51.4439], 'GSM1575092': [180.974, 14.1813, 122.40554, 4087.6662, 66.1764], 'GSM1575093': [198.452, 20.6847, 38.26969, 19431.861, 63.7477], 'GSM1575094': [292.859, 98.5018, 186.88909999999998, 1693.569, 179.683], 'GSM1575095': [263.956, 126.756, 29.742820000000002, 2644.6365, 86.2573], 'GSM1575096': [281.398, 33.0788, 125.3596, 14857.9386, 128.955], 'GSM1575097': [110.974, 114.035, 119.18323000000001, 7373.889, 126.573], 'GSM1575098': [184.895, 24.0193, 223.3882, 7023.8676000000005, 78.5218], 'GSM1575099': [150.462, 33.4138, 64.06935, 8385.894, 166.629], 'GSM1575100': [328.235, 86.7121, 46.0417, 8781.6258, 35.9349], 'GSM1575101': [149.205, 11.58, 44.5853, 8490.7829, 51.4204], 'GSM1575102': [188.724, 66.4475, 40.40601, 3907.9797000000003, 43.1654], 'GSM1575103': [89.8971, 64.8786, 80.62304, 8167.0832, 79.2761], 'GSM1575104': [105.46, 54.5157, 184.92245, 7811.94788, 105.419], 'GSM1575105': [134.264, 69.3734, 129.4333, 3827.6415, 64.9657], 'GSM1575106': [128.661, 11.0667, 23.43952, 7428.9536, 131.392], 'GSM1575107': [110.41, 86.5152, 49.27545, 3584.2893999999997, 287.724], 'GSM1575108': [274.25, 18.4148, 41.418800000000005, 13896.3244, 63.3593], 'GSM1575109': [153.247, 44.1327, 80.5389, 5912.15264, 161.385], 'GSM1575110': [321.185, 4.8377, 84.57979999999999, 3208.1601, 141.255], 'GSM1575111': [132.092, 27.0553, 36.5748, 14937.8089, 74.8281], 'GSM1575112': [202.59, 143.296, 88.72113, 5214.2342, 73.0636], 'GSM1575113': [164.716, 125.528, 106.35991000000001, 2478.6250999999997, 158.03], 'GSM1575114': [188.705, 17.7108, 163.9334, 6350.7761, 81.921], 'GSM1575115': [169.86, 67.092, 149.91933, 1336.0174, 51.2612], 'GSM1575116': [144.154, 18.2916, 283.4104, 2184.26181, 77.6057], 'GSM1575117': [196.409, 17.2501, 208.1441, 3854.5369, 75.139], 'GSM1575118': [189.303, 14.6572, 136.55450000000002, 1826.5802999999999, 58.243]}\n" ] } ], "source": [ "# 1. Determine which columns in gene_annotation contain the probe IDs and gene symbols\n", "# From the preview, we can see:\n", "# - 'ID' contains probe IDs like '1007_s_at' which match the gene expression data index\n", "# - 'Gene Symbol' contains gene symbols like 'DDR1 /// MIR4640'\n", "\n", "# 2. Get gene mapping dataframe using the appropriate columns\n", "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')\n", "print(f\"Gene mapping shape: {gene_mapping.shape}\")\n", "\n", "# Preview the mapping to confirm it's correct\n", "print(\"\\nGene mapping preview (first 5 rows):\")\n", "print(preview_df(gene_mapping, n=5))\n", "\n", "# 3. Apply gene mapping to convert probe-level measurements to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"\\nConverted gene expression data shape: {gene_data.shape}\")\n", "\n", "# 4. Preview the gene expression data after mapping\n", "print(\"\\nGene expression data preview (first 5 genes):\")\n", "print(preview_df(gene_data, n=5))\n", "\n", "# 5. Update the gene availability flag based on the successful gene mapping\n", "is_gene_available = True\n" ] }, { "cell_type": "markdown", "id": "6c8a8149", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "a5ab4d4c", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:22:04.770515Z", "iopub.status.busy": "2025-03-25T08:22:04.770395Z", "iopub.status.idle": "2025-03-25T08:22:05.229778Z", "shell.execute_reply": "2025-03-25T08:22:05.229161Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data shape: (19845, 34)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE64599.csv\n", "Dataset usability status: False\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\"Normalized gene data shape: {normalized_gene_data.shape}\")\n", "\n", "# Save 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", "# Since we determined in Step 2 that trait_row is None (is_trait_available = False),\n", "# we can't create clinical features or perform linking with trait data\n", "\n", "# Update gene availability flag based on successful gene mapping\n", "is_gene_available = True\n", "is_trait_available = False # As determined in Step 2\n", "\n", "# Create a note about the dataset\n", "note = \"Dataset contains gene expression data from alveolar macrophages of HIV+ and HIV- smokers. No COPD trait information is available.\"\n", "\n", "# Create a minimal dataframe with proper structure for the validation function\n", "dummy_df = pd.DataFrame(index=normalized_gene_data.index[:5], columns=normalized_gene_data.columns[:5])\n", "\n", "# Conduct final quality validation\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=False, # Set to False when no trait data\n", " df=dummy_df, # Provide a non-empty DataFrame with structure\n", " note=note\n", ")\n", "\n", "print(f\"Dataset usability status: {is_usable}\")" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }