{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "48eab886", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:37.486018Z", "iopub.status.busy": "2025-03-25T06:00:37.485907Z", "iopub.status.idle": "2025-03-25T06:00:37.648873Z", "shell.execute_reply": "2025-03-25T06:00:37.648541Z" } }, "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 = \"Osteoarthritis\"\n", "cohort = \"GSE93720\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Osteoarthritis\"\n", "in_cohort_dir = \"../../input/GEO/Osteoarthritis/GSE93720\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Osteoarthritis/GSE93720.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Osteoarthritis/gene_data/GSE93720.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Osteoarthritis/clinical_data/GSE93720.csv\"\n", "json_path = \"../../output/preprocess/Osteoarthritis/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "944e8c67", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "e94dd1e9", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:37.650338Z", "iopub.status.busy": "2025-03-25T06:00:37.650189Z", "iopub.status.idle": "2025-03-25T06:00:37.858896Z", "shell.execute_reply": "2025-03-25T06:00:37.858541Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Transcriptional response of human synovial fibroblasts to TNF and IL-17A (Microarray Data 2)\"\n", "!Series_summary\t\"The combination of TNF and IL-17A has synergistic effects on the transcription of genes encoding chemokines and cytokines.\"\n", "!Series_overall_design\t\"Human fibroblasts derived from 2 individuals with rheumatoid arthritis and 2 individuals with osteoarthritis were stimulated with TNF, IL-17A, or TNF and IL-17A and gene expression was assayed with microarrays.\"\n", "!Series_overall_design\t\"\"\n", "!Series_overall_design\t\"Microarray Data 2 (n = 48): 1 time point (24 hours), 3 passages, 4 cell lines, 4 stimulations (Baseline; None; TNF; TNF and IL-17A).\"\n", "!Series_overall_design\t\"\"\n", "!Series_overall_design\t\"GeneChip™ Human Genome U133 Plus 2.0 Array (\"\"affy_hg_u133_plus_2\"\" on Bioconductor).\"\n", "Sample Characteristics Dictionary:\n", "{0: ['disease: OA', 'disease: RA'], 1: ['donor: OA6', 'donor: RA32', 'donor: OA502', 'donor: RA449'], 2: ['il6: high', 'il6: low'], 3: ['passage: 6', 'passage: 7', 'passage: 8'], 4: ['stimulation: TNF_IL17', 'stimulation: None', 'stimulation: TNF', 'stimulation: Baseline']}\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": "ebf43043", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "e389c2c8", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:37.860309Z", "iopub.status.busy": "2025-03-25T06:00:37.860107Z", "iopub.status.idle": "2025-03-25T06:00:37.867619Z", "shell.execute_reply": "2025-03-25T06:00:37.867305Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Data Preview:\n", "{'GSM3713911': [1.0], 'GSM3713912': [1.0], 'GSM3713913': [1.0], 'GSM3713914': [1.0], 'GSM3713915': [1.0], 'GSM3713916': [1.0], 'GSM3713917': [1.0], 'GSM3713918': [0.0], 'GSM3713919': [0.0], 'GSM3713920': [0.0], 'GSM3713921': [0.0], 'GSM3713922': [0.0], 'GSM3713923': [0.0], 'GSM3713924': [0.0], 'GSM3713925': [0.0], 'GSM3713926': [1.0], 'GSM3713927': [1.0], 'GSM3713928': [1.0], 'GSM3713929': [1.0], 'GSM3713930': [1.0], 'GSM3713931': [1.0], 'GSM3713932': [1.0], 'GSM3713933': [1.0], 'GSM3713934': [0.0], 'GSM3713935': [0.0], 'GSM3713936': [0.0], 'GSM3713937': [0.0], 'GSM3713938': [0.0], 'GSM3713939': [0.0], 'GSM3713940': [0.0], 'GSM3713941': [0.0], 'GSM3713942': [1.0], 'GSM3713943': [1.0], 'GSM3713944': [1.0], 'GSM3713945': [0.0], 'GSM3713946': [0.0], 'GSM3713947': [0.0], 'GSM3713948': [1.0], 'GSM3713949': [1.0], 'GSM3713950': [1.0], 'GSM3713951': [0.0], 'GSM3713952': [0.0], 'GSM3713953': [0.0], 'GSM3713954': [1.0], 'GSM3713955': [0.0], 'GSM3713956': [1.0], 'GSM3713957': [0.0], 'GSM3713958': [1.0]}\n", "Clinical data saved to ../../output/preprocess/Osteoarthritis/clinical_data/GSE93720.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# Yes, this dataset contains gene expression data from microarrays\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "\n", "# For the trait (Osteoarthritis), we can find it in the disease field (key 0)\n", "trait_row = 0 # The \"disease: OA\" or \"disease: RA\" records\n", "\n", "# No age information is available in the sample characteristics\n", "age_row = None\n", "\n", "# No gender information is available in the sample characteristics\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion\n", "def convert_trait(value):\n", " \"\"\"Convert the disease value to binary (1 for Osteoarthritis, 0 for other conditions)\"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " \n", " # Extract the value after the colon\n", " if \":\" in value:\n", " value = value.split(\":\", 1)[1].strip()\n", " \n", " # OA means Osteoarthritis, RA means Rheumatoid Arthritis\n", " if value.upper() == \"OA\":\n", " return 1 # Has Osteoarthritis\n", " elif value.upper() == \"RA\":\n", " return 0 # Does not have Osteoarthritis (has RA instead)\n", " return None\n", "\n", "# No need to define convert_age and convert_gender since these data are not available\n", "\n", "# 3. Save Metadata\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 (if trait data is available)\n", "if trait_row is not None:\n", " # Extract clinical features\n", " clinical_df = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=None,\n", " gender_row=gender_row,\n", " convert_gender=None\n", " )\n", " \n", " # Preview the results\n", " print(\"Clinical Data Preview:\")\n", " print(preview_df(clinical_df))\n", " \n", " # Save the clinical data to a CSV file\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_df.to_csv(out_clinical_data_file, index=False)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "15dbdafc", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "b8f68b30", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:37.868843Z", "iopub.status.busy": "2025-03-25T06:00:37.868736Z", "iopub.status.idle": "2025-03-25T06:00:38.170896Z", "shell.execute_reply": "2025-03-25T06:00:38.170505Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix file found: ../../input/GEO/Osteoarthritis/GSE93720/GSE93720_series_matrix.txt.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape: (54675, 48)\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": "25ee7300", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "9a24f944", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:38.172308Z", "iopub.status.busy": "2025-03-25T06:00:38.172196Z", "iopub.status.idle": "2025-03-25T06:00:38.174129Z", "shell.execute_reply": "2025-03-25T06:00:38.173814Z" } }, "outputs": [], "source": [ "# This step doesn't require code implementation, just review and determination\n", "# These are Affymetrix probe IDs (e.g., \"1007_s_at\"), not human gene symbols\n", "# They follow the standard Affymetrix format with \"_at\" suffix\n", "# These need to be mapped to human gene symbols for analysis\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "97237b3e", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "a189a633", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:38.175351Z", "iopub.status.busy": "2025-03-25T06:00:38.175245Z", "iopub.status.idle": "2025-03-25T06:00:42.802188Z", "shell.execute_reply": "2025-03-25T06:00:42.801795Z" } }, "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", "[]\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": "52ac2834", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "e6f88d9e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:42.803540Z", "iopub.status.busy": "2025-03-25T06:00:42.803415Z", "iopub.status.idle": "2025-03-25T06:00:43.781370Z", "shell.execute_reply": "2025-03-25T06:00:43.781017Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene mapping dataframe shape: (45782, 2)\n", "Gene mapping preview:\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": [ "Gene expression data after mapping, shape: (21278, 48)\n", "First few gene symbols:\n", "['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data after normalization, shape: (19845, 48)\n", "First few normalized gene symbols:\n", "['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Mapped gene expression data saved to ../../output/preprocess/Osteoarthritis/gene_data/GSE93720.csv\n" ] } ], "source": [ "# 1. Determine which columns to use for gene mapping\n", "# The 'ID' column in gene_annotation corresponds to the probe identifiers in gene_data\n", "# The 'Gene Symbol' column contains the human gene symbols we want to map to\n", "prob_col = 'ID' # This is the probe identifier column\n", "gene_col = 'Gene Symbol' # This is the gene symbol column\n", "\n", "# 2. Get gene mapping dataframe using helper function \n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n", "print(\"Gene mapping preview:\")\n", "print(preview_df(gene_mapping, n=5))\n", "\n", "# 3. Apply gene mapping to convert probe-level data to gene-level data\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "print(f\"Gene expression data after mapping, shape: {gene_data.shape}\")\n", "print(\"First few gene symbols:\")\n", "print(list(gene_data.index[:10]))\n", "\n", "# Normalize gene symbols to handle synonyms and duplicates\n", "gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene expression data after normalization, shape: {gene_data.shape}\")\n", "print(\"First few normalized gene symbols:\")\n", "print(list(gene_data.index[:10]))\n", "\n", "# Save the gene data to a CSV file\n", "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n", "gene_data.to_csv(out_gene_data_file)\n", "print(f\"Mapped gene expression data saved to {out_gene_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "3e163e86", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "699418db", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:00:43.782806Z", "iopub.status.busy": "2025-03-25T06:00:43.782680Z", "iopub.status.idle": "2025-03-25T06:00:43.840735Z", "shell.execute_reply": "2025-03-25T06:00:43.840378Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical data columns: ['GSM3713911', 'GSM3713912', 'GSM3713913', 'GSM3713914', 'GSM3713915', 'GSM3713916', 'GSM3713917', 'GSM3713918', 'GSM3713919', 'GSM3713920', 'GSM3713921', 'GSM3713922', 'GSM3713923', 'GSM3713924', 'GSM3713925', 'GSM3713926', 'GSM3713927', 'GSM3713928', 'GSM3713929', 'GSM3713930', 'GSM3713931', 'GSM3713932', 'GSM3713933', 'GSM3713934', 'GSM3713935', 'GSM3713936', 'GSM3713937', 'GSM3713938', 'GSM3713939', 'GSM3713940', 'GSM3713941', 'GSM3713942', 'GSM3713943', 'GSM3713944', 'GSM3713945', 'GSM3713946', 'GSM3713947', 'GSM3713948', 'GSM3713949', 'GSM3713950', 'GSM3713951', 'GSM3713952', 'GSM3713953', 'GSM3713954', 'GSM3713955', 'GSM3713956', 'GSM3713957', 'GSM3713958']\n", "Clinical data shape: (47, 1)\n", "Clinical data preview: {1.0: [1.0, 1.0, 1.0, 1.0, 1.0]}\n", "Linked data shape before handling missing values: (49, 19892)\n", "Linked data shape after handling missing values: (1, 47)\n", "Quartiles for 'Osteoarthritis':\n", " 25%: 1.0\n", " 50% (Median): 1.0\n", " 75%: 1.0\n", "Min: 1.0\n", "Max: 1.0\n", "The distribution of the feature 'Osteoarthritis' in this dataset is severely biased.\n", "\n", "Dataset deemed not usable due to quality issues - linked data not saved\n" ] } ], "source": [ "# 1. Gene data was already normalized and saved in step 6\n", "\n", "# 2. Link the clinical and genetic data\n", "# First, load the clinical data saved in step 2\n", "clinical_df = pd.read_csv(out_clinical_data_file)\n", "\n", "# Print column names to inspect\n", "print(\"Clinical data columns:\", clinical_df.columns.tolist())\n", "\n", "# Set up clinical features dataframe in the format expected by geo_link_clinical_genetic_data\n", "# The first column is the sample ID column, and any remaining column contains the trait data\n", "clinical_features_df = pd.DataFrame(clinical_df)\n", "data_columns = [col for col in clinical_df.columns if col != clinical_df.columns[0]]\n", "if len(data_columns) > 0:\n", " # Rename the data column to the trait name\n", " clinical_features_df = clinical_features_df.rename(columns={data_columns[0]: trait})\n", "else:\n", " # If there's no data column, something is wrong with the clinical data\n", " raise ValueError(\"Clinical data does not contain trait information\")\n", "\n", "# Set the index to the first column (sample IDs) and transpose\n", "clinical_features_df = clinical_features_df.set_index(clinical_features_df.columns[0])\n", "clinical_features_df = clinical_features_df.T\n", "\n", "print(\"Clinical data shape:\", clinical_features_df.shape)\n", "print(\"Clinical data preview:\", preview_df(clinical_features_df))\n", "\n", "# Link clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(clinical_features_df, gene_data)\n", "print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\n", "\n", "# 3. 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", "# 4. Evaluate bias in trait and demographic features\n", "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "\n", "# 5. Conduct final quality validation\n", "note = \"This dataset contains human synovial fibroblasts derived from individuals with rheumatoid arthritis and osteoarthritis, stimulated with different conditions.\"\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,\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=note\n", ")\n", "\n", "# 6. Save linked data if 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(\"Dataset deemed not usable due to quality issues - linked data not saved\")" ] } ], "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 }