{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "02ad3739", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:58:29.111788Z", "iopub.status.busy": "2025-03-25T03:58:29.111391Z", "iopub.status.idle": "2025-03-25T03:58:29.276247Z", "shell.execute_reply": "2025-03-25T03:58:29.275898Z" } }, "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 = \"Sjögrens_Syndrome\"\n", "cohort = \"GSE40611\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Sjögrens_Syndrome\"\n", "in_cohort_dir = \"../../input/GEO/Sjögrens_Syndrome/GSE40611\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Sjögrens_Syndrome/GSE40611.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Sjögrens_Syndrome/gene_data/GSE40611.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Sjögrens_Syndrome/clinical_data/GSE40611.csv\"\n", "json_path = \"../../output/preprocess/Sjögrens_Syndrome/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "f404c4e2", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "cf7f8ab0", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:58:29.277677Z", "iopub.status.busy": "2025-03-25T03:58:29.277539Z", "iopub.status.idle": "2025-03-25T03:58:29.440562Z", "shell.execute_reply": "2025-03-25T03:58:29.440196Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Gene expression data of parotid tissue from Primary Sjogren’s Syndrome and controls\"\n", "!Series_summary\t\"Primary Sjögren’s syndrome (pSS) is a chronic autoimmune disease with complex etiopathogenesis. Here we use Affymetrix U133 plus 2.0 microarray gene expression data from human parotid tissue. Parotid gland tissues were harvested from 17 pSS and 14 14 non-pSS sicca patients and 18 controls. The data were used in the following article: Nazmul-Hossain ANM, Pollard RPE, Kroese FGM, Vissink A, Kallenberg CGM, Spijkervet FKL, Bootsma H, Michie SA, Gorr SU, Peck AB, Cai C, Zhou H, Horvath S, Wong DTW (2012) Systems Analysis of Primary Sjögren’s Syndrome Pathogenesis in Salivary Glands: Comparative Pathways and Molecular Events in Humans and a Mouse Model.\"\n", "!Series_overall_design\t\"Parotid gland tissues were harvested from 17 pSS and 14 non-pSS sicca patients and 18 controls.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['disease status: Control', 'disease status: pSS', 'disease status: Sicca'], 1: ['batch: 1', 'batch: 2', 'batch: 3']}\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": "bc7c66ed", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "ad7fa9ee", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:58:29.441910Z", "iopub.status.busy": "2025-03-25T03:58:29.441799Z", "iopub.status.idle": "2025-03-25T03:58:29.449965Z", "shell.execute_reply": "2025-03-25T03:58:29.449672Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clinical Features Preview:\n", "{'GSM997850': [0.0], 'GSM997851': [0.0], 'GSM997852': [0.0], 'GSM997853': [0.0], 'GSM997854': [0.0], 'GSM997855': [0.0], 'GSM997856': [0.0], 'GSM997857': [0.0], 'GSM997858': [0.0], 'GSM997859': [0.0], 'GSM997860': [0.0], 'GSM997861': [0.0], 'GSM997862': [0.0], 'GSM997863': [0.0], 'GSM997864': [0.0], 'GSM997865': [0.0], 'GSM997866': [0.0], 'GSM997867': [0.0], 'GSM997868': [1.0], 'GSM997869': [1.0], 'GSM997870': [1.0], 'GSM997871': [1.0], 'GSM997872': [1.0], 'GSM997873': [1.0], 'GSM997874': [1.0], 'GSM997875': [1.0], 'GSM997876': [1.0], 'GSM997877': [1.0], 'GSM997878': [0.0], 'GSM997879': [0.0], 'GSM997880': [0.0], 'GSM997881': [0.0], 'GSM997882': [0.0], 'GSM997883': [0.0], 'GSM997884': [0.0], 'GSM997885': [0.0], 'GSM997886': [0.0], 'GSM997887': [0.0], 'GSM997888': [0.0], 'GSM997889': [0.0], 'GSM997890': [0.0], 'GSM997891': [0.0], 'GSM997892': [1.0], 'GSM997893': [1.0], 'GSM997894': [1.0], 'GSM997895': [1.0], 'GSM997896': [1.0], 'GSM997897': [1.0], 'GSM997898': [1.0]}\n", "Clinical data saved to ../../output/preprocess/Sjögrens_Syndrome/clinical_data/GSE40611.csv\n" ] } ], "source": [ "import os\n", "import pandas as pd\n", "import json\n", "from typing import Optional, Callable, Dict, Any\n", "\n", "# 1. Gene Expression Data Availability\n", "# This dataset appears to be an Affymetrix gene expression microarray dataset (U133 plus 2.0)\n", "# which contains 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 dictionary, we can see:\n", "# - trait_row: 0 (disease status: Control, pSS, Sicca)\n", "# - age_row: None (not available in the dictionary)\n", "# - gender_row: None (not available in the dictionary)\n", "trait_row = 0\n", "age_row = None\n", "gender_row = None\n", "\n", "# 2.2 Data Type Conversion Functions\n", "def convert_trait(value):\n", " \"\"\"Convert trait values to binary (0: Control/Sicca, 1: pSS)\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract the value after the colon if it exists\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Map values to binary\n", " if value.lower() == 'pss':\n", " return 1 # Primary Sjögren's syndrome\n", " elif value.lower() in ['control', 'sicca']:\n", " return 0 # Control or non-pSS sicca patients\n", " else:\n", " return None\n", "\n", "# Since age and gender data are not available, we'll define placeholder functions\n", "def convert_age(value):\n", " return None\n", "\n", "def convert_gender(value):\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Validate 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", "# We only proceed if trait data is available\n", "if trait_row is not None:\n", " # Check if clinical_data exists in the global scope\n", " # This assumes clinical_data was loaded in a previous step\n", " if 'clinical_data' in globals():\n", " # Extract clinical features\n", " clinical_features = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row,\n", " convert_trait=convert_trait,\n", " age_row=age_row,\n", " convert_age=convert_age,\n", " gender_row=gender_row,\n", " convert_gender=convert_gender\n", " )\n", " \n", " # Preview the data\n", " preview = preview_df(clinical_features)\n", " print(\"Clinical Features Preview:\")\n", " print(preview)\n", " \n", " # Create directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " \n", " # Save to CSV\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " else:\n", " print(\"Error: clinical_data not found. Make sure it was loaded in a previous step.\")\n", "else:\n", " print(\"Skipping clinical feature extraction as trait data is not available.\")\n" ] }, { "cell_type": "markdown", "id": "bb75b562", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "232d3cef", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:58:29.451033Z", "iopub.status.busy": "2025-03-25T03:58:29.450923Z", "iopub.status.idle": "2025-03-25T03:58:29.698954Z", "shell.execute_reply": "2025-03-25T03:58:29.698578Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "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. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n", "gene_data = get_genetic_data(matrix_file)\n", "\n", "# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n", "print(gene_data.index[:20])\n" ] }, { "cell_type": "markdown", "id": "fdb7de71", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "7d78a7b6", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:58:29.700219Z", "iopub.status.busy": "2025-03-25T03:58:29.700111Z", "iopub.status.idle": "2025-03-25T03:58:29.701940Z", "shell.execute_reply": "2025-03-25T03:58:29.701674Z" } }, "outputs": [], "source": [ "# Reviewing the gene identifiers shown from the previous step\n", "# These identifiers follow Affymetrix probe ID format (e.g., '1007_s_at', '1053_at')\n", "# They are not standard human gene symbols (like BRCA1, TP53, etc.)\n", "# Affymetrix probe IDs need to be mapped to gene symbols for biological interpretation\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "77b0a477", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "9c2ac344", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:58:29.702992Z", "iopub.status.busy": "2025-03-25T03:58:29.702890Z", "iopub.status.idle": "2025-03-25T03:58:34.174433Z", "shell.execute_reply": "2025-03-25T03:58:34.173785Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "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", "gene_annotation = get_gene_annotation(soft_file)\n", "\n", "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n", "print(\"Gene annotation preview:\")\n", "print(preview_df(gene_annotation))\n" ] }, { "cell_type": "markdown", "id": "df5b979f", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "d7104a9d", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:58:34.176236Z", "iopub.status.busy": "2025-03-25T03:58:34.176103Z", "iopub.status.idle": "2025-03-25T03:58:34.440640Z", "shell.execute_reply": "2025-03-25T03:58:34.440091Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of genes after mapping: 21278\n", "Gene expression data preview (first 5 genes):\n", " GSM997850 GSM997851 GSM997852 GSM997853 GSM997854 \\\n", "Gene \n", "A1BG 200.7886 264.12790 222.02390 1130.58000 115.61810 \n", "A1BG-AS1 348.5785 75.04438 17.70408 348.76770 53.74231 \n", "A1CF 376.0515 1038.22820 218.99734 1637.57586 509.34959 \n", "A2M 999.7460 3896.31248 5243.59760 1262.17840 3060.09980 \n", "A2M-AS1 136.3005 438.83670 282.71130 522.03900 876.44090 \n", "\n", " GSM997855 GSM997856 GSM997857 GSM997858 GSM997859 ... \\\n", "Gene ... \n", "A1BG 40.41677 1250.6850 243.05170 315.2728 13.07202 ... \n", "A1BG-AS1 27.96156 422.7090 69.14751 283.7576 104.83430 ... \n", "A1CF 746.00250 1613.7927 537.44098 1199.0763 168.77953 ... \n", "A2M 6499.39390 837.3244 4695.02140 4872.3466 7792.30425 ... \n", "A2M-AS1 306.74150 654.1342 560.16720 527.2808 107.95910 ... \n", "\n", " GSM997889 GSM997890 GSM997891 GSM997892 GSM997893 \\\n", "Gene \n", "A1BG 21.16710 40.493980 63.087580 62.21655 66.88108 \n", "A1BG-AS1 55.24595 47.972190 46.389860 88.12190 47.55962 \n", "A1CF 409.21292 174.991255 375.394085 74.42745 71.48017 \n", "A2M 8055.29020 5814.708800 8363.580700 7908.74015 7383.40130 \n", "A2M-AS1 199.08120 193.929700 211.744600 578.11280 390.72920 \n", "\n", " GSM997894 GSM997895 GSM997896 GSM997897 GSM997898 \n", "Gene \n", "A1BG 127.36900 75.78689 37.322990 47.936120 10.321330 \n", "A1BG-AS1 28.25539 126.93140 95.101430 77.801830 15.465710 \n", "A1CF 155.54525 216.46475 86.442453 98.232095 228.898167 \n", "A2M 8988.30200 11637.30739 6283.175000 6406.162380 6950.870600 \n", "A2M-AS1 472.60290 356.39780 241.618100 307.129200 283.219300 \n", "\n", "[5 rows x 49 columns]\n" ] } ], "source": [ "# 1. Observe gene identifiers in expression data and annotation data\n", "# From the previous outputs we can see:\n", "# - In gene_data, the index is ID (e.g., '1007_s_at')\n", "# - In gene_annotation, there are columns 'ID' and 'Gene Symbol'\n", "\n", "# 2. Extract gene mapping dataframe with probe IDs and gene symbols\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n", "\n", "# 3. Apply gene mapping to convert from probe-level to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# Print the number of mapped genes to check\n", "print(f\"Number of genes after mapping: {len(gene_data)}\")\n", "\n", "# Preview the first few rows of the gene expression data\n", "print(\"Gene expression data preview (first 5 genes):\")\n", "print(gene_data.head())\n" ] }, { "cell_type": "markdown", "id": "ba5dc3d7", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "6e4c10b6", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T03:58:34.442478Z", "iopub.status.busy": "2025-03-25T03:58:34.442350Z", "iopub.status.idle": "2025-03-25T03:58:44.913739Z", "shell.execute_reply": "2025-03-25T03:58:44.913150Z" } }, "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/Sjögrens_Syndrome/gene_data/GSE40611.csv\n", "Loaded clinical data shape: (1, 49)\n", " GSM997850 GSM997851 GSM997852 GSM997853 GSM997854 \\\n", "Sjögrens_Syndrome 0.0 0.0 0.0 0.0 0.0 \n", "\n", " GSM997855 GSM997856 GSM997857 GSM997858 GSM997859 ... \\\n", "Sjögrens_Syndrome 0.0 0.0 0.0 0.0 0.0 ... \n", "\n", " GSM997889 GSM997890 GSM997891 GSM997892 GSM997893 \\\n", "Sjögrens_Syndrome 0.0 0.0 0.0 1.0 1.0 \n", "\n", " GSM997894 GSM997895 GSM997896 GSM997897 GSM997898 \n", "Sjögrens_Syndrome 1.0 1.0 1.0 1.0 1.0 \n", "\n", "[1 rows x 49 columns]\n", "Linked data shape: (49, 19846)\n", " Sjögrens_Syndrome A1BG A1BG-AS1 A1CF A2M \\\n", "GSM997850 0.0 200.7886 348.57850 376.05150 999.74600 \n", "GSM997851 0.0 264.1279 75.04438 1038.22820 3896.31248 \n", "GSM997852 0.0 222.0239 17.70408 218.99734 5243.59760 \n", "GSM997853 0.0 1130.5800 348.76770 1637.57586 1262.17840 \n", "GSM997854 0.0 115.6181 53.74231 509.34959 3060.09980 \n", "\n", " A2M-AS1 A2ML1 A2MP1 A4GALT A4GNT ... \\\n", "GSM997850 136.3005 956.50560 85.21337 657.39690 984.12040 ... \n", "GSM997851 438.8367 260.65042 166.90610 69.42258 442.22240 ... \n", "GSM997852 282.7113 121.81847 47.99925 268.85530 90.24828 ... \n", "GSM997853 522.0390 774.54284 496.19370 559.59220 351.28460 ... \n", "GSM997854 876.4409 963.63660 92.41728 183.86210 844.53590 ... \n", "\n", " ZWILCH ZWINT ZXDA ZXDB ZXDC \\\n", "GSM997850 1781.78150 263.3597 1967.076450 2232.361650 1160.78300 \n", "GSM997851 954.92410 133.9090 1216.957850 1127.341940 2962.44986 \n", "GSM997852 1154.06855 587.2657 997.776300 1572.311900 1678.13429 \n", "GSM997853 758.57850 606.9058 2261.951820 1333.825220 2821.83765 \n", "GSM997854 1183.83853 819.0315 1177.169955 1065.579555 2534.41745 \n", "\n", " ZYG11A ZYG11B ZYX ZZEF1 ZZZ3 \n", "GSM997850 195.69460 2460.49090 654.16730 333.09537 335.3813 \n", "GSM997851 51.14468 2931.08880 172.32059 1599.89660 1775.3181 \n", "GSM997852 62.35224 2963.74360 759.35265 923.29605 2687.2880 \n", "GSM997853 1329.96200 2540.91277 342.01896 3366.62490 2176.5714 \n", "GSM997854 43.48121 1946.57980 118.29463 1996.80697 573.1395 \n", "\n", "[5 rows x 19846 columns]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Shape after handling missing values: (49, 19846)\n", "For the feature 'Sjögrens_Syndrome', the least common label is '1.0' with 17 occurrences. This represents 34.69% of the dataset.\n", "The distribution of the feature 'Sjögrens_Syndrome' in this dataset is fine.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Sjögrens_Syndrome/GSE40611.csv\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", "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. Load the previously saved clinical data\n", "clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)\n", "print(f\"Loaded clinical data shape: {clinical_df.shape}\")\n", "print(clinical_df.head())\n", "\n", "# 3. Link the clinical and genetic data\n", "linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "print(linked_data.head())\n", "\n", "# 4. Handle missing values in the linked data\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(f\"Shape after handling missing values: {linked_data.shape}\")\n", "\n", "# 5. Determine whether the trait and demographic features are severely biased\n", "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "\n", "# 6. 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,\n", " is_biased=is_trait_biased, \n", " df=unbiased_linked_data,\n", " note=f\"Dataset contains gene expression data from CD4 T-cells of pSS patients and healthy controls.\"\n", ")\n", "\n", "# 7. Save the data if it's usable\n", "if is_usable:\n", " # Create directory if it doesn't exist\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " # Save the data\n", " unbiased_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 is not suitable for association studies.\")" ] } ], "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 }