{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "24991aa1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:53:46.401981Z", "iopub.status.busy": "2025-03-25T05:53:46.401874Z", "iopub.status.idle": "2025-03-25T05:53:46.567076Z", "shell.execute_reply": "2025-03-25T05:53:46.566620Z" } }, "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 = \"Multiple_sclerosis\"\n", "cohort = \"GSE146383\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Multiple_sclerosis\"\n", "in_cohort_dir = \"../../input/GEO/Multiple_sclerosis/GSE146383\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Multiple_sclerosis/GSE146383.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Multiple_sclerosis/gene_data/GSE146383.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Multiple_sclerosis/clinical_data/GSE146383.csv\"\n", "json_path = \"../../output/preprocess/Multiple_sclerosis/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "ce8aec2a", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "c7136f20", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:53:46.568571Z", "iopub.status.busy": "2025-03-25T05:53:46.568419Z", "iopub.status.idle": "2025-03-25T05:53:46.680260Z", "shell.execute_reply": "2025-03-25T05:53:46.679802Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Effect of Age on Severity and Recovery of Acute Multiple Sclerosis Attack\"\n", "!Series_summary\t\"Pediatric MS patients suffer from severe first and second relapse but better recovery explained by difference in age-restricted transcriptional profiles associated with antigen-presentation and B-cell activation\"\n", "!Series_summary\t\"Herein, we compared the blood mononuclear cell transcriptome of pediatric and adult MS patients with recovery (PDMS-rec, ADMS-rec) and without recovery (PDMS-norec, ADMS-norec) 6 months after relapse. Healthy pediatric and adult subjects (PDC, ADC) were used as controls.\"\n", "!Series_overall_design\t\"A total of 30 MS patients (14 PDMS-rec and ADMS-rec, 16 PDMS-norec and ADMS-norec , 55 PDC andADC) that met the inclusion criteria were analyzed.\"\n", "Sample Characteristics Dictionary:\n", "{0: ['gender: Female', 'gender: Male'], 1: ['age (years): 16', 'age (years): 38', 'age (years): 17', 'age (years): 37', 'age (years): 14', 'age (years): 15', 'age (years): 25', 'age (years): 24', 'age (years): 22', 'age (years): 34', 'age (years): 12', 'age (years): 33', 'age (years): 29', 'age (years): 13', 'age (years): 23', 'age (years): 9.94', 'age (years): 10.7', 'age (years): 12.4', 'age (years): 13.2', 'age (years): 13.7', 'age (years): 13.9', 'age (years): 22.7', 'age (years): 24.9', 'age (years): 26.7', 'age (years): 27', 'age (years): 27.4', 'age (years): 27.7', 'age (years): 27.9', 'age (years): 29.1', 'age (years): 29.7']}\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": "ce50114a", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "90c84704", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:53:46.681785Z", "iopub.status.busy": "2025-03-25T05:53:46.681666Z", "iopub.status.idle": "2025-03-25T05:53:46.687758Z", "shell.execute_reply": "2025-03-25T05:53:46.687374Z" } }, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "from typing import Optional, Callable, Dict, Any\n", "\n", "# 1. Gene Expression Data Availability\n", "# Based on the title and summary, this dataset involves transcriptome data, which implies 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", "# The trait information isn't directly available in the sample characteristics\n", "trait_row = None # Not directly available in the sample characteristics\n", "age_row = 1 # Age information is available at index 1\n", "gender_row = 0 # Gender information is available at index 0\n", "\n", "# 2.2 Data Type Conversion\n", "# For trait: We define this function even though we don't have trait data\n", "def convert_trait(value: str) -> Optional[int]:\n", " \"\"\"\n", " Convert trait values to binary format.\n", " Based on the summary, we have MS patients and healthy controls.\n", " \"\"\"\n", " if not value or ':' not in value:\n", " return None\n", " value = value.lower().split(':', 1)[1].strip()\n", " \n", " # Based on the background info, there are MS patients and healthy controls\n", " if 'ms' in value or 'multiple sclerosis' in value:\n", " return 1 # MS patient\n", " elif 'control' in value or 'healthy' in value:\n", " return 0 # Healthy control\n", " else:\n", " return None # Unknown\n", "\n", "# For age: Convert to continuous values\n", "def convert_age(value: str) -> Optional[float]:\n", " \"\"\"Convert age values to continuous format.\"\"\"\n", " if not value or ':' not in value:\n", " return None\n", " try:\n", " # Extract the value after the colon and convert to float\n", " age = float(value.split(':', 1)[1].strip())\n", " return age\n", " except (ValueError, TypeError):\n", " return None\n", "\n", "# For gender: Convert to binary (0 for female, 1 for male)\n", "def convert_gender(value: str) -> Optional[int]:\n", " \"\"\"Convert gender values to binary format (0 for female, 1 for male).\"\"\"\n", " if not value or ':' not in value:\n", " return None\n", " value = value.lower().split(':', 1)[1].strip()\n", " \n", " if 'female' in value:\n", " return 0\n", " elif 'male' in value:\n", " return 1\n", " else:\n", " return None\n", "\n", "# 3. Save Metadata\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "# Conduct initial filtering on usability\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "# Since trait_row is None, we still extract age and gender information if available\n", "if clinical_file := os.path.join(in_cohort_dir, \"clinical_data.csv\"):\n", " if os.path.exists(clinical_file):\n", " clinical_data = pd.read_csv(clinical_file)\n", " \n", " # Extract available clinical features (age and gender)\n", " selected_features = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=trait_row, # This is None\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 extracted features\n", " preview = preview_df(selected_features)\n", " print(\"Preview of extracted clinical features:\", preview)\n", " \n", " # Save the clinical data\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " selected_features.to_csv(out_clinical_data_file, index=False)\n" ] }, { "cell_type": "markdown", "id": "329c62cc", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "30367084", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:53:46.689109Z", "iopub.status.busy": "2025-03-25T05:53:46.688982Z", "iopub.status.idle": "2025-03-25T05:53:46.886823Z", "shell.execute_reply": "2025-03-25T05:53:46.886291Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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', '1598_g_at', '160020_at', '1729_at', '1773_at', '177_at',\n", " '179_at', '1861_at'],\n", " dtype='object', name='ID')\n", "\n", "Gene data dimensions: 22215 genes × 85 samples\n" ] } ], "source": [ "# 1. Extract the gene expression data from the matrix file\n", "gene_data = get_genetic_data(matrix_file)\n", "\n", "# 2. Print the first 20 row IDs (gene or probe identifiers)\n", "print(\"\\nFirst 20 gene/probe identifiers:\")\n", "print(gene_data.index[:20])\n", "\n", "# 3. Print the dimensions of the gene expression data\n", "print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n", "\n", "# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n", "is_gene_available = True\n" ] }, { "cell_type": "markdown", "id": "c98d62bd", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "27128634", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:53:46.888361Z", "iopub.status.busy": "2025-03-25T05:53:46.888228Z", "iopub.status.idle": "2025-03-25T05:53:46.890602Z", "shell.execute_reply": "2025-03-25T05:53:46.890192Z" } }, "outputs": [], "source": [ "# Reviewing the gene identifiers shown in the output\n", "# These appear to be Affymetrix probe IDs (like '1007_s_at', '1053_at', etc.)\n", "# which are not standard human gene symbols and will need to be mapped\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "b6fbc961", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "fc5b1091", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:53:46.892296Z", "iopub.status.busy": "2025-03-25T05:53:46.892184Z", "iopub.status.idle": "2025-03-25T05:53:49.870932Z", "shell.execute_reply": "2025-03-25T05:53:49.870337Z" } }, "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. First get the file paths using geo_get_relevant_filepaths function\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. 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", "# 3. 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": "53ac01be", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "fa17654f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:53:49.872833Z", "iopub.status.busy": "2025-03-25T05:53:49.872713Z", "iopub.status.idle": "2025-03-25T05:53:50.044177Z", "shell.execute_reply": "2025-03-25T05:53:50.043538Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Sample of gene mapping (ID to Gene Symbol):\n", " ID Gene\n", "0 1007_s_at DDR1 /// MIR4640\n", "1 1053_at RFC2\n", "2 117_at HSPA6\n", "3 121_at PAX8\n", "4 1255_g_at GUCA1A\n", "Mapping shape: (21225, 2)\n", "\n", "After mapping to gene symbols:\n", "Gene data dimensions: 13830 genes × 85 samples\n", "First 10 gene symbols:\n", "Index(['A1CF', 'A2M', 'A4GALT', 'A4GNT', 'AAAS', 'AACS', 'AADAC', 'AAGAB',\n", " 'AAK1', 'AAMDC'],\n", " dtype='object', name='Gene')\n" ] } ], "source": [ "# 1. Observe gene identifiers in both gene expression and annotation data\n", "# From previous outputs we can see:\n", "# - Gene expression data row indices are identifiers like '1007_s_at', '1053_at'\n", "# - Gene annotation has 'ID' column with same format, and 'Gene Symbol' column with gene symbols\n", "\n", "# 2. Extract the gene identifier and gene symbol columns\n", "# We need to map from 'ID' column to 'Gene Symbol' column\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n", "\n", "# Print a sample of the mapping to verify\n", "print(\"\\nSample of gene mapping (ID to Gene Symbol):\")\n", "print(gene_mapping.head())\n", "print(f\"Mapping shape: {gene_mapping.shape}\")\n", "\n", "# 3. Apply the gene mapping to convert probe-level data to gene expression data\n", "gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)\n", "\n", "# Print info about the resulting gene data\n", "print(\"\\nAfter mapping to gene symbols:\")\n", "print(f\"Gene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n", "print(\"First 10 gene symbols:\")\n", "print(gene_data.index[:10])\n" ] }, { "cell_type": "markdown", "id": "17fcaeea", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "2f87c2c4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T05:53:50.046233Z", "iopub.status.busy": "2025-03-25T05:53:50.046074Z", "iopub.status.idle": "2025-03-25T05:53:50.802252Z", "shell.execute_reply": "2025-03-25T05:53:50.801609Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene data shape after normalization: (13542, 85)\n", "First 5 gene symbols after normalization: Index(['A1CF', 'A2M', 'A4GALT', 'A4GNT', 'AAAS'], dtype='object', name='Gene')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Multiple_sclerosis/gene_data/GSE146383.csv\n", "Clinical features shape: (85, 2)\n", "Clinical features preview:\n", "{'Age': [16.0, 38.0, 17.0, 37.0, 14.0], 'Gender': [0.0, 0.0, 1.0, 0.0, 0.0]}\n", "Clinical data saved to ../../output/preprocess/Multiple_sclerosis/clinical_data/GSE146383.csv\n", "Gene data samples: 85\n", "Clinical data samples: 85\n", "Common samples: 85\n", "Linked data shape: (85, 13544)\n", "Dataset marked as not usable due to missing trait information.\n" ] } ], "source": [ "# 1. Normalize gene symbols in the index of gene expression data\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n", "print(f\"First 5 gene symbols after normalization: {normalized_gene_data.index[:5]}\")\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. Since trait_row was None in Step 2, we need to handle missing trait data\n", "# Create a clinical dataframe with age and gender information from the original clinical_data\n", "# obtained in step 1, rather than trying to load from a file that doesn't exist\n", "clinical_features = pd.DataFrame()\n", "\n", "# Extract age data if available\n", "if age_row is not None:\n", " age_data = get_feature_data(clinical_data, age_row, 'Age', convert_age)\n", " clinical_features = pd.concat([clinical_features, age_data])\n", "\n", "# Extract gender data if available\n", "if gender_row is not None:\n", " gender_data = get_feature_data(clinical_data, gender_row, 'Gender', convert_gender)\n", " clinical_features = pd.concat([clinical_features, gender_data])\n", "\n", "# Transpose to have samples as rows and features as columns\n", "clinical_features = clinical_features.T\n", "\n", "print(f\"Clinical features shape: {clinical_features.shape}\")\n", "print(\"Clinical features preview:\")\n", "print(preview_df(clinical_features))\n", "\n", "# Save the clinical data for reference\n", "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", "clinical_features.to_csv(out_clinical_data_file)\n", "print(f\"Clinical data saved to {out_clinical_data_file}\")\n", "\n", "# 3. Link clinical and genetic data\n", "# Check if the sample IDs need alignment\n", "gene_samples = set(normalized_gene_data.columns)\n", "clinical_samples = set(clinical_features.index)\n", "common_samples = gene_samples.intersection(clinical_samples)\n", "\n", "print(f\"Gene data samples: {len(gene_samples)}\")\n", "print(f\"Clinical data samples: {len(clinical_samples)}\")\n", "print(f\"Common samples: {len(common_samples)}\")\n", "\n", "# If no common samples, we need to align the sample IDs\n", "if len(common_samples) == 0:\n", " print(\"No common sample IDs found, attempting to standardize IDs...\")\n", " \n", " # Create a mapping from gene_data column names to clinical_features index\n", " # This is specific to GEO datasets where sample IDs follow a pattern\n", " # First, convert both to strings to ensure consistent comparison\n", " gene_samples_str = [str(s) for s in normalized_gene_data.columns]\n", " clinical_samples_str = [str(s) for s in clinical_features.index]\n", " \n", " # For GEO data, both should contain GSM identifiers\n", " # Try to match based on partial string matching\n", " id_map = {}\n", " for g_id in gene_samples_str:\n", " for c_id in clinical_samples_str:\n", " if g_id in c_id or c_id in g_id:\n", " id_map[g_id] = c_id\n", " break\n", " \n", " if id_map:\n", " print(f\"Found {len(id_map)} sample ID mappings\")\n", " # Rename columns in gene_data to match clinical_features\n", " gene_data_aligned = normalized_gene_data.copy()\n", " gene_data_aligned.columns = [id_map.get(str(col), col) for col in gene_data_aligned.columns]\n", " \n", " # Now link the data\n", " linked_data = pd.concat([clinical_features, gene_data_aligned.T], axis=1, join='inner')\n", " else:\n", " print(\"Could not align sample IDs, using original IDs\")\n", " # Since we can't align, just try direct concatenation\n", " linked_data = pd.concat([clinical_features, normalized_gene_data.T], axis=1)\n", "else:\n", " # If there are common samples, use only those\n", " linked_data = pd.concat([clinical_features.loc[list(common_samples)], \n", " normalized_gene_data.T.loc[list(common_samples)]], \n", " axis=1)\n", "\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# 4. Since no trait data is available, we call validate_and_save_cohort_info with is_final=False\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=True,\n", " is_trait_available=False # No trait data\n", ")\n", "\n", "print(f\"Dataset marked as not usable due to missing trait information.\")" ] } ], "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 }