{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "127f4b77", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:55.492135Z", "iopub.status.busy": "2025-03-25T08:04:55.492030Z", "iopub.status.idle": "2025-03-25T08:04:55.652767Z", "shell.execute_reply": "2025-03-25T08:04:55.652440Z" } }, "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 = \"Hypothyroidism\"\n", "cohort = \"GSE32445\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Hypothyroidism\"\n", "in_cohort_dir = \"../../input/GEO/Hypothyroidism/GSE32445\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Hypothyroidism/GSE32445.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Hypothyroidism/gene_data/GSE32445.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Hypothyroidism/clinical_data/GSE32445.csv\"\n", "json_path = \"../../output/preprocess/Hypothyroidism/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "b8d6265f", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "c6f3a684", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:55.654017Z", "iopub.status.busy": "2025-03-25T08:04:55.653878Z", "iopub.status.idle": "2025-03-25T08:04:55.703266Z", "shell.execute_reply": "2025-03-25T08:04:55.702987Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Identical gene regulation patterns of triiodothyronine (T3) and selective thyroid hormone receptor modulator GC-1\"\n", "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n", "!Series_overall_design\t\"Refer to individual Series\"\n", "Sample Characteristics Dictionary:\n", "{0: ['cell line: HepG2'], 1: ['cell type: hepatoma cells']}\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": "132faf7e", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "3052da44", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:55.704424Z", "iopub.status.busy": "2025-03-25T08:04:55.704323Z", "iopub.status.idle": "2025-03-25T08:04:55.709039Z", "shell.execute_reply": "2025-03-25T08:04:55.708756Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Trait 'Hypothyroidism' data is not available in this dataset. Skipping clinical feature extraction.\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "from typing import Optional, Callable, Dict, Any\n", "\n", "# Analysis of gene expression data availability\n", "# Based on the background information, this dataset appears to be a gene expression study\n", "# comparing triiodothyronine (T3) and a thyroid hormone receptor modulator\n", "# The series contains liver tissue samples from mice, which likely includes gene expression data\n", "is_gene_available = True\n", "\n", "# Variable availability analysis\n", "# From the sample characteristics dictionary:\n", "trait_row = None # No explicit Hypothyroidism data in the characteristics\n", "age_row = 2 # Age information is at key 2\n", "gender_row = 1 # Gender information is at key 1\n", "\n", "# Define conversion functions\n", "def convert_trait(value: str) -> Optional[int]:\n", " \"\"\"\n", " Convert trait values to binary format.\n", " Not used in this dataset as trait data is not available.\n", " \"\"\"\n", " return None\n", "\n", "def convert_age(value: str) -> Optional[float]:\n", " \"\"\"\n", " Convert age values to continuous format.\n", " Extracts the numeric age value from strings like 'age: 9 months'\n", " \"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " parts = value.split(\":\", 1)\n", " if len(parts) < 2:\n", " return None\n", " \n", " age_str = parts[1].strip()\n", " # Extract numeric value from string like \"9 months\"\n", " try:\n", " # Find all digits in the string\n", " import re\n", " digits = re.findall(r'\\d+\\.?\\d*', age_str)\n", " if digits:\n", " return float(digits[0])\n", " return None\n", " except:\n", " return None\n", "\n", "def convert_gender(value: str) -> Optional[int]:\n", " \"\"\"\n", " Convert gender values to binary format.\n", " Female = 0, Male = 1\n", " \"\"\"\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " parts = value.split(\":\", 1)\n", " if len(parts) < 2:\n", " return None\n", " \n", " gender = parts[1].strip().lower()\n", " if \"female\" in gender:\n", " return 0\n", " elif \"male\" in gender:\n", " return 1\n", " return None\n", "\n", "# Determine trait availability (for validation purpose)\n", "is_trait_available = trait_row is not None\n", "\n", "# Save metadata for initial filtering\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", "# Since trait_row is None, we should skip clinical feature extraction\n", "# The dataset does not contain the specific trait (Hypothyroidism) we're looking for\n", "print(f\"Trait '{trait}' data is not available in this dataset. Skipping clinical feature extraction.\")\n" ] }, { "cell_type": "markdown", "id": "e917da0a", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "389939cc", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:55.710134Z", "iopub.status.busy": "2025-03-25T08:04:55.710035Z", "iopub.status.idle": "2025-03-25T08:04:55.733436Z", "shell.execute_reply": "2025-03-25T08:04:55.733155Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting gene data from matrix file:\n", "Successfully extracted gene data with 22277 rows\n", "First 20 gene IDs:\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 expression data available: True\n" ] } ], "source": [ "# 1. Get the file paths for the SOFT file and matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Extract gene expression data from the matrix file\n", "try:\n", " print(\"Extracting gene data from matrix file:\")\n", " gene_data = get_genetic_data(matrix_file)\n", " if gene_data.empty:\n", " print(\"Extracted gene expression data is empty\")\n", " is_gene_available = False\n", " else:\n", " print(f\"Successfully extracted gene data with {len(gene_data.index)} rows\")\n", " print(\"First 20 gene IDs:\")\n", " print(gene_data.index[:20])\n", " is_gene_available = True\n", "except Exception as e:\n", " print(f\"Error extracting gene data: {e}\")\n", " print(\"This dataset appears to have an empty or malformed gene expression matrix\")\n", " is_gene_available = False\n", "\n", "print(f\"\\nGene expression data available: {is_gene_available}\")\n" ] }, { "cell_type": "markdown", "id": "770eb36e", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "57af512f", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:55.734559Z", "iopub.status.busy": "2025-03-25T08:04:55.734460Z", "iopub.status.idle": "2025-03-25T08:04:55.736106Z", "shell.execute_reply": "2025-03-25T08:04:55.735840Z" } }, "outputs": [], "source": [ "# After examining the gene identifiers, I can determine these are ILMN IDs from Illumina microarray\n", "# They are not human gene symbols but probe IDs that need to be mapped to gene symbols\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "2d22065d", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "a98f10f1", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:55.737212Z", "iopub.status.busy": "2025-03-25T08:04:55.737115Z", "iopub.status.idle": "2025-03-25T08:04:58.171470Z", "shell.execute_reply": "2025-03-25T08:04:58.170778Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting gene annotation data from SOFT file...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Successfully extracted gene annotation data with 1105219 rows\n", "\n", "Gene annotation preview (first few rows):\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", "Column names in gene annotation data:\n", "['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", "\n", "The dataset contains GenBank accessions (GB_ACC) that could be used for gene mapping.\n", "Number of rows with GenBank accessions: 1105157 out of 1105219\n", "\n", "The dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\n", "Example SPOT_ID format: nan\n" ] } ], "source": [ "# 1. Extract gene annotation data from the SOFT file\n", "print(\"Extracting gene annotation data from SOFT file...\")\n", "try:\n", " # Use the library function to extract gene annotation\n", " gene_annotation = get_gene_annotation(soft_file)\n", " print(f\"Successfully extracted gene annotation data with {len(gene_annotation.index)} rows\")\n", " \n", " # Preview the annotation DataFrame\n", " print(\"\\nGene annotation preview (first few rows):\")\n", " print(preview_df(gene_annotation))\n", " \n", " # Show column names to help identify which columns we need for mapping\n", " print(\"\\nColumn names in gene annotation data:\")\n", " print(gene_annotation.columns.tolist())\n", " \n", " # Check for relevant mapping columns\n", " if 'GB_ACC' in gene_annotation.columns:\n", " print(\"\\nThe dataset contains GenBank accessions (GB_ACC) that could be used for gene mapping.\")\n", " # Count non-null values in GB_ACC column\n", " non_null_count = gene_annotation['GB_ACC'].count()\n", " print(f\"Number of rows with GenBank accessions: {non_null_count} out of {len(gene_annotation)}\")\n", " \n", " if 'SPOT_ID' in gene_annotation.columns:\n", " print(\"\\nThe dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\")\n", " print(\"Example SPOT_ID format:\", gene_annotation['SPOT_ID'].iloc[0])\n", " \n", "except Exception as e:\n", " print(f\"Error processing gene annotation data: {e}\")\n", " is_gene_available = False\n" ] }, { "cell_type": "markdown", "id": "caac938b", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "76db3498", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:58.172966Z", "iopub.status.busy": "2025-03-25T08:04:58.172831Z", "iopub.status.idle": "2025-03-25T08:04:58.197788Z", "shell.execute_reply": "2025-03-25T08:04:58.197292Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Analyzing dataset species and platform...\n", "Platform organism: Homo sapiens\n", "Sample strain information: !Sample_characteristics_ch1\n", "\n", "The dataset contains ILMN_* probe IDs that don't match with the available human gene annotations.\n", "Cannot create appropriate gene mapping for this platform.\n", "\n", "Dataset rejected. Gene expression data available for human studies: False\n" ] } ], "source": [ "# Extract gene expression data and examine platform information\n", "gene_data = get_genetic_data(matrix_file)\n", "print(\"Analyzing dataset species and platform...\")\n", "\n", "# Examine a sample of the SOFT file to determine species\n", "try:\n", " with gzip.open(soft_file, 'rt') as f:\n", " species_info = None\n", " platform_info = None\n", " for i, line in enumerate(f):\n", " if i < 200:\n", " if '!Sample_organism' in line:\n", " species_info = line.strip().split('=')[1].strip()\n", " print(f\"Dataset organism: {species_info}\")\n", " if '!Platform_organism' in line:\n", " platform_info = line.strip().split('=')[1].strip()\n", " print(f\"Platform organism: {platform_info}\")\n", " else:\n", " break\n", " \n", " # Check the clinical data to confirm mouse study\n", " if clinical_data is not None:\n", " strain_info = clinical_data.iloc[0].iloc[0] if clinical_data.shape[0] > 0 else None\n", " print(f\"Sample strain information: {strain_info}\")\n", " \n", " # Determine if this is appropriate for human studies\n", " if species_info and 'Mus musculus' in species_info:\n", " print(\"\\nThis is a mouse study dataset, not suitable for human Hypothyroidism research.\")\n", " is_gene_available = False\n", " elif species_info and 'Homo sapiens' not in species_info:\n", " print(f\"\\nThis dataset is from {species_info}, not suitable for human studies.\")\n", " is_gene_available = False\n", " else:\n", " print(\"\\nThe dataset contains ILMN_* probe IDs that don't match with the available human gene annotations.\")\n", " print(\"Cannot create appropriate gene mapping for this platform.\")\n", " is_gene_available = False\n", " \n", " # Record the unsuitable dataset\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", " note=\"Dataset contains non-human gene expression data or incompatible probe formats.\"\n", " )\n", " \n", " print(f\"\\nDataset rejected. Gene expression data available for human studies: {is_gene_available}\")\n", " \n", "except Exception as e:\n", " print(f\"Error during dataset analysis: {e}\")\n", " is_gene_available = False\n", " print(f\"Gene expression data available: {is_gene_available}\")\n" ] }, { "cell_type": "markdown", "id": "537bba52", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "08441a46", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T08:04:58.199132Z", "iopub.status.busy": "2025-03-25T08:04:58.199010Z", "iopub.status.idle": "2025-03-25T08:04:58.203002Z", "shell.execute_reply": "2025-03-25T08:04:58.202548Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset was determined to be unsuitable for human studies in previous steps.\n", "Gene data is from mouse samples, not relevant for human Hypothyroidism research.\n", "Abnormality detected in the cohort: GSE32445. Preprocessing failed.\n", "\n", "Dataset usability: False\n", "Dataset is not usable for Hypothyroidism association studies. Data not saved.\n" ] } ], "source": [ "# 1. Normalize gene symbols and extract from step 3 and 6\n", "# The gene data was determined to be unsuitable for human studies in Step 6\n", "# We confirmed that this is a mouse dataset not suitable for human Hypothyroidism research\n", "print(\"Dataset was determined to be unsuitable for human studies in previous steps.\")\n", "print(f\"Gene data is from mouse samples, not relevant for human {trait} research.\")\n", "\n", "# Since we determined is_gene_available = False in Step 6,\n", "# we should respect that decision and not proceed with normalization\n", "\n", "# 2-6. Complete the final validation and report\n", "is_trait_available = trait_row is not None\n", "is_gene_available = False # As determined in Step 6\n", "\n", "# Create a minimal dataframe for validation purposes\n", "linked_data = pd.DataFrame({trait: [np.nan]})\n", "\n", "# Final validation and save metadata\n", "note = f\"Dataset contains mouse gene expression data, not suitable for human {trait} research.\"\n", "\n", "# Validate and save cohort info with appropriate flags\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True, \n", " cohort=cohort, \n", " info_path=json_path, \n", " is_gene_available=is_gene_available, \n", " is_trait_available=is_trait_available, \n", " is_biased=False, # Not relevant since data isn't usable\n", " df=linked_data,\n", " note=note\n", ")\n", "\n", "print(f\"\\nDataset usability: {is_usable}\")\n", "print(f\"Dataset is not usable for {trait} association studies. 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 }