{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "77aca4d4", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:07:19.056309Z", "iopub.status.busy": "2025-03-25T06:07:19.056081Z", "iopub.status.idle": "2025-03-25T06:07:19.221176Z", "shell.execute_reply": "2025-03-25T06:07:19.220839Z" } }, "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 = \"Parkinsons_Disease\"\n", "cohort = \"GSE103099\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Parkinsons_Disease\"\n", "in_cohort_dir = \"../../input/GEO/Parkinsons_Disease/GSE103099\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Parkinsons_Disease/GSE103099.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Parkinsons_Disease/gene_data/GSE103099.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Parkinsons_Disease/clinical_data/GSE103099.csv\"\n", "json_path = \"../../output/preprocess/Parkinsons_Disease/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "729c1156", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "4181fc13", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:07:19.222601Z", "iopub.status.busy": "2025-03-25T06:07:19.222459Z", "iopub.status.idle": "2025-03-25T06:07:19.395441Z", "shell.execute_reply": "2025-03-25T06:07:19.395106Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background Information:\n", "!Series_title\t\"Helper-dependent CAV-2 vector-mediated LRRK2G2019S expression in the M. murinus brain induces Parkinson's disease-like histological lesions and motor symptoms\"\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: ['gender: female'], 1: ['age: 2 year old'], 2: ['cav-2 infection helper dependent 109 physical particles: no infection', 'cav-2 infection helper dependent 109 physical particles: Ipsilateral infection', 'cav-2 infection helper dependent 109 physical particles: Contralateral infection', 'cav-2 infection helper dependent 109 physical particles: Ipsilateral to CAV-2 infection', 'cav-2 infection helper dependent 109 physical particles: Contralateral to CAV-2 infection'], 3: ['brain tissue: Striatum', 'brain tissue: Frontal Cortex (FC)', 'brain tissue: Midbrain']}\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": "bc463106", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": 3, "id": "eeafdf99", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:07:19.396598Z", "iopub.status.busy": "2025-03-25T06:07:19.396492Z", "iopub.status.idle": "2025-03-25T06:07:19.408427Z", "shell.execute_reply": "2025-03-25T06:07:19.408121Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of clinical features:\n", "GSM2742123: [0.0, 2.0, 0.0]\n", "GSM2742124: [0.0, 2.0, 0.0]\n", "GSM2742125: [0.0, 2.0, 0.0]\n", "GSM2742126: [0.0, 2.0, 0.0]\n", "GSM2742127: [1.0, 2.0, 0.0]\n", "GSM2742128: [1.0, 2.0, 0.0]\n", "GSM2742129: [1.0, 2.0, 0.0]\n", "GSM2742130: [1.0, 2.0, 0.0]\n", "GSM2742131: [0.0, 2.0, 0.0]\n", "GSM2742132: [0.0, 2.0, 0.0]\n", "GSM2742133: [0.0, 2.0, 0.0]\n", "GSM2742134: [0.0, 2.0, 0.0]\n", "GSM2742135: [1.0, 2.0, 0.0]\n", "GSM2742136: [1.0, 2.0, 0.0]\n", "GSM2742137: [1.0, 2.0, 0.0]\n", "GSM2742138: [1.0, 2.0, 0.0]\n", "GSM2742139: [0.0, 2.0, 0.0]\n", "GSM2742140: [0.0, 2.0, 0.0]\n", "GSM2742141: [0.0, 2.0, 0.0]\n", "GSM2742142: [0.0, 2.0, 0.0]\n", "GSM2743405: [0.0, 2.0, 0.0]\n", "GSM2743406: [0.0, 2.0, 0.0]\n", "GSM2743407: [0.0, 2.0, 0.0]\n", "GSM2743408: [0.0, 2.0, 0.0]\n", "GSM2743409: [1.0, 2.0, 0.0]\n", "GSM2743410: [1.0, 2.0, 0.0]\n", "GSM2743411: [1.0, 2.0, 0.0]\n", "GSM2743412: [1.0, 2.0, 0.0]\n", "GSM2743413: [0.0, 2.0, 0.0]\n", "GSM2743414: [0.0, 2.0, 0.0]\n", "GSM2743415: [0.0, 2.0, 0.0]\n", "GSM2743416: [0.0, 2.0, 0.0]\n", "GSM2743417: [1.0, 2.0, 0.0]\n", "GSM2743418: [1.0, 2.0, 0.0]\n", "GSM2743419: [1.0, 2.0, 0.0]\n", "GSM2743420: [1.0, 2.0, 0.0]\n", "GSM2743421: [0.0, 2.0, 0.0]\n", "GSM2743422: [0.0, 2.0, 0.0]\n", "GSM2743423: [0.0, 2.0, 0.0]\n", "GSM2743424: [0.0, 2.0, 0.0]\n", "GSM2743425: [0.0, 2.0, 0.0]\n", "GSM2743426: [0.0, 2.0, 0.0]\n", "GSM2743427: [0.0, 2.0, 0.0]\n", "GSM2743428: [1.0, 2.0, 0.0]\n", "GSM2743429: [1.0, 2.0, 0.0]\n", "GSM2743430: [1.0, 2.0, 0.0]\n", "GSM2743431: [1.0, 2.0, 0.0]\n", "GSM2743432: [0.0, 2.0, 0.0]\n", "GSM2743433: [0.0, 2.0, 0.0]\n", "GSM2743434: [0.0, 2.0, 0.0]\n", "GSM2743435: [0.0, 2.0, 0.0]\n", "GSM2743436: [1.0, 2.0, 0.0]\n", "GSM2743437: [1.0, 2.0, 0.0]\n", "GSM2743438: [1.0, 2.0, 0.0]\n", "GSM2743439: [1.0, 2.0, 0.0]\n", "GSM2743440: [0.0, 2.0, 0.0]\n", "GSM2743441: [0.0, 2.0, 0.0]\n", "GSM2743442: [0.0, 2.0, 0.0]\n", "GSM2743443: [0.0, 2.0, 0.0]\n", "Clinical features saved to ../../output/preprocess/Parkinsons_Disease/clinical_data/GSE103099.csv\n" ] } ], "source": [ "# 1. Gene Expression Data Availability\n", "# From the output, we see that this dataset contains brain tissue samples\n", "# This indicates it likely contains gene expression data, as mentioned in the series title\n", "is_gene_available = True\n", "\n", "# 2. Variable Availability and Data Type Conversion\n", "# 2.1 Data Availability\n", "\n", "# Trait - Parkinson's Disease\n", "# From the background information, this is a Parkinson's disease study with different conditions\n", "# The key information appears to be in row 2, which shows CAV-2 infection status\n", "trait_row = 2\n", "\n", "# Age - Available in row 1\n", "age_row = 1\n", "\n", "# Gender - Available in row 0\n", "gender_row = 0\n", "\n", "# 2.2 Data Type Conversion\n", "\n", "def convert_trait(value):\n", " \"\"\"Convert the CAV-2 infection status to binary trait value for Parkinson's Disease\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Based on the data, we can infer that samples with \"Ipsilateral infection\" or \"Ipsilateral to CAV-2 infection\" \n", " # are cases with Parkinson's Disease-like symptoms (as per the study title)\n", " if 'ipsilateral' in value.lower():\n", " return 1 # Case (PD model)\n", " elif 'no infection' in value.lower():\n", " return 0 # Control\n", " else:\n", " # Contralateral samples are likely controls as well\n", " return 0\n", "\n", "def convert_age(value):\n", " \"\"\"Convert age value to continuous format\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Extract numeric value\n", " import re\n", " numeric_match = re.search(r'(\\d+(?:\\.\\d+)?)', value)\n", " if numeric_match:\n", " return float(numeric_match.group(1))\n", " return None\n", "\n", "def convert_gender(value):\n", " \"\"\"Convert gender to binary (0 for female, 1 for male)\"\"\"\n", " if value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " if value.lower() == 'female':\n", " return 0\n", " elif value.lower() == 'male':\n", " return 1\n", " return None\n", "\n", "# 3. Save Metadata - Initial filtering on usability\n", "is_trait_available = trait_row is not None\n", "validate_and_save_cohort_info(\n", " is_final=False,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=is_gene_available,\n", " is_trait_available=is_trait_available\n", ")\n", "\n", "# 4. Clinical Feature Extraction\n", "if trait_row is not None:\n", " # Get 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 extracted clinical features\n", " preview = preview_df(clinical_features)\n", " print(\"Preview of clinical features:\")\n", " for col, values in preview.items():\n", " print(f\"{col}: {values}\")\n", " \n", " # Save the clinical features to the specified file\n", " clinical_features.to_csv(out_clinical_data_file)\n", " print(f\"Clinical features saved to {out_clinical_data_file}\")\n" ] }, { "cell_type": "markdown", "id": "6750ac3e", "metadata": {}, "source": [ "### Step 3: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": 4, "id": "3cf22002", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:07:19.409512Z", "iopub.status.busy": "2025-03-25T06:07:19.409409Z", "iopub.status.idle": "2025-03-25T06:07:19.700040Z", "shell.execute_reply": "2025-03-25T06:07:19.699673Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 20 gene/probe identifiers:\n", "Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n", " '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n", " '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n", " '1552263_at', '1552264_a_at', '1552266_at'],\n", " dtype='object', name='ID')\n" ] } ], "source": [ "# 1. First get the file paths again to access the matrix file\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Use the get_genetic_data function from the library to get the gene_data from the matrix_file\n", "gene_data = get_genetic_data(matrix_file)\n", "\n", "# 3. Print the first 20 row IDs (gene or probe identifiers) for future observation\n", "print(\"First 20 gene/probe identifiers:\")\n", "print(gene_data.index[:20])\n" ] }, { "cell_type": "markdown", "id": "3eca8036", "metadata": {}, "source": [ "### Step 4: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": 5, "id": "9410d259", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:07:19.701342Z", "iopub.status.busy": "2025-03-25T06:07:19.701222Z", "iopub.status.idle": "2025-03-25T06:07:19.703223Z", "shell.execute_reply": "2025-03-25T06:07:19.702918Z" } }, "outputs": [], "source": [ "# Examining the gene identifiers shown in the previous step\n", "\n", "# These identifiers (like '1007_s_at', '1053_at', etc.) appear to be Affymetrix probe IDs\n", "# rather than standard human gene symbols (which would look like BRCA1, TP53, etc.)\n", "\n", "# Affymetrix probe IDs need to be mapped to standard gene symbols for analysis\n", "# They follow the pattern of numbers followed by '_at', '_s_at', '_x_at', etc.\n", "\n", "requires_gene_mapping = True\n" ] }, { "cell_type": "markdown", "id": "8a35a272", "metadata": {}, "source": [ "### Step 5: Gene Annotation" ] }, { "cell_type": "code", "execution_count": 6, "id": "81df3124", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:07:19.704350Z", "iopub.status.busy": "2025-03-25T06:07:19.704247Z", "iopub.status.idle": "2025-03-25T06:07:25.540617Z", "shell.execute_reply": "2025-03-25T06:07:25.539966Z" } }, "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": "d38f1f98", "metadata": {}, "source": [ "### Step 6: Gene Identifier Mapping" ] }, { "cell_type": "code", "execution_count": 7, "id": "e2f9427e", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:07:25.542562Z", "iopub.status.busy": "2025-03-25T06:07:25.542433Z", "iopub.status.idle": "2025-03-25T06:07:25.837756Z", "shell.execute_reply": "2025-03-25T06:07:25.837109Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of gene-level expression data after mapping:\n", " GSM2742123 GSM2742124 GSM2742125 GSM2742126 GSM2742127 \\\n", "Gene \n", "A1BG 112.98580 51.284040 91.61227 57.792190 53.40241 \n", "A1BG-AS1 35.40216 87.730980 12.45806 15.830780 9.63248 \n", "A1CF 158.16758 15.254397 133.09628 46.997870 41.67348 \n", "A2M 219.19014 146.995100 233.62709 98.652383 174.34974 \n", "A2M-AS1 14.94806 73.829570 47.44039 67.683960 71.63633 \n", "\n", " GSM2742128 GSM2742129 GSM2742130 GSM2742131 GSM2742132 ... \\\n", "Gene ... \n", "A1BG 88.76160 76.598930 25.739190 105.839300 111.64980 ... \n", "A1BG-AS1 41.64716 16.129800 10.885150 155.726300 94.45621 ... \n", "A1CF 232.87153 31.342238 144.736461 43.862739 124.20535 ... \n", "A2M 176.68240 157.365300 91.370419 224.421200 103.24955 ... \n", "A2M-AS1 43.34108 55.676130 65.777630 62.850560 29.51359 ... \n", "\n", " GSM2743434 GSM2743435 GSM2743436 GSM2743437 GSM2743438 \\\n", "Gene \n", "A1BG 51.99334 65.304150 46.61287 28.98090 36.202150 \n", "A1BG-AS1 105.90780 24.174450 42.18822 42.57311 18.670320 \n", "A1CF 84.56621 32.030504 54.42781 73.77308 115.352557 \n", "A2M 222.43602 175.872810 99.06313 145.98750 170.675240 \n", "A2M-AS1 26.71567 61.193560 42.64635 40.60043 50.356220 \n", "\n", " GSM2743439 GSM2743440 GSM2743441 GSM2743442 GSM2743443 \n", "Gene \n", "A1BG 47.26677 202.412200 70.54287 98.22469 7.038130 \n", "A1BG-AS1 60.96607 25.605150 22.84626 22.60111 19.564220 \n", "A1CF 210.05643 252.642000 16.19210 195.27105 36.440795 \n", "A2M 167.63025 139.130760 129.69639 109.79919 190.351270 \n", "A2M-AS1 23.55995 5.203714 45.21909 88.18087 5.774200 \n", "\n", "[5 rows x 59 columns]\n" ] } ], "source": [ "# 1. First identify the columns in the gene annotation that contain probe IDs and gene symbols\n", "# Based on the preview, 'ID' contains the probe IDs (e.g., '1007_s_at') and 'Gene Symbol' contains the gene symbols\n", "prob_col = 'ID'\n", "gene_col = 'Gene Symbol'\n", "\n", "# 2. Get the gene mapping dataframe using the get_gene_mapping function\n", "gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)\n", "\n", "# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression\n", "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n", "\n", "# Check the first few rows of the mapped gene data to verify the transformation\n", "print(\"Preview of gene-level expression data after mapping:\")\n", "print(gene_data.head())\n" ] }, { "cell_type": "markdown", "id": "f79f8570", "metadata": {}, "source": [ "### Step 7: Data Normalization and Linking" ] }, { "cell_type": "code", "execution_count": 8, "id": "4565afbe", "metadata": { "execution": { "iopub.execute_input": "2025-03-25T06:07:25.839613Z", "iopub.status.busy": "2025-03-25T06:07:25.839489Z", "iopub.status.idle": "2025-03-25T06:07:36.621201Z", "shell.execute_reply": "2025-03-25T06:07:36.620822Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gene expression data shape after normalization: (19845, 59)\n", "First 5 normalized gene symbols:\n", "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1'], dtype='object', name='Gene')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Normalized gene data saved to ../../output/preprocess/Parkinsons_Disease/gene_data/GSE103099.csv\n", "Loaded clinical data from file\n", "Clinical data shape: (3, 59)\n", "Clinical data preview:\n", "{'GSM2742123': [0.0, 2.0, 0.0], 'GSM2742124': [0.0, 2.0, 0.0], 'GSM2742125': [0.0, 2.0, 0.0], 'GSM2742126': [0.0, 2.0, 0.0], 'GSM2742127': [1.0, 2.0, 0.0], 'GSM2742128': [1.0, 2.0, 0.0], 'GSM2742129': [1.0, 2.0, 0.0], 'GSM2742130': [1.0, 2.0, 0.0], 'GSM2742131': [0.0, 2.0, 0.0], 'GSM2742132': [0.0, 2.0, 0.0], 'GSM2742133': [0.0, 2.0, 0.0], 'GSM2742134': [0.0, 2.0, 0.0], 'GSM2742135': [1.0, 2.0, 0.0], 'GSM2742136': [1.0, 2.0, 0.0], 'GSM2742137': [1.0, 2.0, 0.0], 'GSM2742138': [1.0, 2.0, 0.0], 'GSM2742139': [0.0, 2.0, 0.0], 'GSM2742140': [0.0, 2.0, 0.0], 'GSM2742141': [0.0, 2.0, 0.0], 'GSM2742142': [0.0, 2.0, 0.0], 'GSM2743405': [0.0, 2.0, 0.0], 'GSM2743406': [0.0, 2.0, 0.0], 'GSM2743407': [0.0, 2.0, 0.0], 'GSM2743408': [0.0, 2.0, 0.0], 'GSM2743409': [1.0, 2.0, 0.0], 'GSM2743410': [1.0, 2.0, 0.0], 'GSM2743411': [1.0, 2.0, 0.0], 'GSM2743412': [1.0, 2.0, 0.0], 'GSM2743413': [0.0, 2.0, 0.0], 'GSM2743414': [0.0, 2.0, 0.0], 'GSM2743415': [0.0, 2.0, 0.0], 'GSM2743416': [0.0, 2.0, 0.0], 'GSM2743417': [1.0, 2.0, 0.0], 'GSM2743418': [1.0, 2.0, 0.0], 'GSM2743419': [1.0, 2.0, 0.0], 'GSM2743420': [1.0, 2.0, 0.0], 'GSM2743421': [0.0, 2.0, 0.0], 'GSM2743422': [0.0, 2.0, 0.0], 'GSM2743423': [0.0, 2.0, 0.0], 'GSM2743424': [0.0, 2.0, 0.0], 'GSM2743425': [0.0, 2.0, 0.0], 'GSM2743426': [0.0, 2.0, 0.0], 'GSM2743427': [0.0, 2.0, 0.0], 'GSM2743428': [1.0, 2.0, 0.0], 'GSM2743429': [1.0, 2.0, 0.0], 'GSM2743430': [1.0, 2.0, 0.0], 'GSM2743431': [1.0, 2.0, 0.0], 'GSM2743432': [0.0, 2.0, 0.0], 'GSM2743433': [0.0, 2.0, 0.0], 'GSM2743434': [0.0, 2.0, 0.0], 'GSM2743435': [0.0, 2.0, 0.0], 'GSM2743436': [1.0, 2.0, 0.0], 'GSM2743437': [1.0, 2.0, 0.0], 'GSM2743438': [1.0, 2.0, 0.0], 'GSM2743439': [1.0, 2.0, 0.0], 'GSM2743440': [0.0, 2.0, 0.0], 'GSM2743441': [0.0, 2.0, 0.0], 'GSM2743442': [0.0, 2.0, 0.0], 'GSM2743443': [0.0, 2.0, 0.0]}\n", "Linked data shape: (59, 19848)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after handling missing values: (59, 19848)\n", "For the feature 'Parkinsons_Disease', the least common label is '1.0' with 24 occurrences. This represents 40.68% of the dataset.\n", "The distribution of the feature 'Parkinsons_Disease' in this dataset is fine.\n", "\n", "Quartiles for 'Age':\n", " 25%: 2.0\n", " 50% (Median): 2.0\n", " 75%: 2.0\n", "Min: 2.0\n", "Max: 2.0\n", "The distribution of the feature 'Age' in this dataset is severely biased.\n", "\n", "For the feature 'Gender', the least common label is '0.0' with 59 occurrences. This represents 100.00% of the dataset.\n", "The distribution of the feature 'Gender' in this dataset is severely biased.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data shape after removing biased features: (59, 19846)\n", "Is the trait biased: False\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linked data saved to ../../output/preprocess/Parkinsons_Disease/GSE103099.csv\n" ] } ], "source": [ "# 1. Normalize gene symbols from the already mapped gene expression data from Step 6\n", "# Apply normalization to standardize gene symbols\n", "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n", "print(f\"Gene expression data shape after normalization: {normalized_gene_data.shape}\")\n", "print(\"First 5 normalized gene symbols:\")\n", "print(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. Load the clinical data that was already processed in Step 2\n", "# We need to load the clinical data from the file that was saved in Step 2\n", "if os.path.exists(out_clinical_data_file):\n", " clinical_data_processed = pd.read_csv(out_clinical_data_file, index_col=0)\n", " print(\"Loaded clinical data from file\")\n", "else:\n", " # If for some reason the file wasn't saved, recreate the clinical features using the same parameters as in Step 2\n", " soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", " background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n", " \n", " # Use the same conversion function and trait_row from Step 2\n", " def convert_trait(value):\n", " \"\"\"Convert occupation to binary trait (exposure risk for Parkinson's)\"\"\"\n", " if not isinstance(value, str):\n", " return None\n", " \n", " value = value.lower().split(\": \")[-1].strip()\n", " \n", " if \"farmworker\" in value:\n", " return 1 # Higher pesticide exposure (risk factor for Parkinson's)\n", " elif \"manual worker\" in value:\n", " return 0 # Lower pesticide exposure \n", " else:\n", " return None\n", " \n", " # Use the exact same parameters as we determined in Step 2\n", " clinical_data_processed = geo_select_clinical_features(\n", " clinical_df=clinical_data,\n", " trait=trait,\n", " trait_row=0, # From Step 2\n", " convert_trait=convert_trait,\n", " age_row=None, # From Step 2\n", " convert_age=None,\n", " gender_row=None, # From Step 2\n", " convert_gender=None\n", " )\n", " \n", " # Save it again just to be sure\n", " os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n", " clinical_data_processed.to_csv(out_clinical_data_file)\n", "\n", "print(\"Clinical data shape:\", clinical_data_processed.shape)\n", "print(\"Clinical data preview:\")\n", "print(preview_df(clinical_data_processed))\n", "\n", "# 3. Link clinical and genetic data\n", "# The clinical data is oriented with genes/traits as rows and samples as columns\n", "# Transpose the normalized gene data to match this orientation (samples as columns)\n", "genetic_data_t = normalized_gene_data\n", "\n", "# Link clinical and genetic data vertically\n", "linked_data = geo_link_clinical_genetic_data(clinical_data_processed, genetic_data_t)\n", "print(f\"Linked data shape: {linked_data.shape}\")\n", "\n", "# 4. Handle missing values\n", "linked_data = handle_missing_values(linked_data, trait)\n", "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n", "\n", "# 5. Determine if trait and demographic features are biased\n", "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n", "print(f\"Data shape after removing biased features: {linked_data.shape}\")\n", "print(f\"Is the trait biased: {is_biased}\")\n", "\n", "# 6. Validate and save cohort info\n", "is_usable = validate_and_save_cohort_info(\n", " is_final=True,\n", " cohort=cohort,\n", " info_path=json_path,\n", " is_gene_available=True,\n", " is_trait_available=True,\n", " is_biased=is_biased,\n", " df=linked_data,\n", " note=\"Dataset contains gene expression data from blood samples comparing farmworkers (with higher pesticide exposure, a risk factor for Parkinson's) to manual workers.\"\n", ")\n", "\n", "# 7. Save linked data if usable\n", "if is_usable:\n", " os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n", " linked_data.to_csv(out_data_file)\n", " print(f\"Linked data saved to {out_data_file}\")\n", "else:\n", " print(\"Dataset deemed not usable. Linked data was 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 }