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{
"cells": [
{
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"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 = \"Rheumatoid_Arthritis\"\n",
"cohort = \"GSE236924\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Rheumatoid_Arthritis\"\n",
"in_cohort_dir = \"../../input/GEO/Rheumatoid_Arthritis/GSE236924\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Rheumatoid_Arthritis/GSE236924.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Rheumatoid_Arthritis/gene_data/GSE236924.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Rheumatoid_Arthritis/clinical_data/GSE236924.csv\"\n",
"json_path = \"../../output/preprocess/Rheumatoid_Arthritis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "4453e061",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a5117cf3",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"SIRPa agonist antibody treatment ameliorates experimental arthritis and colitis [array]\"\n",
"!Series_summary\t\"The innate immune system is finely tuned to enable. rapid response to pathogenic stimuli but keep quiescent during tissue homeostasis. Balance of activating and inhibitory signaling sets a threshold for immune activation. Signal regulatory protein (SIRPa) is an immune inhibitory receptor expressed by myeloid cells and interacts with CD47 to inhibit immune cell phagocytosis, migration, and activation. Despite the progress of SIRPa and CD47 antagonist antibodies to promote anti-cancer immunity, it is not yet known whether therapeutic SIRPa receptor agonism could restrain excessive autoimmune inflammation in the context of autoimmunity. Here, we reported that increased neutrophil- and monocyte-associated genes including SIRPA in inflamed tissues biopsies of rheumatoid arthritis and inflammatory bowel diseases, and elevated SIRPA in colonic biopsies is associated with treatment refractory ulcerative colitis patients. We next identified a novel agonistic anti-SIRPa antibody that exhibited potent anti-inflammatory effects in reducing neutrophil and monocytes chemotaxis and tissue infiltration. In preclinical models of arthritis and colitis, anti-SIRPa agonistic antibody ameliorates autoimmune joint inflammation and inflammatory colitis through reducing neutrophils and monocytes in tissues. Our work provides a proof-of-concept for SIRPa receptor agonism for suppressing excessive innate immune activation and autoimmune inflammatory therapeutic treatment\"\n",
"!Series_overall_design\t\"Comparison of non-disease joint tissue to tissue samples from osteoarthritis and rheumatoid arthritis\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['disease: OA', 'disease: Control', 'disease: RA']}\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": "72e8aa95",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2486cbe3",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preview of clinical data:\n",
"{'GSM7585682': [0.0], 'GSM7585683': [0.0], 'GSM7585684': [0.0], 'GSM7585685': [0.0], 'GSM7585686': [1.0], 'GSM7585687': [0.0], 'GSM7585688': [0.0], 'GSM7585689': [0.0], 'GSM7585690': [0.0], 'GSM7585691': [1.0], 'GSM7585692': [0.0], 'GSM7585693': [1.0], 'GSM7585694': [0.0], 'GSM7585695': [0.0], 'GSM7585696': [0.0], 'GSM7585697': [1.0], 'GSM7585698': [1.0], 'GSM7585699': [0.0], 'GSM7585700': [0.0], 'GSM7585701': [1.0], 'GSM7585702': [0.0], 'GSM7585703': [0.0], 'GSM7585704': [0.0], 'GSM7585705': [0.0], 'GSM7585706': [0.0], 'GSM7585707': [0.0], 'GSM7585708': [1.0], 'GSM7585709': [1.0], 'GSM7585710': [0.0], 'GSM7585711': [0.0], 'GSM7585712': [0.0], 'GSM7585713': [1.0], 'GSM7585714': [0.0], 'GSM7585715': [0.0], 'GSM7585716': [1.0], 'GSM7585717': [0.0], 'GSM7585718': [0.0], 'GSM7585719': [0.0], 'GSM7585720': [0.0], 'GSM7585721': [0.0], 'GSM7585722': [1.0], 'GSM7585723': [1.0], 'GSM7585724': [0.0], 'GSM7585725': [0.0], 'GSM7585726': [0.0], 'GSM7585727': [0.0], 'GSM7585728': [0.0], 'GSM7585729': [1.0], 'GSM7585730': [0.0], 'GSM7585731': [0.0], 'GSM7585732': [0.0], 'GSM7585733': [0.0], 'GSM7585734': [0.0], 'GSM7585735': [0.0], 'GSM7585736': [0.0], 'GSM7585737': [1.0], 'GSM7585738': [0.0], 'GSM7585739': [0.0], 'GSM7585740': [0.0], 'GSM7585741': [1.0], 'GSM7585742': [1.0], 'GSM7585743': [1.0], 'GSM7585744': [1.0], 'GSM7585745': [0.0], 'GSM7585746': [1.0], 'GSM7585747': [1.0], 'GSM7585748': [0.0], 'GSM7585749': [0.0], 'GSM7585750': [0.0], 'GSM7585751': [0.0], 'GSM7585752': [0.0], 'GSM7585753': [0.0], 'GSM7585754': [0.0], 'GSM7585755': [0.0], 'GSM7585756': [0.0], 'GSM7585757': [0.0], 'GSM7585758': [0.0], 'GSM7585759': [0.0], 'GSM7585760': [1.0], 'GSM7585761': [0.0], 'GSM7585762': [0.0], 'GSM7585763': [0.0], 'GSM7585764': [1.0], 'GSM7585765': [0.0], 'GSM7585766': [0.0], 'GSM7585767': [0.0], 'GSM7585768': [0.0], 'GSM7585769': [1.0], 'GSM7585770': [0.0], 'GSM7585771': [1.0], 'GSM7585772': [1.0], 'GSM7585773': [0.0], 'GSM7585774': [0.0], 'GSM7585775': [0.0], 'GSM7585776': [0.0], 'GSM7585777': [0.0], 'GSM7585778': [0.0], 'GSM7585779': [1.0], 'GSM7585780': [1.0], 'GSM7585781': [0.0], 'GSM7585782': [0.0], 'GSM7585783': [0.0], 'GSM7585784': [1.0], 'GSM7585785': [1.0], 'GSM7585786': [0.0], 'GSM7585787': [0.0], 'GSM7585788': [0.0], 'GSM7585789': [0.0], 'GSM7585790': [1.0], 'GSM7585791': [0.0], 'GSM7585792': [0.0], 'GSM7585793': [0.0], 'GSM7585794': [1.0], 'GSM7585795': [1.0], 'GSM7585796': [0.0], 'GSM7585797': [0.0], 'GSM7585798': [1.0], 'GSM7585799': [1.0], 'GSM7585800': [1.0], 'GSM7585801': [0.0], 'GSM7585802': [0.0], 'GSM7585803': [0.0], 'GSM7585804': [0.0], 'GSM7585805': [0.0], 'GSM7585806': [0.0], 'GSM7585807': [0.0], 'GSM7585808': [1.0], 'GSM7585809': [0.0], 'GSM7585810': [0.0], 'GSM7585811': [0.0], 'GSM7585812': [0.0], 'GSM7585813': [0.0]}\n",
"Clinical data saved to ../../output/preprocess/Rheumatoid_Arthritis/clinical_data/GSE236924.csv\n"
]
}
],
"source": [
"# Let's analyze the given information\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the title and summary, this appears to be an array dataset comparing joint tissues\n",
"# from RA, OA and control samples. This suggests it contains gene expression data.\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# Looking at the sample characteristics dictionary, we only have one key (0) with disease information\n",
"trait_row = 0 # Disease status is available in row 0\n",
"age_row = None # Age information is not available\n",
"gender_row = None # Gender information is not available\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value):\n",
" \"\"\"Convert trait values to binary (0 for Control/OA, 1 for RA)\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if isinstance(value, str) and \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Convert to binary where RA=1, others=0\n",
" if value == \"RA\":\n",
" return 1\n",
" elif value in [\"Control\", \"OA\"]:\n",
" return 0\n",
" else:\n",
" return None\n",
"\n",
"# No need for age and gender conversion functions since data is not available\n",
"\n",
"# 3. Save Metadata - Initial Filtering\n",
"# Determine if trait data is available\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save initial cohort information\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 not None, we need to extract clinical features\n",
"if trait_row is not None:\n",
" # Extract clinical features\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=None,\n",
" gender_row=gender_row,\n",
" convert_gender=None\n",
" )\n",
" \n",
" # Preview the extracted clinical data\n",
" print(\"Preview of clinical data:\")\n",
" print(preview_df(selected_clinical_df))\n",
" \n",
" # Save clinical data to file\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "51641a15",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a7b59d66",
"metadata": {
"execution": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n",
" '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n",
" '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n",
" '1552263_at', '1552264_a_at', '1552266_at'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n",
"gene_data = get_genetic_data(matrix_file)\n",
"\n",
"# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n",
"print(gene_data.index[:20])\n"
]
},
{
"cell_type": "markdown",
"id": "4b6e6b15",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1b7b9372",
"metadata": {
"execution": {
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}
},
"outputs": [],
"source": [
"# The identifiers in the gene expression data don't appear to be human gene symbols\n",
"# They look like Affymetrix probe IDs (like \"1007_s_at\", \"1053_at\") which need to be mapped to gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "70fab6e3",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b80e9a55",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "01c74eb3",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sample of gene mapping:\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"After mapping, gene expression data shape: (21278, 132)\n",
"Sample of gene symbols in the mapped data:\n",
"Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n",
" 'A4GALT', 'A4GNT', 'AA06'],\n",
" dtype='object', name='Gene')\n"
]
}
],
"source": [
"# 1. From the gene annotation preview, we can see:\n",
"# - 'ID' contains probe identifiers matching those in gene_data.index\n",
"# - 'Gene Symbol' contains the gene symbols we want to map to\n",
"\n",
"# 2. Extract the mapping between probe IDs and gene symbols\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(\"Sample of gene mapping:\")\n",
"print(gene_mapping.head())\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Print the shape of the result and a sample of gene symbols to verify\n",
"print(\"\\nAfter mapping, gene expression data shape:\", gene_data.shape)\n",
"print(\"Sample of gene symbols in the mapped data:\")\n",
"print(gene_data.index[:10])\n"
]
},
{
"cell_type": "markdown",
"id": "eefa4615",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
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"execution_count": 8,
"id": "55ac829f",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T03:52:16.709145Z"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data shape: (19845, 132)\n",
"Sample of normalized gene symbols:\n",
"Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n",
" 'A4GALT', 'A4GNT', 'AA06'],\n",
" dtype='object', name='Gene')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Rheumatoid_Arthritis/gene_data/GSE236924.csv\n",
"Clinical data index: Index(['Rheumatoid_Arthritis'], dtype='object')\n",
"Linked data shape: (132, 19846)\n",
"Linked data column names: Index(['Rheumatoid_Arthritis', 'A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1',\n",
" 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT'],\n",
" dtype='object')\n",
"Using trait column name: Rheumatoid_Arthritis\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (132, 19846)\n",
"For the feature 'Rheumatoid_Arthritis', the least common label is '1.0' with 36 occurrences. This represents 27.27% of the dataset.\n",
"The distribution of the feature 'Rheumatoid_Arthritis' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Usable linked data saved to ../../output/preprocess/Rheumatoid_Arthritis/GSE236924.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the obtained gene expression data\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
"print(\"Sample of normalized gene symbols:\")\n",
"print(normalized_gene_data.index[:10])\n",
"\n",
"# Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Link the clinical and genetic data\n",
"# First, let's reload the clinical data to ensure we have the correct structure\n",
"clinical_data_reloaded = geo_select_clinical_features(\n",
" clinical_data, \n",
" trait, \n",
" trait_row, \n",
" convert_trait,\n",
" None, # age_row is None \n",
" None, # convert_age is None\n",
" None, # gender_row is None\n",
" None # convert_gender is None\n",
")\n",
"\n",
"# Print column names to debug\n",
"print(\"Clinical data index:\", clinical_data_reloaded.index)\n",
"\n",
"# Link the clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(clinical_data_reloaded, normalized_gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"print(\"Linked data column names:\", linked_data.columns[:10]) # Print some column names\n",
"\n",
"# 4. Handle missing values in the linked data\n",
"# The first column should contain the trait data\n",
"trait_column_name = clinical_data_reloaded.index[0]\n",
"print(f\"Using trait column name: {trait_column_name}\")\n",
"linked_data = handle_missing_values(linked_data, trait_column_name)\n",
"print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 5. Determine whether the trait and some demographic features are severely biased\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait_column_name)\n",
"\n",
"# 6. Conduct quality check and save the cohort information\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=True, \n",
" is_biased=is_trait_biased, \n",
" df=unbiased_linked_data,\n",
" note=\"Dataset contains gene expression data for Rheumatoid Arthritis cases versus controls (OA and normal samples).\"\n",
")\n",
"\n",
"# 7. If the linked data is usable, save it as a CSV file\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Usable linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Linked data was not usable and was not saved.\")"
]
}
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|