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{
"cells": [
{
"cell_type": "code",
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"metadata": {
"execution": {
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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 = \"Essential_Thrombocythemia\"\n",
"cohort = \"GSE65161\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Essential_Thrombocythemia\"\n",
"in_cohort_dir = \"../../input/GEO/Essential_Thrombocythemia/GSE65161\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Essential_Thrombocythemia/GSE65161.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Essential_Thrombocythemia/gene_data/GSE65161.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE65161.csv\"\n",
"json_path = \"../../output/preprocess/Essential_Thrombocythemia/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "19cade2f",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e4e60d94",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:16:22.914808Z",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Mediator kinase inhibition further activates super-enhancer-associated genes in AML\"\n",
"!Series_summary\t\"Super-enhancers (SEs), which are composed of large clusters of enhancers densely loaded with the Mediator complex, transcription factors and chromatin regulators, drive high expression of genes implicated in cell identity and disease, such as lineage-controlling transcription factors and oncogenes. BRD4 and CDK7 are positive regulators of SE-mediated transcription. By contrast, negative regulators of SE-associated genes have not been well described. Here we show that the Mediator-associated kinases cyclin-dependent kinase 8 (CDK8) and CDK19 restrain increased activation of key SE-associated genes in acute myeloid leukaemia (AML) cells. We report that the natural product cortistatin A (CA) selectively inhibits Mediator kinases, has anti-leukaemic activity in vitro and in vivo, and disproportionately induces upregulation of SE-associated genes in CA-sensitive AML cell lines but not in CA-insensitive cell lines. In AML cells, CA upregulated SE-associated genes with tumour suppressor and lineage-controlling functions, including the transcription factors CEBPA, IRF8, IRF1 and ETV6. The BRD4 inhibitor I-BET151 downregulated these SE-associated genes, yet also has anti-leukaemic activity. Individually increasing or decreasing the expression of these transcription factors suppressed AML cell growth, providing evidence that leukaemia cells are sensitive to the dosage of SE-associated genes. Our results demonstrate that Mediator kinases can negatively regulate SE-associated gene expression in specific cell types, and can be pharmacologically targeted as a therapeutic approach to AML.\"\n",
"!Series_summary\t\"\"\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: K562', 'cell line: MOLM-14', 'cell line: MV-4-11'], 1: ['treatment: DMSO', 'treatment: 25nM CA for 3hrs', 'treatment: 10nM CA for 24hrs'], 2: ['cell type: chronic myelogenous leukemia (CML)', 'cell type: MLL-AF9-rearranged AML', 'cell type: MLL-AF4-rearranged AML']}\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": "3ce8fe4d",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ca8de78a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:16:23.050079Z",
"iopub.status.busy": "2025-03-25T05:16:23.049953Z",
"iopub.status.idle": "2025-03-25T05:16:23.054774Z",
"shell.execute_reply": "2025-03-25T05:16:23.054490Z"
}
},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"import pandas as pd\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"def convert_trait(value):\n",
" if value is None:\n",
" return None\n",
" \n",
" value_str = str(value)\n",
" if ':' in value_str:\n",
" value_str = value_str.split(':', 1)[1].strip()\n",
" \n",
" # Convert cell type to binary for Essential Thrombocythemia\n",
" if 'AML' in value_str:\n",
" return 1 # Has the disease - Acute Myeloid Leukemia\n",
" elif 'CML' in value_str:\n",
" return 0 # Different disease - Chronic Myelogenous Leukemia\n",
" else:\n",
" return None # Unknown or other conditions\n",
"\n",
"def get_feature_data(clinical_df, row_idx, feature_name, convert_func):\n",
" \"\"\"Helper function to extract feature data from clinical DataFrame.\"\"\"\n",
" # Extract row\n",
" row_data = clinical_df.iloc[row_idx]\n",
" \n",
" # Create a new Series for the feature\n",
" feature_data = pd.Series(\n",
" [convert_func(val) for val in row_data.values],\n",
" index=row_data.index,\n",
" name=feature_name\n",
" )\n",
" \n",
" return pd.DataFrame(feature_data)\n",
"\n",
"# Trait data is available in row 2 (index) where cell type is mentioned\n",
"trait_row = 2\n",
"\n",
"# No age data available in the sample characteristics\n",
"age_row = None\n",
"convert_age = None\n",
"\n",
"# No gender data available in the sample characteristics\n",
"gender_row = None\n",
"convert_gender = None\n",
"\n",
"# Gene expression data availability\n",
"# Based on the background information, this is likely genomics data (not only miRNA or methylation)\n",
"is_gene_available = True\n",
"\n",
"# Trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Save metadata\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",
"# Clinical feature extraction if trait data is available\n",
"if trait_row is not None:\n",
" # Assuming clinical_data was previously loaded\n",
" clinical_data_file = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
" if os.path.exists(clinical_data_file):\n",
" clinical_data = pd.read_csv(clinical_data_file, index_col=0)\n",
" \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=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the extracted clinical features\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\", preview)\n",
" \n",
" # Create the output directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save the clinical features to a CSV file\n",
" selected_clinical_df.to_csv(out_clinical_data_file)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "5ef76b3c",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "92f9df00",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:16:23.055833Z",
"iopub.status.busy": "2025-03-25T05:16:23.055725Z",
"iopub.status.idle": "2025-03-25T05:16:23.210655Z",
"shell.execute_reply": "2025-03-25T05:16:23.210287Z"
}
},
"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": "d0b7e2d4",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1e97e2b4",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:16:23.211886Z",
"iopub.status.busy": "2025-03-25T05:16:23.211774Z",
"iopub.status.idle": "2025-03-25T05:16:23.213607Z",
"shell.execute_reply": "2025-03-25T05:16:23.213348Z"
}
},
"outputs": [],
"source": [
"# Examining the gene identifiers\n",
"# The gene identifiers shown (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",
"# These probe IDs need to be mapped to human gene symbols for meaningful analysis\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "c56bd3e8",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3d768013",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:16:23.214681Z",
"iopub.status.busy": "2025-03-25T05:16:23.214577Z",
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"shell.execute_reply": "2025-03-25T05:16:25.980069Z"
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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": "32d277c9",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ca3b1610",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T05:16:25.981738Z",
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"shell.execute_reply": "2025-03-25T05:16:26.534660Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping preview:\n",
"{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'Gene': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A']}\n",
"After mapping to gene symbols - first 10 genes:\n",
"Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n",
" 'A4GALT', 'A4GNT', 'AA06'],\n",
" dtype='object', name='Gene')\n",
"Gene expression data shape: (21278, 24)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"After normalizing gene symbols - shape: (19845, 24)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene expression data saved to ../../output/preprocess/Essential_Thrombocythemia/gene_data/GSE65161.csv\n"
]
}
],
"source": [
"# 1. Identify the columns in gene_annotation that store probe IDs and gene symbols\n",
"# From the preview, we can see:\n",
"# - The 'ID' column contains probe IDs like '1007_s_at' which match the gene expression data\n",
"# - The 'Gene Symbol' column contains gene symbols like 'DDR1 /// MIR4640'\n",
"\n",
"# 2. Get a gene mapping dataframe using the get_gene_mapping function\n",
"gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
"\n",
"# Check the mapping dataframe\n",
"print(\"Gene mapping preview:\")\n",
"print(preview_df(gene_mapping))\n",
"\n",
"# 3. Apply the gene mapping to convert probe-level data to gene expression data\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"\n",
"# Check the transformed gene expression data\n",
"print(\"After mapping to gene symbols - first 10 genes:\")\n",
"print(gene_data.index[:10])\n",
"print(\"Gene expression data shape:\", gene_data.shape)\n",
"\n",
"# Normalize gene symbols to handle synonyms\n",
"gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(\"After normalizing gene symbols - shape:\", gene_data.shape)\n",
"\n",
"# Create the output directory if it doesn't exist\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"\n",
"# Save the gene expression data to a CSV file\n",
"gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Gene expression data saved to {out_gene_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "b6be4e36",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d2f9b974",
"metadata": {
"execution": {
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"shell.execute_reply": "2025-03-25T05:16:34.222112Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting clinical features...\n",
"Clinical data preview:\n",
"{'GSM1585803': [0.0], 'GSM1585804': [0.0], 'GSM1585805': [0.0], 'GSM1585806': [0.0], 'GSM1585807': [0.0], 'GSM1585808': [0.0], 'GSM1585875': [1.0], 'GSM1585876': [1.0], 'GSM1585877': [1.0], 'GSM1585878': [1.0], 'GSM1585879': [1.0], 'GSM1585880': [1.0], 'GSM1585881': [1.0], 'GSM1585882': [1.0], 'GSM1585883': [1.0], 'GSM1585884': [1.0], 'GSM1585885': [1.0], 'GSM1585886': [1.0], 'GSM1585924': [1.0], 'GSM1585925': [1.0], 'GSM1585926': [1.0], 'GSM1585927': [1.0], 'GSM1585928': [1.0], 'GSM1585929': [1.0]}\n",
"Clinical data saved to ../../output/preprocess/Essential_Thrombocythemia/clinical_data/GSE65161.csv\n",
"Linking clinical and genetic data...\n",
"Linked data shape: (24, 19846)\n",
"Handling missing values...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data shape after handling missing values: (24, 19846)\n",
"Checking for bias in trait distribution...\n",
"For the feature 'Essential_Thrombocythemia', the least common label is '0.0' with 6 occurrences. This represents 25.00% of the dataset.\n",
"The distribution of the feature 'Essential_Thrombocythemia' in this dataset is fine.\n",
"\n",
"Dataset usability: True\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linked data saved to ../../output/preprocess/Essential_Thrombocythemia/GSE65161.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data - this was already done in Step 6\n",
"# We'll reuse the normalized gene_data from the previous step\n",
"\n",
"# 2. Link the clinical and genetic data\n",
"# First, we need to properly extract the clinical features as identified in Step 2\n",
"print(\"Extracting clinical features...\")\n",
"# Create the clinical features using the trait row identified in Step 2\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=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
")\n",
"\n",
"print(\"Clinical data preview:\")\n",
"print(preview_df(selected_clinical_df))\n",
"\n",
"# Save the clinical data to a CSV file\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"selected_clinical_df.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"\n",
"# Link clinical and genetic data\n",
"print(\"Linking clinical and genetic data...\")\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# 3. Handle missing values in the linked data\n",
"print(\"Handling missing values...\")\n",
"linked_data = handle_missing_values(linked_data, trait)\n",
"print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 4. Check if trait is biased\n",
"print(\"Checking for bias in trait distribution...\")\n",
"is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 5. Final validation\n",
"note = \"Dataset contains gene expression data from leukemia cell lines including Essential Thrombocythemia relevant AML subtypes.\"\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=is_biased,\n",
" df=linked_data,\n",
" note=note\n",
")\n",
"\n",
"print(f\"Dataset usability: {is_usable}\")\n",
"\n",
"# 6. 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 is not usable for trait-gene association studies due to bias or other issues.\")"
]
}
],
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|