ArticleGenome biology2023
GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Immune-stromal dysregulation and senescence in chronic spontaneous urticaria.The journal of allergy and clinical immunology. Global · 2026Article
- scENature communications · 2026Article
- Multimodal data analysis reveals asynchronous aging dynamics across female reproductive organs.Nature aging · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- SpaTM: topic models for inferring spatially informed transcriptional programs.Briefings in bioinformatics · 2025Article
- Identification of biomarkers associated with exhausted CD8 + T cells in the tumor microenvironment of intrahepatic cholangiocarcinoma based on Mendelian randomization and bioinformatics analysis.Discover oncology · 2025Article
- EMixed: Probabilistic Multi-Omics Cellular Deconvolution of Bulk Omics Data.Journal of data science : JDS · 2025Article
- A deconvolution framework that uses single-cell sequencing plus a small benchmark data set for accurate analysis of cell type ratios in complex tissue samples.Genome research · 2025Article
- SMART: spatial transcriptomics deconvolution using marker-gene-assisted topic model.Genome biology · 2024Article
- Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.Nature neuroscience · 2024Review
- GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution.Briefings in bioinformatics · 2024Article
- GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes.Genome biology · 2023Article
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3 authors.
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Abstract
Cell-type composition is an important indicator of health. We present Guided Topic Model for deconvolution (GTM-decon) to automatically infer cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in deconvolving disease transcriptomes, GTM-decon can infer multiple cell-type-specific gene topic distributions per cell type, which captures sub-cell-type variations. GTM-decon can also use phenotype labels from single-cell or bulk data to infer phenotype-specific gene distributions. In a nested-guided design, GTM-decon identified cell-type-specific differentially expressed genes from bulk breast cancer transcriptomes.
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