ArticleGenome biology2024
DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Synchronization of the 12-h circatidal rhythm maintains the kidney through repeated cycles of warm reperfusion in the hibernating ground squirrel.Function (Oxford, England) · 2026Article
- High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples.Nature biotechnology · 2026Article
- Article
- Evaluating reference-mixture matching in cell-type deconvolution with single-cell RNA-seq references.Briefings in bioinformatics · 2026Article
- MOADE: a multimodal autoencoder for dissociating bulk multi-omics data.Genome biology · 2025Article
- An interpretable and adaptive autoencoder for efficient tissue deconvolution.Nucleic acids research · 2025Article
- Direct single cell-type gene expression analysis in peripheral blood: novel ratio-based gene expression biomarkers using 2 novel monocyte reference genes (PSAP and CTSS) for detection of bacterial infection.Human molecular genetics · 2025Article
- DM_CorrMatch: a semi-supervised semantic segmentation framework for rapeseed flower coverage estimation using UAV imagery.Plant methods · 2025Article
- Deep learning based deconvolution methods: A systematic review.Computational and structural biotechnology journal · 2025Review
- Immune profiling-based targeting of pathogenic T cells with ustekinumab in ANCA-associated glomerulonephritis.Nature communications · 2024Article
- DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation.Genome biology · 2024Article
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3 authors.
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Abstract
Cell deconvolution is the estimation of cell type fractions and cell type-specific gene expression from mixed data. An unmet challenge in cell deconvolution is the scarcity of realistic training data and the domain shift often observed in synthetic training data. Here, we show that two novel deep neural networks with simultaneous consistency regularization of the target and training domains significantly improve deconvolution performance. Our algorithm, DISSECT, outperforms competing algorithms in cell fraction and gene expression estimation by up to 14 percentage points. DISSECT can be easily adapted to other biomedical data types, as exemplified by our proteomic deconvolution experiments.
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