ArticleGenome biology2023
DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection.
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 10 papers.
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Who cites it
10 citing papers in PubMed, 25 citations in OpenAlex.
- Immunological maladaptation preceding spontaneous preterm birth in human pregnancies.Nature communications · 2026Article
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- Cytotoxic CD4Signal transduction and targeted therapy · 2026Review
- Refining the resolution of the yeast genotype-phenotype map using single-cell RNA-sequencing.eLife · 2025Article
- FedscGen: privacy-preserving federated batch effect correction of single-cell RNA sequencing data.Genome biology · 2025Article
- Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow.F1000Research · 2025Review
- scTCA: a hybrid Transformer-CNN architecture for imputation and denoising of scDNA-seq data.Briefings in bioinformatics · 2024Article
- Review
- DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation.Genome biology · 2024Article
- DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection.Genome biology · 2023Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSingle-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification.
resultsHere, we present DISCERN, a novel deep generative network that precisely reconstructs missing single-cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We show that DISCERN is robust against differences between batches and is able to keep biological differences between batches, which is a common problem for imputation and batch correction algorithms. We use DISCERN to detect two unseen COVID-19-associated T cell types, cytotoxic CD4
conclusionsThus, DISCERN is a flexible tool for reconstructing missing single-cell gene expression using a reference dataset and can easily be applied to a variety of data sets yielding novel insights, e.g., into disease mechanisms.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.