ArticleGenome biology2019
BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
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Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- ContiTE: continuous manifold MoE for few-shot cross-tissue mRNA translation efficiency prediction.Briefings in bioinformatics · 2026Article
- Cross-stage single-cell and spatial metabolome analyses reveal periderm specialization and tanshinone biosynthesis in Salvia miltiorrhiza roots.The New phytologist · 2026Article
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder.Nature communications · 2026Article
- USADAE: a deep learning approach to disentangle hidden covariates in RNA-seq data.Briefings in bioinformatics · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning.eLife · 2025Article
- Article
- RGCN-BA: relational graph convolutional network with batch awareness for single-cell RNA sequencing clustering.Briefings in bioinformatics · 2025Article
- Out of distribution learning in bioinformatics: advancements and challenges.Briefings in bioinformatics · 2025Review
- Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives.Molecular medicine (Cambridge, Mass.) · 2025Review
- CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data.Cell genomics · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Article
- Thinking points for effective batch correction on biomedical data.Briefings in bioinformatics · 2024Article
- A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
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Abstract
To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for identifying cell lineages and bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch Effect ReMoval Using Deep Autoencoders), a novel transfer-learning-based method for batch effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate that BERMUDA outperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.
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Registered trials
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 OpenQuestion graph.