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BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

Wang, Tongxin ; Johnson, Travis S ; Shao, Wei ; Lu, Zixiao ; Helm, Bryan R ; Zhang, Jie ; Huang, Kun

Genome Biology, 2019-08, Vol.20 (1), p.165-165, Article 165 [Periódico revisado por pares]

England: BioMed Central Ltd

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Título:
BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes
Autor: Wang, Tongxin ; Johnson, Travis S ; Shao, Wei ; Lu, Zixiao ; Helm, Bryan R ; Zhang, Jie ; Huang, Kun
Assuntos: Algorithms ; Autoencoder ; Batch effect ; Deep Learning ; Gene Expression Profiling - methods ; Humans ; Leukocytes, Mononuclear - metabolism ; Method ; Methods ; Novels ; Pancreas - cytology ; Pancreas - metabolism ; RNA ; RNA sequencing ; RNA-seq ; Sequence Analysis, RNA - methods ; Single cell ; Single-Cell Analysis - methods ; T-Lymphocytes - metabolism ; Technology ; Technology application ; Transcription (Genetics) ; Transfer learning
É parte de: Genome Biology, 2019-08, Vol.20 (1), p.165-165, Article 165
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Descrição: 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.
Editor: England: BioMed Central Ltd
Idioma: Inglês

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BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

Wang, Tongxin ; Johnson, Travis S ; Shao, Wei ; Lu, Zixiao ; Helm, Bryan R ; Zhang, Jie ; Huang, Kun

England: BioMed Central Ltd

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