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

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

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 transf...

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Bibliographic Details
Main Authors: Tongxin Wang, Travis S. Johnson, Wei Shao, Zixiao Lu, Bryan R. Helm, Jie Zhang, Kun Huang
Format: Article
Language:English
Published: BMC 2019-08-01
Series:Genome Biology
Subjects:
Single cell
RNA-seq
Batch effect
Transfer learning
Autoencoder
Online Access:http://link.springer.com/article/10.1186/s13059-019-1764-6

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