Nature CommunicationsGenomicsPeer-reviewed journal article
Deep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysis
Xiangjie LiiD, Kui WangiD, Yafei LyuiD, Huize PaniD, Jingxiao ZhangiD, Dwight E. Stambolian, Katalin SusztákiD, Muredach P. ReillyiD, Gang HuiD, Mingyao LiiD
Abstract
Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells imposes computational challenges. We present an unsupervised deep embedding algorithm for single-cell clustering (DESC) that iteratively learns cluster-specific gene expression signatures and cluster assignment. DESC significantly improves clustering accuracy across various datasets and is capable of removing complex batch effects while maintaining true biological variations.
- assay: scRNA-seq
- research focus: clustering
- research focus: batch correction / integration
- approach: deep learning
- Citations
- 415
- FWCI
- 17.02
- Field percentile
- top 1%
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