Jan 25, 2019

Deep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysis

BioRxiv : the Preprint Server for Biology
Xiangjie LiMingyao Li

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.

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Mentioned in this Paper

Single-Cell Analysis
Sequence Determinations, RNA
Sequence Determinations
Gene Expression
Embedding
Sequence Analysis, RNA
Learning
Computed (Procedure)
Cell Cluster

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