PT  - JOURNAL ARTICLE
AU  - Xiangjie Li
AU  - Yafei Lyu
AU  - Jihwan Park
AU  - Jingxiao Zhang
AU  - Dwight Stambolian
AU  - Katalin Susztak
AU  - Gang Hu
AU  - Mingyao Li
TI  - Deep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysis
AID  - 10.1101/530378
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 530378
4099  - http://biorxiv.org/content/early/2019/01/25/530378.short
4100  - http://biorxiv.org/content/early/2019/01/25/530378.full
AB  - 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.