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

Xiangjie Li, Kui Wang, Yafei Lyu, Huize Pan, Jingxiao Zhang, Dwight Stambolian, Katalin Susztak, Muredach P. Reilly, Gang Hu () and Mingyao Li ()
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Xiangjie Li: University of Pennsylvania
Kui Wang: University of Pennsylvania
Yafei Lyu: University of Pennsylvania
Huize Pan: Columbia University Medical Center
Jingxiao Zhang: Renmin University of China
Dwight Stambolian: University of Pennsylvania
Katalin Susztak: University of Pennsylvania
Muredach P. Reilly: Columbia University Medical Center
Gang Hu: University of Pennsylvania
Mingyao Li: University of Pennsylvania

Nature Communications, 2020, vol. 11, issue 1, 1-14

Abstract: Abstract Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function. Through iterative self-learning, DESC gradually removes batch effects, as long as technical differences across batches are smaller than true biological variations. As a soft clustering algorithm, cluster assignment probabilities from DESC are biologically interpretable and can reveal both discrete and pseudotemporal structure of cells. Comprehensive evaluations show that DESC offers a proper balance of clustering accuracy and stability, has a small footprint on memory, does not explicitly require batch information for batch effect removal, and can utilize GPU when available. As the scale of single-cell studies continues to grow, we believe DESC will offer a valuable tool for biomedical researchers to disentangle complex cellular heterogeneity.

Date: 2020
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DOI: 10.1038/s41467-020-15851-3

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