NeuralEE: A GPU-Accelerated Elastic Embedding Dimensionality Reduction Method for Visualizing Large-Scale scRNA-Seq Data

Jiankang Xiong; Fuzhou Gong; Lin Wan; Liang Ma

doi:10.3389/fgene.2020.00786

NeuralEE: A GPU-Accelerated Elastic Embedding Dimensionality Reduction Method for Visualizing Large-Scale scRNA-Seq Data

Front Genet. 2020 Oct 6:11:786. doi: 10.3389/fgene.2020.00786. eCollection 2020.

Authors

Jiankang Xiong^{1

2}, Fuzhou Gong^{1

2}, Lin Wan^{1

2}, Liang Ma³

Affiliations

¹ National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), Beijing, China.
² School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China.
³ Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, China.

Abstract

The dramatic increase in amount and size of single-cell RNA sequencing data calls for more efficient and scalable dimensional reduction and visualization tools. Here, we design a GPU-accelerated method, NeuralEE, which aggregates the advantages of elastic embedding and neural network. We show that NeuralEE is both scalable and generalizable in dimensional reduction and visualization of large-scale scRNA-seq data. In addition, the GPU-based implementation of NeuralEE makes it applicable to limited computational resources while maintains high performance, as it takes only half an hour to visualize 1.3 million mice brain cells, and NeuralEE has generalizability for integrating newly generated data.

Keywords: elastic embedding; generalizable models; large-scale; neural networks; parametric models; single-cell RNA sequencing; stochastic optimization.