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

Frontiers in Genetics
Jiankang XiongLiang Ma

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.

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Methods Mentioned

BETA
scRNA-seq
PCA
chip

Software Mentioned

scScope
DCA
SNE
scVI
ArtificialTree
PHATE
NeuralEE
Net
NeuralEEs
UMAP

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