Single-cell RNA sequencing data analysis based on non-uniform ε-neighborhood network

Abstract

Motivation: Single-cell RNA sequencing (scRNA-seq) technology provides the possibility to study cell heterogeneity and cell development on the resolution of individual cells. Arguably, three of the most important computational targets on scRNA-seq data analysis are data visualization, cell clustering and trajectory inference. Although a substantial number of algorithms have been developed, most of them do not treat the three targets in a systematic or consistent manner.

Results: In this article, we propose an efficient scRNA-seq analysis framework, which accomplishes the three targets consistently by non-uniform ε-neighborhood (NEN) network. First, a network is generated by our NEN method, which combines the advantages of both k-nearest neighbors (KNN) and ε-neighborhood (EN) to represent the manifold that data points reside in gene space. Then from such a network, we use its layout, its community and further its shortest path to achieve the purpose of scRNA-seq data visualization, clustering and trajectory inference. The results on both synthetic and real datasets indicate that our NEN method not only can visually provide the global topological structure of a dataset accurately compared with t-SNE (t-Distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection), but also has superior performances on clustering and pseudotime ordering of cells over the existing approaches.

Availability and implementation: This analysis method has been made into a python package called ccnet and is freely available at https://github.com/Just-Jia/ccNet.

Supplementary information: Supplementary data are available at Bioinformatics online.