Automatically detecting anchor cells and clustering for scRNA-seq data using scTSNN.

Abstract

Advancing in single-cell RNA sequencing techniques enhances the resolution of cell heterogeneity study. Density-based unsupervised clustering has the potential to detect the representative anchor points and the number of clusters automatically. Meanwhile, discovering the true cell type of scRNA-seq data in the unsupervised scenario is still challenging. To this end, we proposed a tensor shared nearest neighbor anchor clustering for scRNA-seq data, named scTSNN, which first makes use of the tensor affinity learning module to mine the local-global balanced topological structures among cells, next designs density-based shared nearest neighbor measurement method to automatically detect anchor cells, finally partitions the non-anchor cells to obtain the clustering results. Validated on synthetic datasets and scRNA-seq datasets, scTSNN not only exactly detects the complicated structures but also has better performance in accuracy and robustness compared with the state-of-the-art methods. Moreover, case studies on mammalian cells and cervical cancer tumor cells demonstrate the selected anchor cells of scTSNN benefit the cell pseudotime inference and rare cell identification, which show good application and research value of scTSNN.