A Global Similarity Learning for Clustering of Single-Cell RNA-Seq Data

Single-cell RNA-seq (scRNA-seq) data analysis is a powerful tool for biological researches. Similarity plays an important role in clustering scRNA-seq data. Existing similarity measurements are mainly based on local distance information that is calculated between directly connected node pairs, or shared nearest neighbours' information, without considering the global information. Therefore, these similarity measurements may be not very accurate based on the insufficient information. Based on multi-kernel indices in a global feature space and path-based similarity, we proposed a new similarity measurement for single-cell clustering, called multi-kernel and path-based global similarity (MPGS). In MPGS, global information was incorporated by a new feature space from Spearman correlation coefficient, and a global similarity matrix calculated by multi-kernel. A path-based similarity metric was designed to expand the relevant node range. Based on this similaritiy, a modified Louvain community detection method was applied to cluster the scRNA-seq data, named MPGS-Louvain. To validate the performance of MPGS, the clustering performances of several clustering methods combined with different similarity measurements were compared. To demonstrate the performance of MPGS-Louvain, we compared MPGS-Louvain and five scRNA-seq clustering methods on twenty scRNA-seq datasets. The experimental results showed that MPGS outperformed other similarity measurements, and MPGS-Louvain achieved better performance on these datasets. It can be observed that MPGS provided a new insight to improve the accuracy of clustering scRNA-seq data by considering the global information in similarity measurement. MPGS-Louvain automatically detected clusters accurately without prior knowledge.