A Global Similarity Learning for Clustering of SingleCell RNASeq Data Singlecell RNAseq (scRNAseq) data analysis is a powerful tool for biological researches. Similarity plays an important role in clustering scRNAseq data. Existing similarity measurements are mainly based on local distance information that is calculated between directly connected node pairs, or shared nearest neighboursu0027 information, without considering the global information. Therefore, these similarity measurements may be not very accurate based on the insufficient information. Based on multikernel indices in a global feature space and pathbased similarity, we proposed a new similarity measurement for singlecell clustering, called multikernel and pathbased 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 multikernel. A pathbased 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 scRNAseq data, named MPGSLouvain. 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 MPGSLouvain, we compared MPGSLouvain and five scRNAseq clustering methods on twenty scRNAseq datasets. The experimental results showed that MPGS outperformed other similarity measurements, and MPGSLouvain achieved better performance on these datasets. It can be observed that MPGS provided a new insight to improve the accuracy of clustering scRNAseq data by considering the global information in similarity measurement. MPGSLouvain automatically detected clusters accurately without prior knowledge.

Detection of Cell Types from Singlecell RNAseq Data using Similarity via Kernel Preserving Learning Embedding The recent advances in singlecell sequencing techniques allow us to study biological issues on cell levels. Detecting cell types from scRNAseq data analysis is important and meaningful. However, highlevel noise and the nonlinearity and sparsity of scRNAseq data are great challenges. In this paper, we propose a celltype detection algorithm preserving the overall cell relations named POCR to analyze scRNAseq data. POCR utilizes a kernel embedding similarity measure to calculate celltocell similarity, by minimizing the reconstruction error of a kernel matrix, rather than the reconstruction error of the original data adopted by other similarity metrics. According to the scale of scRNAseq datasets, we select Gaussian kernel or linear kernel to calculate the embedding. We then adopt spectral clustering to detect the cell types based on the learned celltocell similarity. The results are further visualized to demonstrate the effectiveness of the celltype detection algorithm POCR. Further analysis shows that the learned similarity could improve the clustering and visualization of cell types in scRNAseq data. Our proposed algorithm is compared with five other stateoftheart cell subtype detection methods. The effectiveness of the algorithms is evaluated by two criteria: ARI and NMI. The experiments show that POCR achieves accurate and robust performance across different scRNAseq data. Our python implementation of POCR is available at https:github.comZeMingLiuPOCR.