NDRindex: A method for the quality assessment of singlecell RNASeq preprocessing data Background: Singlecell RNA sequencing can be used to determine cell types in an unbiased way. Normally, the analysis pipeline of singlecell RNA data includes data n ormalization, dimension reduction and unsupervised clustering. However, different normalization and dimension reduction methods will influence the results of clustering and cell type enrichment analysis significantly. Choices of preprocessing paths is crucial in scRNASeq data mining because an appropriate preprocessing path can extract more important information from complex raw data and lead to a more accurate clustering result. Results: We propose a method called NDRindex(Normalization and Dimensionality Reduction index) to evaluate singlecell RNAseq data quality. The method includes a function that calculates the degree of aggregation of data, which is the key to benchmarking data quality before clustering. For five singlecell RNA sequencing data sets we tested, the result shows the effectiveness and the accuracy of our index. Conclusions: This method we introduce focuses on filling the blanks in the selection of preprocessing paths and the result proves its effectiveness and accuracy. Our study provides a useful indicator for RNASeq data assessment.

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.