NDRindex: A method for the quality assessment of single-cell RNA-Seq preprocessing data

Ruiyu Xiao, Guoshan Lu, Shuilin Jin

Abstract: Background: Single-cell RNA sequencing can be used to determine cell types in an unbiased way. Normally, the analysis pipeline of single-cell 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 scRNA-Seq 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 single-cell RNA-seq 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 single-cell 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 RNA-Seq data assessment.