A Single-Cell-Resolution Quantitative Metric of Similarity to a Target Cell Type for scRNA-seq Data

Abstract

Empowered by advances in single-cell RNA sequencing techniques (scRNA-seq), discovering new cell types or new subsets of a cell type has become an increasingly popular research interest. This type of study, by nature, requires assessment of similarity between cell groups. However, so far there is no quantitative metric for accurate and objective evaluation of such similarity; while current practice suffers from quite a few challenges including subjectivity. In this work, we propose a novel quantitative metric of single-cell-to-target-cell-type similarity, on the basis of scRNA-seq data and the signatures or differentially expressed gene (DEG) list of the target cell type. The proposed similarity score, TySim, evaluates the statistical significance of joint differential expression of the given DEGs in the cell to be tested. For this statistical test, the null distribution is established upon full consideration of complex factors causing heterogeneous sequencing efficiency of genes/cells. The design of TySim avoids the needs for clustering and for batch effect removal on cross-platform data, detouring the accompanying risks and burdens. Being the first quantitative metric of similarity to target cell type at a single-cell resolution, TySim has the potential to facilitate and enable a variety of biological studies. We validated the effectiveness of TySim and explored the possible directions of application through three example study cases of real datasets. Experimental results demonstrate TySim’s superior performance and great potential in making contributions to biological studies.