Clustering scRNA-seq data via qualitative and quantitative analysis

Abstract

Single-cell RNA sequencing (scRNA-seq) technologies have been driving the development of algorithms of clustering heterogeneous cells. We introduce a novel clustering algorithm scQA, which can effectively and efficiently recognize different cell types via qualitative and quantitative analysis. It iteratively extracts quasi-trend-preserved genes to conform a consensus by representing expression patterns with dropouts qualitatively and quantitatively, and, then automatically clusters cells using a new label propagation strategy without specifying the number of cell types in advance. Validated on 20 public scRNA-seq datasets, scQA consistently outperformed 9 salient tools in both accuracy and efficiency across 16 out of 20 datasets tested, and ranked top 2 or 3 across the other 4 datasets. Furthermore, we demonstrate scQA can extract informative genes in both perspectives of biology and data wise by performing consensus, allowing genes used for landmark construction multiple characteristics, which is essential for clustering cells accurately. Overall, scQA could be a useful tool for discovery of cell types that can be integrated into general scRNA-seq analyses.