SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model.

ABSTRACT

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) enables the possibility of many in-depth transcriptomic analyses at a single-cell resolution. It's already widely used for exploring the dynamic development process of life, studying the gene regulation mechanism, and discovering new cell types. However, the low RNA capture rate, which cause highly sparse expression with dropout, makes it difficult to do downstream analyses. RESULTS: We propose a new method SCC to impute the dropouts of scRNA-seq data. Experiment results show that SCC gives competitive results compared to two existing methods while showing superiority in reducing the intra-class distance of cells and improving the clustering accuracy in both simulation and real data. CONCLUSIONS: SCC is an effective tool to resolve the dropout noise in scRNA-seq data. The code is freely accessible at https//github.com/nwpuzhengyan/SCC .