Abstract
BackgroundSingle-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.ResultsWe 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.ConclusionsSCC is an effective tool to resolve the dropout noise in scRNA-seq data. The code is freely accessible at https://github.com/nwpuzhengyan/SCC.
Highlights
Single-cell RNA sequencing enables the possibility of many in-depth transcriptomic analyses at a single-cell resolution
The result shows that : scRNA-seq complementation (SCC) can significantly reduce the intra-class distance of cells and enhance the clustering of cell subpopulation
We can conclude that SCC is an effective method to recover gene expression
Summary
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. Results: We propose a new method SCC to impute the dropouts of scRNA-seq data. Conclusions: SCC is an effective tool to resolve the dropout noise in scRNA-seq data. Methods based on bulk RNA-seq can obtain the genome-wide RNA sequence expression information, the resulting gene expression profiles are only the average values of the different cell types, which cause the studies of gene expression limited to the analysis of pooled populations of cells. Technology that define the gene expression of individual cells is necessary for a better understand of the differentiation and heterogeneity of cells
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