scRMD: imputation for single cell RNA-seq data via robust matrix decomposition

Chong Chen, Changjing Wu, Linjie Wu, Xiaochen Wang, Minghua Deng, Ruibin Xi

Abstract: Single cell RNA-sequencing (scRNA-seq) technology enables whole transcriptome profiling at single cell resolution and holds great promises in many biological and medical applications. Nevertheless, scRNA-seq often fails to capture expressed genes, leading to the prominent dropout problem. These dropouts cause many problems in down-stream analysis, such as significant increase of noises, power loss in differential expression analysis and obscuring of gene-to-gene or cell-to-cell relationship. Imputation of these dropout values can be beneficial in scRNA-seq data analysis.