Background: The single cell RNA sequencing (scRNA-seq) technique begin a new era by allowing the observation of gene expression at the single cell level. However, there is also a large amount of technical and biological noise. Because of the low number of RNA transcriptomes and the stochastic nature of the gene expression pattern, there is a high chance of missing nonzero entries as zero, which are called dropout events. Results: We develop DrImpute to impute dropout events in scRNA-seq data. We show that DrImpute has significantly better performance on the separation of the dropout zeros from true zeros than existing imputation algorithms. We also demonstrate that DrImpute can significantly improve the performance of existing tools for clustering, visualization and lineage reconstruction of nine published scRNA-seq datasets. Conclusions: DrImpute can serve as a very useful addition to the currently existing statistical tools for single cell RNA-seq analysis. DrImpute is implemented in R and is available at https://github.com/gongx030/DrImpute.
Bibliographical noteFunding Information:
We acknowledge the support from the University of Minnesota Supercomputing Institute.
Funding support was obtained from the National Institutes of Health (R01HL122576), Minnesota Regenerative Medicine and the Department of Defense (GRANT11763537). These funding sources equally supported the design of the study, data collection, analysis and interpretation of the data.
- Dropout events
- Next generation sequencing
- Single cell RNA sequencing data