The single cell RNA sequencing (scRNA-seq) technique began 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. However, many statistical methods used for analyzing scRNA-seq data in cell type identification, visualization, and lineage reconstruction do not model for dropout events. Researchers from the University of Minnesota have developed DrImpute to impute dropout events, and it improves many of the statistical tools used for scRNA-seq analysis that do not account for dropout events. Our numerical studies with real data demonstrate the promising performance of the proposed method, which has been implemented in R.
DrImpute pipeline for imputing single cell RNA sequencing data
Overview of DrImpute pipeline: (1) perform data cleansing, normalizing, and log transformation; (2) calculate the distance matrix among cells; (3) impute the dropout entries based on the clustering results; and (4) average all imputation results to determine the final imputation.
Availability – DrImpute was implemented as an R package, and it is available on CRAN: https://cran.r-project.org/web/packages/DrImpute/index.html