DrImpute: imputing dropout events in single cell RNA sequencing data

By Il-Youp Kwak, Wuming Gong, Naoko Koyano-Nakagawa, Daniel J. Garry. BMC Bioinformatics, 2017

298 citations recorded by the source corpus.

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. 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. 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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0db80d6ea0f828aa9637d6ff5a49999b4b5ca4d9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Tieliu Shi
2. Baitang Ning
3. Valentine Svensson
4. Geng Chen

In Molecular Biology

1. Tieliu Shi
2. Baitang Ning
3. Valentine Svensson
4. Geng Chen
5. Rahul Satija
6. Saket Kumar Choudhary

Citations in the corpus

• Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “Several imputation methods have been recently developed for scRNA-seq, including SAVER (Huang et al., 2018), MAGIC (van Dijk et al., 2018), ScImpute (Li and Li, 2018), DrImpute (Gong et al., 2018), and AutoImpute (Talwar et al., 2018).”
• Droplet scRNA-seq is not zero-inflated (2019, background): “…zeros in data, with the aim of letting a user predict what the expression level of a gene in a cell would have been, had there been no zero-inflation or dropouts (Azizi et al., 2017; van Dijk et al., 2018; Gong et al., 2018; Huang et al., 2018; Li and Li, 2018; Tang et al., 2018; Zhu et al., 2016).”