DrImpute: Imputing Dropout Events in Single Cell RNA Sequencing Data

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2018 Jun 10

PMID 29884114

Citations 127

Authors

Wuming Gong

Il-Youp Kwak

Pruthvi Pota

Naoko Koyano-Nakagawa

Daniel J Garry

Affiliations

Soon will be listed here.

Abstract

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 .

Citing Articles

AcImpute: a constraint-enhancing smooth-based approach for imputing single-cell RNA sequencing data.

Zhang W, Liu T, Zhang H, Li Y Bioinformatics. 2025; 41(3).

PMID: 40037523 PMC: 11890269. DOI: 10.1093/bioinformatics/btae711.

SeqBMC: Single-cell data processing using iterative block matrix completion algorithm based on matrix factorisation.

Lejun G, Like Y, Xinyi W, Shehai Z, Shuhua X IET Syst Biol. 2025; 19(1):e70003.

PMID: 39943646 PMC: 11821729. DOI: 10.1049/syb2.70003.

CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data.

Juan W, Ahn K, Chen Y, Lin C Bioengineering (Basel). 2025; 12(1).

PMID: 39851305 PMC: 11763284. DOI: 10.3390/bioengineering12010031.

Scope+: an open source generalizable architecture for single-cell RNA-seq atlases at sample and cell levels.

Yin D, Cao Y, Chen J, Mak C, Yu K, Zhang J Bioinformatics. 2024; 41(1).

PMID: 39705183 PMC: 11755096. DOI: 10.1093/bioinformatics/btae727.

A deep neural network to de-noise single-cell RNA sequencing data.

Sharifitabar M, Kazempour S, Razavian J, Sajedi S, Solhjoo S, Zare H bioRxiv. 2024; .

PMID: 39605470 PMC: 11601639. DOI: 10.1101/2024.11.20.624552.

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