2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions-Reference-Cited by-同舟云学术

2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions

Published:2020-02-28 Issue:11 Volume:36 Page:3588-3589
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Zhu Kaiyi¹²^ORCID,Anastassiou Dimitris¹²³

Affiliation:

1. Department of Systems Biology, Columbia University, New York, NY 10032, USA

2. Department of Electrical Engineering, New York, NY 10027, USA

3. Data Science Institute, Columbia University, New York, NY 10027, USA

Abstract

Abstract Summary We developed 2DImpute, an imputation method for correcting false zeros (known as dropouts) in single-cell RNA-sequencing (scRNA-seq) data. It features preventing excessive correction by predicting the false zeros and imputing their values by making use of the interrelationships between both genes and cells in the expression matrix. We showed that 2DImpute outperforms several leading imputation methods by applying it on datasets from various scRNA-seq protocols. Availability and implementation The R package of 2DImpute is freely available at GitHub (https://github.com/zky0708/2DImpute). Contact d.anastassiou@columbia.edu Supplementary information Supplementary data are available at Bioinformatics online.

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa148/33044452/btaa148.pdf

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