Locality Sensitive Imputation for Single Cell RNA-Seq Data

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
Marmar Moussa, Ion I Măndoiu

Abstract

One of the most notable challenges in single cell RNA-Seq data analysis is the so called drop-out effect, where only a fraction of the transcriptome of each cell is captured. The random nature of dropouts, however, makes it possible to consider imputation methods as means of correcting for dropouts. In this article, we study some existing single cell RNA sequencing (scRNA-Seq) imputation methods and propose a novel iterative imputation approach based on efficiently computing highly similar cells. We then present the results of a comprehensive assessment of existing and proposed methods on real scRNA-Seq data sets with varying per cell sequencing depth.

References

Jun 8, 2001·Bioinformatics·O TroyanskayaR B Altman
May 11, 2011·BMC Proceedings·Chih LeeCraig E Nelson
Sep 24, 2014·The Journal of Experimental Medicine·Fei DuanPramod K Srivastava

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Citations

May 5, 2020·Bioinformatics·Di RanLingling An
Feb 9, 2020·Genome Biology·David LähnemannAlexander Schönhuth
Oct 2, 2020·Briefings in Bioinformatics·Lucrezia PatrunoAlex Graudenzi
Jun 12, 2021·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Marmar Moussa, Ion I Măndoiu
Jan 23, 2022·Genome Biology·Ruochen JiangJingyi Jessica Li

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Methods Mentioned

BETA
scRNA-Seq
RNA-Seq

Software Mentioned

R package LSHR
scImpute
DeclareMathSizes
LSImpute
DrImpute
KNNImpute
BISCUIT
R
IsoEM2
LSImputeMed

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