bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Motivation:Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to theirinterpretation. The marked technical variability, high amounts of missing observations and batch effecttypical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient andunified approach for normalisation, imputation and batch effect correction.Results:Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priorsare estimated from expression values across cells using an empirical Bayes approach. We first validateour assumptions by showing this model can reproduce different statistics observed in real scRNA-seqdata. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data thatbayNorm allows robust imputation of missing values generating realistic transcript distributions that matchsingle molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNormimproves accuracy and sensitivity of differential expression analysis and reduces batch effect comparedto other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scalingnormalisation, imputation and true count recovery of gene expression measurements from scRNA-seqdata.Availability:The R package “bayNorm” is available at https://github.com/WT215/bayNorm. The code foranalysing data in this paper is available at https://github.com/WT215/bayNorm_papercode.Contact:samuel.marguerat@imperial.ac.uk or v.shahrezaei@imperial.ac.ukSupplementary information:Supplementary data are available atBioinformaticsonline.
Date Issued
2020-02-15
Date Acceptance
2019-09-27
Citation
Bioinformatics, 2020, 36 (4), pp.1174-1181
ISSN
1367-4803
Publisher
Oxford University Press (OUP)
Start Page
1174
End Page
1181
Journal / Book Title
Bioinformatics
Volume
36
Issue
4
Copyright Statement
© The Author(s) 2019. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Sponsor
The Leverhulme Trust
Engineering & Physical Science Research Council (EPSRC)
Identifier
https://academic.oup.com/bioinformatics/article/36/4/1174/5581401
Grant Number
RPG-2014-408
EP/N014529/1
Subjects
Bioinformatics
01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
Publication Status
Published
Date Publish Online
2019-10-04