bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data

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.