BayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data.

ABSTRACT:

Motivation

Normalization of single-cell RNA-sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability, high amounts of missing observations and batch effect typical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient and unified approach for normalization, 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 priors are estimated from expression values across cells using an empirical Bayes approach. We first validate our assumptions by showing this model can reproduce different statistics observed in real scRNA-se

SUBMITTER: Tang W 

PROVIDER: S-EPMC7703772 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature