Gene expression distribution deconvolution in single-cell RNA sequencing

Abstract

Single-cell RNA sequencing (scRNA-seq) enables the quantifica tion of each gene’s expression distribution across cells, thu allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These sta tistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low cov erage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a reexamina tion of nine public datasets, we propose a simple technical noise model for scRNA-seq data with unique molecular identifiers (UMI) We develop deconvolution of single-cell expression distribution (DESCEND), a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, lead ing to improved estimates of properties of the distribution such as dispersion and nonzero fraction. DESCEND can adjust for cell level covariates such as cell size, cell cycle, and batch effects DESCEND’s noise model and estimation accuracy are further eval uated through comparisons to RNA FISH data, through data split ting and simulations and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially expressed genes, identifying cell types, and selecting differentia tion markers.