RT Journal Article
SR Electronic
T1 A Compositional Model to Assess Expression Changes from Single-Cell Rna-Seq Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 655795
DO 10.1101/655795
A1 By Xiuyu Ma
A1 Keegan Korthauer
A1 Christina Kendziorski
A1 Michael A. Newton
YR 2019
UL http://biorxiv.org/content/early/2019/05/31/655795.abstract
AB On the problem of scoring genes for evidence of changes in the distribution of single-cell expression, we introduce an empirical Bayesian mixture approach and evaluate its operating characteristics in a range of numerical experiments. The proposed approach leverages cell-subtype structure revealed in cluster analysis in order to boost gene-level information on expression changes. Cell clustering informs gene-level analysis through a specially-constructed prior distribution over pairs of multinomial probability vectors; this prior meshes with available model-based tools that score patterns of differential expression over multiple subtypes. We derive an explicit formula for the posterior probability that a gene has the same distribution in two cellular conditions, allowing for a gene-specific mixture over subtypes in each condition. Advantage is gained by the compositional structure of the model, in which a host of gene-specific mixture components are allowed, but also in which the mixing proportions are constrained at the whole cell level. This structure leads to a novel form of information sharing through which the cell-clustering results support gene-level scoring of differential distribution. The result, according to our numerical experiments, is improved sensitivity compared to several standard approaches for detecting distributional expression changes.