Variance component score test for time-course gene set analysis of longitudinal RNA-seq data.

ABSTRACT: As gene expression measurement technology is shifting from microarrays to sequencing, the statistical tools available for their analysis must be adapted since RNA-seq data are measured as counts. It has been proposed to model RNA-seq counts as continuous variables using nonparametric regression to account for their inherent heteroscedasticity. In this vein, we propose tcgsaseq, a principled, model-free, and efficient method for detecting longitudinal changes in RNA-seq gene sets defined a priori. The method identifies those gene sets whose expression varies over time, based on an original variance component score test accounting for both covariates and heteroscedasticity without assuming any specific parametric distribution for the (transformed) counts. We demonstrate that despite the pres

SUBMITTER: Agniel D 

PROVIDER: S-EPMC5862256 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature