Negative binomial additive model for RNA-Seq data analysis.

ABSTRACT:

Background

High-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. Existing models assume linear effect of covariates, which is restrictive and may not be sufficient for certain phenotypes.

Results

We introduce NBAMSeq, a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. Specifically, we model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance i

SUBMITTER: Ren X 

PROVIDER: S-EPMC7195715 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature