Inference of differentially expressed genes using generalized linear mixed models in a pairwise fashion.

ABSTRACT:

Background

Technological advances involving RNA-Seq and Bioinformatics allow quantifying the transcriptional levels of genes in cells, tissues, and cell lines, permitting the identification of Differentially Expressed Genes (DEGs). DESeq2 and edgeR are well-established computational tools used for this purpose and they are based upon generalized linear models (GLMs) that consider only fixed effects in modeling. However, the inclusion of random effects reduces the risk of missing potential DEGs that may be essential in the context of the biological phenomenon under investigation. The generalized linear mixed models (GLMM) can be used to include both effects.

Methods

We present DEGRE (Differentially Expressed Genes with Random Effects), a user-friendly tool capable of inferrin

SUBMITTER: Terra Machado D 

PROVIDER: S-EPMC10078460 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature