DEGRE: Inferring Differentially Expressed Genes using Generalized Linear Mixed Models

Genes that are differentially expressed between two or more experimental conditions can be detected in RNA-Seq. A high biological variability may impact the discovery of these genes once it may be divergent between the fixed effects. However, this variability can be covered by the random effects. 'DEGRE' was designed to identify the differentially expressed genes considering fixed and random effects on individuals. These effects are identified earlier in the experimental design matrix. 'DEGRE' has the implementation of preprocessing procedures to clean the near zero gene reads in the count matrix, normalize by 'RLE' published in the 'DESeq2' package, 'Love et al. (2014)' <doi:10.1186/s13059-014-0550-8> and it fits a regression for each gene using the Generalized Linear Mixed Model with the negative binomial distribution, followed by a Wald test to assess the regression coefficients.

Version: 0.2.0
Depends: R (≥ 4.0)
Imports: utils, parglm, glmmTMB, foreach, tibble, ggplot2, ggpubr, ggrepel, car, dplyr
Suggests: testthat (≥ 3.0.0)
Published: 2022-11-02
DOI: 10.32614/CRAN.package.DEGRE
Author: Douglas Terra Machado ORCID iD [aut, cre], Otávio José Bernardes Brustolini ORCID iD [aut], Yasmmin Côrtes Martins ORCID iD [aut], Marco Antonio Grivet Mattoso Maia ORCID iD [aut], Ana Tereza Ribeiro de Vasconcelos ORCID iD [aut]
Maintainer: Douglas Terra Machado <dougterra at>
License: Artistic-2.0
NeedsCompilation: no
Materials: README
CRAN checks: DEGRE results


Reference manual: DEGRE.pdf


Package source: DEGRE_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): DEGRE_0.2.0.tgz, r-oldrel (arm64): DEGRE_0.2.0.tgz, r-release (x86_64): DEGRE_0.2.0.tgz, r-oldrel (x86_64): DEGRE_0.2.0.tgz
Old sources: DEGRE archive


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