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DEGRE
View on CRAN: Click
here
Download and install DEGRE package within the R console
Install from CRAN:
install.packages("DEGRE")
Install from Github:
library("remotes")
install_github("cran/DEGRE") Install by package version:
library("remotes")
install_version("DEGRE", "0.2.0") Attach the package and use:
library("DEGRE")
Maintained by
Douglas Terra Machado
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-11-01
Latest Update: 2022-11-02
Description:
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)' 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.
How to cite:
Douglas Terra Machado (2022). DEGRE: Inferring Differentially Expressed Genes using Generalized Linear Mixed Models. R package version 0.2.0, https://cran.r-project.org/web/packages/DEGRE. Accessed 28 Oct. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for DEGRE
Functions, R codes and Examples using
the DEGRE R package
Some associated functions: BarGraphDEGRE . DEGRE . DEGRE_package . VolcanoDEGRE .
Some associated R codes: BarGraphDEGRE.R . DEGRE.R . DEGRE_package.R . VolcanoDEGRE.R . Full DEGRE package functions and examples
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