CRAN/E | DEGRE

DEGRE

Inferring Differentially Expressed Genes using Generalized Linear Mixed Models

Installation

About

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.

Key Metrics

Version 0.2.0
R ≥ 4.0
Published 2022-11-02 530 days ago
Needs compilation? no
License Artistic-2.0
CRAN checks DEGRE results

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Maintainer

Maintainer

Douglas Terra Machado

dougterra@gmail.com

Authors

Douglas Terra Machado

aut / cre

Otávio José Bernardes Brustolini

aut

Yasmmin Côrtes Martins

aut

Marco Antonio Grivet Mattoso Maia

aut

Ana Tereza Ribeiro de Vasconcelos

aut

Material

README
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Old Sources

DEGRE archive

Depends

R ≥ 4.0

Imports

utils
parglm
glmmTMB
foreach
tibble
ggplot2
ggpubr
ggrepel
car
dplyr

Suggests

testthat ≥ 3.0.0