Package: DEGRE 0.2.0

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.

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]

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Exports:BarGraphDEGREDEGREVolcanoDEGRE

Dependencies:abindbackportsbootbroomcarcarDataclicodetoolscolorspacecorrplotcowplotcpp11DerivdoBydplyrfansifarverforeachgenericsggplot2ggpubrggrepelggsciggsignifglmmTMBgluegridExtragtableisobanditeratorslabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivparglmpbkrtestpillarpkgconfigpolynompurrrquantregR6RColorBrewerRcppRcppArmadilloRcppEigenrlangrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselectTMButf8vctrsviridisLitewithr