SeqMADE-package: Network Module-Based Model in the Differential Expression...

In SeqMADE: Network Module-Based Model in the Differential Expression Analysis for RNA-Seq

Description

A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.

Details

Package:	SeqMADE
Type:	Package
Version:	1.0
Date:	2016-06-27
License:	GPL (>2)
LazyLoad:	yes

The main functions in this package are Factor A function of constructing the Group variables, Direction variables, and the Count variables, moduleMatrix a function of constructing the modulematrix for all the modules, nbGLM Identify differential expression modules based on the GLM method using Group and Module variables, nbGLMdir Identify differential expression modules based on the Generalized Linear Model(GLM) using Group, Module and Direction variables, and nbGLMdirperm Identify differential expression modules based on the GLM method by shuffling the phenotypic variables.

Author(s)

Mingli Lei, Jia Xu, Li-Ching Huang, Lily Wang, Jing Li Maintainer: Mingli Lei<[e-mail]>

References

Xu, J., Wang, L. and Li, J. (2014) Biological network module-based model for the analysis of differential expression in shotgun proteomics, J Proteome Res, 13, 5743-5750.

See Also

glm(),lm()

Examples

data(exprs)
data(networkModule)
case <- c("A1","A2","A3","A4","A5","A6","A7")
control <- c("B1","B2","B3","B4","B5","B6","B7")
factors <- Factor(exprs,case,control)
modulematrix <- moduleMatrix(exprs,networkModule)
Result1<- nbGLM(factors,14,networkModule,modulematrix,distribution="NB")
Result2<- nbGLMdir(factors,14,networkModule,modulematrix,distribution="NB")
Result3<- nbGLMdirperm(exprs,case,control,factors,networkModule,
                       modulematrix,10,distribution="NB")

Example output

Loading required package: MASS
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SeqMADE documentation built on May 1, 2019, 10:20 p.m.