nbGLMdir {SeqMADE}

Identify Differential Expression Modules Based on the GLM Model with Up or Down-regulated Change

Description

The algorithm identify differential expression modules using Generalized Linear Model (GLM) for differential expression analysis in RNA-Seq data, and in the model three indicator variables Group, Module and Direction are adopted to fit the GLM.

Usage

nbGLMdir(factors, N, networkModule, modulematrix, distribution = c("poisson", "NB")[1])

Arguments

factors

Factors with three variables including Count, Group, Direction.

N

The total sample size.

networkModule

NetworkModule is the gene sets or modules in the biological network or metabolic pathway, with the 1th column as the module names and the 2th columnn as the gene symbol constituting the module.

modulematrix

Modulematrix is a matrix, in which the indicator variables 1 or 0 represent whether a gene belong to a given module or not.

distribution

a character string indicating the distribution of RNA-Seq count value, default is 'NB'.

Details

The GLM method was determined by the distribution of RNA-Seq count value, such as poisson or negative binomial, and there are three indicator variables Group, Module and Direction. Module=1 when a gene belongs to the module and Module= 0 otherwise; Group=1 for case values and Group=0 for control values; Direction=1 for up-regulated and Direction=-1 for down-regualted. Group * Module * Direction represents the interaction effects between Group, Module and Direction.

Value

The nominal pvalue and FDR for the significance of each gene set or module.

Author(s)

Mingli Lei, Li-Ching Huang

See Also

glm()

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)
Result <- nbGLMdir(factors, 14, networkModule, modulematrix,distribution="NB")