NanoStringDiff (version 1.2.0)

glm.LRT: perform gene-wise likelihood ratio test for NanoString Data

Description

The method considers a generalized linear model of the negative binomial family to characterize count data and allows for multi-factor design. The method propose an empirical Bayes shrinkage approach to estimate the dispersion parameter and use likelihood ratio test to obtain p-value.

Usage

glm.LRT(NanoStringData,design.full,Beta=ncol(design.full), contrast=NULL)

Arguments

NanoStringData

An object of "NanoStringSet" class.

design.full

numeric matrix giving the design matrix for the generalized linear models under full model. must be of full column rank.

Beta

integer or character vector indicating which coefficients of the linear model are to be tested equal to zero. Values must be columns or column names of design. Defaults to the last coefficient. Ignored if contrast is specified.

contrast

numeric vector or matrix specifying one or more contrasts of the linear model coefficients to be tested equal to zero.

Value

A list

table

A data frame with each row corresponding to a gene. Rows are sorted according to likelihood ratio test statistics. The columns are: logFC: log fold change between two groups. lr: likelihood ratio test statictics. pvalue: p-value. qvalue: adjust p-value using the procedure of Benjamini and Hochberg.

dispersion

a vertor of dispersion

log.dispersion

a vector of log dispersion: log.dispersion=log(dispersion)

design.full

numeric matrix giving the design matrix under full generalizedlinear model.

design.reduce

numeric matrix giving the design matrix under reduced generalizedlinear model.

Beta.full

coefficients under full model.

mean.full

mean value under full model.

Beta.reduce

coefficients under reduced model.

mean.reduce

mean value under reduced model.

m0

hyper-parameter: mean value of the prior distribution of log dispersion

sigma

hyper-parameter: standard deviation of the prior distribution of log dispersion

Examples

data(NanoStringData)
NanoStringData=estNormalizationFactors(NanoStringData)
group=pData(NanoStringData)
design.full=model.matrix(~0+factor(group$group))
contrast=c(1,-1)
result=glm.LRT(NanoStringData,design.full,
               Beta=ncol(design.full),contrast=contrast)
head(result$table)