glm.LRT: perform gene-wise likelihood ratio test for NanoString Data
In NanoStringDiff: Differential Expression Analysis of NanoString nCounter Data

Description Usage Arguments Value Author(s) Examples

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.

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

`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.

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

hong wang<hong.wang@uky.edu> chi wang <chi.wang@uky.edu>

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)