DESeq2: nbinomLRT – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

nbinomLRT

Likelihood ratio test (chi-squared test) for GLMs

Description

This function tests for significance of change in deviance between a full and reduced model which are provided as formula. Fitting uses previously calculated sizeFactors (or normalizationFactors) and dispersion estimates.

Usage

nbinomLRT(
  object,
  full = design(object),
  reduced,
  betaTol = 1e-08,
  maxit = 100,
  useOptim = TRUE,
  quiet = FALSE,
  useQR = TRUE,
  minmu = if (type == "glmGamPoi") 1e-06 else 0.5,
  type = c("DESeq2", "glmGamPoi")
)

Arguments

`object`	a DESeqDataSet
`full`	the full model formula, this should be the formula in `design(object)`. alternatively, can be a matrix
`reduced`	a reduced formula to compare against, e.g. the full model with a term or terms of interest removed. alternatively, can be a matrix
`betaTol`	control parameter defining convergence
`maxit`	the maximum number of iterations to allow for convergence of the coefficient vector
`useOptim`	whether to use the native optim function on rows which do not converge within maxit
`quiet`	whether to print messages at each step
`useQR`	whether to use the QR decomposition on the design matrix X while fitting the GLM
`minmu`	lower bound on the estimated count while fitting the GLM
`type`	either "DESeq2" or "glmGamPoi". If `type = "DESeq2"` a classical likelihood ratio test based on the Chi-squared distribution is conducted. If `type = "glmGamPoi"` and previously the dispersion has been estimated with glmGamPoi as well, a quasi-likelihood ratio test based on the F-distribution is conducted. It is supposed to be more accurate, because it takes the uncertainty of dispersion estimate into account in the same way that a t-test improves upon a Z-test.

Details

The difference in deviance is compared to a chi-squared distribution with df = (reduced residual degrees of freedom - full residual degrees of freedom). This function is comparable to the nbinomGLMTest of the previous version of DESeq and an alternative to the default nbinomWaldTest.

Value

a DESeqDataSet with new results columns accessible with the results function. The coefficients and standard errors are reported on a log2 scale.

Examples

dds <- makeExampleDESeqDataSet()
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
dds <- nbinomLRT(dds, reduced = ~ 1)
res <- results(dds)

DESeq2

Differential gene expression analysis based on the negative binomial distribution

v1.30.1

LGPL (>= 3)

Authors

Michael Love [aut, cre], Constantin Ahlmann-Eltze [ctb], Kwame Forbes [ctb], Simon Anders [aut, ctb], Wolfgang Huber [aut, ctb]

Initial release