nbinomLRT: Likelihood ratio test (chi-squared test) for GLMs In DESeq2: Differential gene expression analysis based on the negative binomial distribution

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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```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.

`DESeq`, `nbinomWaldTest`
 ```1 2 3 4 5``` ```dds <- makeExampleDESeqDataSet() dds <- estimateSizeFactors(dds) dds <- estimateDispersions(dds) dds <- nbinomLRT(dds, reduced = ~ 1) res <- results(dds) ```