edge (version 2.4.2)

gof: Goodness of Fit Tests for Multiple GLM Fits

Description

Conducts deviance goodness of fit tests for each fit in a DGEGLM object

Usage

gof(glmfit, pcutoff = 0.1, adjust = "holm", plot = FALSE,
    main = "qq-plot of residual deviances", ...)

Arguments

glmfit

a DGEGLM object containing results from fitting NB GLMs to genes in a DGE dataset with a global dispersion model. Usually this is output from glmFit.

pcutoff

scalar giving the cut-off value for the Holm-adjusted p-value. Genes with Holm-adjusted p-values lower than this cutoff value are flagged as `dispersion outlier' genes.

adjust

method used to adjust goodness of fit p-values for multiple testing.

plot

logical, if TRUE a qq-plot is produced.

main

character, title for the plot.

...

other arguments are passed to qqnorm.

Value

• A list with the following components:
• gof.statistics
  numeric vector of deviance statistics, which are the statistics used for the goodness of fit test
• gof.pvalues
  numeric vector of p-values providing evidence of poor fit; computed from the chi-square distribution on the residual degrees of freedom from the GLM fits.
• outlier
  logical vector indicating whether or not each gene is a `dispersion outlier' (i.e., the model fit is poor for that gene indicating that the dispersion estimate is not good for that gene).
• df
  scalar, the residual degrees of freedom from the GLM fit for which the goodness of fit statistics have been computed. Also the degrees of freedom for the goodness of fit statistics for the LR (chi-quare) test for significance.
• If plot=TRUE, then a plot is also produced on the current graphics device.

Details

This function is useful for evaluating the adequacy of a global dispersion model, such as a constant or trended dispersion. If plot=TRUE, then it produces a qq-plot similar to those in Figure 2 of McCarthy et al (2012).

References

McCarthy, DJ, Chen, Y, Smyth, GK (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 40, 4288-4297 http://nar.oxfordjournals.org/content/40/10/4288

See Also

qqnorm. glmFit for more information on fitting NB GLMs to DGE data.

Examples

nlibs <- 3
ngenes <- 100
dispersion.true <- 0.1

# Make first gene respond to covariate x
x <- 0:2
design <- model.matrix(~x)
beta.true <- cbind(Beta1=2,Beta2=c(2,rep(0,ngenes-1)))
mu.true <- 2^(beta.true %*% t(design))

# Generate count data
y <- rnbinom(ngenes*nlibs,mu=mu.true,size=1/dispersion.true)
y <- matrix(y,ngenes,nlibs)
colnames(y) <- c("x0","x1","x2")
rownames(y) <- paste("gene",1:ngenes,sep=".")
d <- DGEList(y)

# Normalize
d <- calcNormFactors(d)

# Fit the NB GLMs
fit <- glmFit(d, design, dispersion=dispersion.true)
# Check how good the fit is for each gene
gof(fit)