Obtain absolute or squared Levene residuals for each CpG given a series of methylation arrays
object of class
the design matrix of the experiment, with rows corresponding to arrays/samples and columns to coefficients to be estimated. Defaults to the unit vector.
This function will return absolute or squared Levene residuals given a matrix of M values and a design matrix. This can be used for graphing purposes or for downstream analysis such a gene set testing based on differential variability rather than differential methylation. If no design matrix is given, the residuals are determined by treating all samples as coming from one group.
Returns a list with three components.
data contains a matrix of absolute or squared residuals,
AvgVar is a vector of sample variances and
LogVarRatio corresponds to the columns of the design matrix and is usually the ratios of the log of the group variances.
Phipson, B., and Oshlack, A. (2014). A method for detecting differential variability in methylation data shows CpG islands are highly variably methylated in cancers. Genome Biology, 15:465.
1 2 3 4 5 6 7 8 9 10 11
# Randomly generate data for a 2 group problem with 100 CpG sites and 5 arrays in each group y <- matrix(rnorm(1000),ncol=10) group <- factor(rep(c(1,2),each=5)) design <- model.matrix(~group) # Get absolute Levene Residuals resid <- getLeveneResiduals(y,design) # Plot the first CpG barplot(resid$data[1,],col=rep(c(2,4),each=5),ylab="Absolute Levene Residuals",names=group)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.