Obtain absolute or squared Levene residuals for each CpG given a series of methylation arrays

1 | ```
getLeveneResiduals(data, design = NULL, type = NULL)
``` |

`data` |
object of class |

`design` |
the design matrix of the experiment, with rows corresponding to arrays/samples and columns to coefficients to be estimated. Defaults to the unit vector. |

`type` |
character string, |

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.

Belinda Phipson

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)
``` |

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