Nothing

```
#' diffMeth
#'
#' Determines differential methylated sites from a RangedSummarizedExperiment
#'
#' @param se A RangedSummarizedExperiment containing meta data of the samples.
#' @param cateogory The heading name in the sample data to be tested for differential methylation.
#' @param condition1 The reference group within the cateogory.
#' @param condition2 The experimental group within the cateogory.
#' @param block The heading name in the sample data if differential methylation is to be tested with a blocking factor. Default is NULL.
#' @param cpmThreshold Counts per million threshold of read counts to be filtered out of the analysis.
#' @param thresholdSamples Minimum number of samples to contain the counts per million threshold.
#' @usage diffMeth(se, cateogory, condition1, condition2,
#' block = NULL, cpmThreshold, thresholdSamples)
#' @return A data frame containing which cut sites that are differenitally methylated.
#' @import edgeR
#' @importFrom stats model.matrix relevel
#' @author Benjamin Mayne
#' @examples
#' # Load data
#' data(ratdata2)
#' top <- diffMeth(se = ratdata2, cateogory = "Group",
#' condition1 = "Control", condition2 = "Experimental",
#' cpmThreshold = 1, thresholdSamples = 1)
#' @export
# Function for determining differential cutting/methylation
diffMeth <- function(se, cateogory, condition1, condition2, block = NULL,
cpmThreshold, thresholdSamples){
# Unit tests
## Check if se is a RangedSummarizedExperiment
if(!is(se, "RangedSummarizedExperiment")){
stop("se must be a RangedSummarizedExperiment")
}
## Check if cateogory is a character
if(!is(cateogory, "character")){
stop("cateogory must be a character")
}
## Check if condition1 is a character
if(!is(condition1, "character")){
stop("condition1 must be a character")
}
## Check if condition2 is a character
if(!is(condition2, "character")){
stop("condition2 must be a character")
}
## Check if cpmThreshold is a numeric
if(!is(cpmThreshold, "numeric")){
stop("cpmThreshold must be a numeric")
}
## Check if thresholdSamples is a numeric
if(!is(thresholdSamples, "numeric")){
stop("thresholdSamples must be a numeric")
}
pd <- colData(se)
# Seperate the data into the two conditions
counts <- data.frame(assay(se)[ ,pd[,cateogory] == condition1 | pd[,cateogory] == condition2])
group <- pd[,cateogory][pd[,cateogory] == condition1 | pd[,cateogory] == condition2]
pd <- pd[pd[,cateogory] == condition1 | pd[,cateogory] == condition2,]
# Make the DGE object
dge <- DGEList(counts=counts, group=group,
genes = row.names(counts))
# Remove lowly cut sites as defined by user
dge_expressed <- dge[rowSums(cpm(dge) > cpmThreshold) >= thresholdSamples, ]
# Re-compute the library sizes after filtering
dge_expressed$samples$lib.size <- colSums(dge_expressed$counts)
# Data Normalisation
# Compute effective library sizes using the default trimmed mean of M-values (TMM)
dge_expressed <- calcNormFactors(dge_expressed, method = "TMM")
if(is.null(block)){
group = factor(group)
group <- relevel(group, ref = condition1)
design <- model.matrix(~group)
} else {
# Make the design matrix and use the blocking
block = factor(pd[,block])
group = factor(group)
group <- relevel(group, ref = condition1)
design <- model.matrix(~block+group)
}
# Estimate the dispersion parameters and fit the model
dispersions <- estimateDisp(dge_expressed, design)
# fit the genewise negative binomial GLMs for design
fit <- glmFit(dispersions, design)
# Statistical Analyses for differential methylaiton
# Coefficient contrast
if(is.null(block)){
lrt <- glmLRT(fit, coef = colnames(design)[2])
} else {
lrt <- glmLRT(fit, coef = colnames(design)[3])
}
# Use Benjamini-Hochberg (BH) method for p-values
lrt_top <- topTags(lrt, n = nrow(dge_expressed$counts), adjust.method = "BH", sort.by = "PValue")
colnames(lrt_top$table)[1] <- "site"
row.names(lrt_top$table) <- NULL
return(lrt_top$table)
}
```

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