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%\VignetteIndexEntry{DMRcate for bisulfite sequencing assays (WGBS and RRBS)}
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\begin{document}
\title{\texttt{DMRcate} for bisulfite sequencing}

\author{Peters TJ}
\maketitle


\renewcommand{\abstractname}{Summary}
\begin{abstract}
Worked example to find DMRs from whole genome bisulfite sequencing data.
\end{abstract}

<<bioconductor, message=FALSE, warning=FALSE, eval=FALSE>>=
if (!require("BiocManager"))
	install.packages("BiocManager")
BiocManager::install("DMRcate")
@

Load \texttt{DMRcate} into the workspace:

<<libr, message=FALSE, warning=FALSE>>=
library(DMRcate)
@

Bisulfite sequencing assays are fundamentally different to arrays, because methylation is represented as a pair of methylated and unmethylated reads per sample, instead of a single beta value. Although we could simply take the logit-transformed fraction of methylated reads per CpG, this removes the effect of varying read depth across the genome. For example, a sampling depth of 30 methylated reads and 10 unmethylated reads is a much more precise estimate of the methylation level of a given CpG site than 3 methylated and 1 unmethylated. Hence, we take advantage of the fact that the overall effect can be expressed as an interaction between the coefficient of interest and a two-level factor representing methylated and unmethylated reads \cite{Chenb}.

The example shown here will be performed on a BSseq object containing bisulfite sequencing of regulatory T cells from various tissues as part of the \texttt{tissueTreg} package\cite{Delacher}, imported using ExperimentHub. First, we will import the data:

<<loadeh, message=FALSE>>=
library(ExperimentHub)
eh <- ExperimentHub()
bis_1072 <- eh[["EH1072"]]
bis_1072
colnames(bis_1072)
@

The data contains 15 samples: 3 (unmatched) replicates of mouse Tregs from fat, liver, skin and lymph node, plus a group of 3 CD4+ conventional lymph node T cells (Tcon). We will annotate the BSseq object to reflect this phenotypic information:

<<bisphen>>=
bsseq::pData(bis_1072) <- data.frame(replicate=gsub(".*-", "", colnames(bis_1072)),
                              tissue=substr(colnames(bis_1072), 1, 
                                            nchar(colnames(bis_1072))-3), 
                              row.names=colnames(bis_1072))
colData(bis_1072)$tissue <- gsub("-", "_", colData(bis_1072)$tissue)
as.data.frame(colData(bis_1072))
@

For standardisation purposes (and for \texttt{DMR.plot} to recognise the genome) we will change the chromosome naming convention to UCSC:

<<changeseqlevs>>=
library(GenomeInfoDb)
bis_1072 <- renameSeqlevels(bis_1072, mapSeqlevels(seqlevels(bis_1072), "UCSC"))
@

For demonstration purposes, we will retain CpGs on chromosome 19 only:

<<chr19filter>>=
bis_1072 <- bis_1072[seqnames(bis_1072)=="chr19",]
bis_1072
@

Now we can prepare the model to be fit for \texttt{sequencing.annotate()}. The arguments are equivalent to \texttt{cpg.annotate()} but for a couple of exceptions: 
\begin{itemize}
  \item There is an extra argument \texttt{all.cov} giving an option whether to retain only CpGs where \textit{all} samples have non-zero coverage, or whether to retain CpGs with only partial sample representation.
  \item The design matrix should be constructed to reflect the 2-factor structure of methylated and unmethylated reads. Fortunately, \texttt{edgeR::modelMatrixMeth()} can take a regular design matrix and transform is into the appropriate structure ready for model fitting. 
\end{itemize}

<<bsdesign, message=FALSE>>=
tissue <- factor(pData(bis_1072)$tissue)
tissue <- relevel(tissue, "Liver_Treg")

#Regular matrix design
design <- model.matrix(~tissue)
colnames(design) <- gsub("tissue", "", colnames(design))
colnames(design)[1] <- "Intercept"
rownames(design) <- colnames(bis_1072)
design

#Methylation matrix design
methdesign <- edgeR::modelMatrixMeth(design)
methdesign
@

Just like for \texttt{cpg.annotate()}, we can specify a contrast matrix to find our comparisons of interest.

<<fitBSseq>>=
cont.mat <- limma::makeContrasts(treg_vs_tcon=Lymph_N_Treg-Lymph_N_Tcon,
                                 fat_vs_ln=Fat_Treg-Lymph_N_Treg,
                                 skin_vs_ln=Skin_Treg-Lymph_N_Treg,
                                 fat_vs_skin=Fat_Treg-Skin_Treg,
                                 levels=methdesign)
cont.mat
@

Say we want to find DMRs between the regulatory and conventional T cells from the lymph node. First we would fit the model, where \texttt{sequencing.annotate()} transforms counts into log2CPMs (via \texttt{limma::voom()}) and uses \texttt{limma} under the hood to generate per-CpG \textit{t}-statistics, indexing the FDR at 0.05: 

<<sequencingannotate>>=
seq_annot <- sequencing.annotate(bis_1072, methdesign, all.cov = TRUE, 
                                   contrasts = TRUE, cont.matrix = cont.mat, 
                                   coef = "treg_vs_tcon", fdr=0.05)
seq_annot
@

And then, just like before, we can call DMRs with \texttt{dmrcate()}:

<<seqdmrcate>>=
dmrcate.res <- dmrcate(seq_annot, C=2, min.cpgs = 5)
dmrcate.res
treg_vs_tcon.ranges <- extractRanges(dmrcate.res, genome="mm10")
treg_vs_tcon.ranges
@

Looks like the top DMR is associated with the \textit{Jak2} locus and hypomethylated in the Treg cells (since \texttt{meandiff < 0}). We can plot it like so:

<<seqDMRplot1, message=FALSE>>=
cols <- as.character(plyr::mapvalues(tissue, unique(tissue), 
                                     c("darkorange", "maroon", "blue", 
                                       "black", "magenta")))
names(cols) <- tissue

DMR.plot(treg_vs_tcon.ranges, dmr = 1, 
         CpGs=bis_1072[,tissue %in% c("Lymph_N_Tcon", "Lymph_N_Treg")], 
         phen.col = cols[tissue %in% c("Lymph_N_Tcon", "Lymph_N_Treg")], 
         genome="mm10")
@

Now, let's find DMRs between fat and skin Tregs. 

<<fatskin>>=
seq_annot <- sequencing.annotate(bis_1072, methdesign, all.cov = TRUE, 
                                   contrasts = TRUE, cont.matrix = cont.mat, 
                                   coef = "fat_vs_skin", fdr=0.05)
@

Because this comparison is a bit more subtle, there are very few significantly differential CpGs at this threshold. So we can use \texttt{changeFDR()} to relax the FDR to 0.25, taking into account that there is an increased risk of false positives.

<<redefinethresh>>==
seq_annot <- changeFDR(seq_annot, 0.25)
@


<<dmrsfatskin>>=
dmrcate.res <- dmrcate(seq_annot, C=2, min.cpgs = 5)
fat_vs_skin.ranges <- extractRanges(dmrcate.res, genome="mm10")
@

Now let's plot the top DMR with not only fat and skin, but with all samples:

<<seqDMRplot2, message=FALSE>>=
cols
DMR.plot(fat_vs_skin.ranges, dmr = 1, CpGs=bis_1072, phen.col = cols, genome="mm10")
@

Here we can see the methylation of skin cells over this region near the \textit{Gcnt1} promoter is hypomethylated not only relative to fat, but to the other tissues as well.

<<sessionInfo>>=
sessionInfo()
@

\begin{thebibliography}{99}

\bibitem{Chenb}  
  Chen Y, Pal B, Visvader JE, Smyth GK. 
  Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR. 
  \emph{F1000Research}. 2017 \textbf{6}, 2055.

\bibitem{Delacher}
  Delacher M, Imbusch CD, Weichenhan D, Breiling A, Hotz-Wagenblatt A, Trager U, ... Feuerer M. (2017). 
  Genome-wide DNA-methylation landscape defines specialization of regulatory T cells in tissues. 
  \emph{Nature Immunology}. 2017 \textbf{18}(10), 1160-1172. 
  
\end{thebibliography}

\end{document}