knitr::opts_chunk$set(tidy=FALSE, cache=FALSE, dev="png", message=FALSE, error=FALSE, warning=TRUE)
If you use dmrseq in published research, please cite:
Korthauer, K., Chakraborty, S., Benjamini, Y., and Irizarry, R.A. Detection and accurate False Discovery Rate control of differentially methylated regions from Whole Genome Bisulfite Sequencing Biostatistics, 2018 (in press).
This package builds upon the bsseq package [@Hansen2012], which provides efficient storage and manipulation of bisulfite sequencing data and inference for differentially methylated CpGs. The main goal of dmrseq [@Korthauer183210] is to provide inference for differentially methylated regions, or groups of CpGs.
Here we show the most basic steps for a differential methylation analysis. There are a variety of steps upstream of dmrseq that result in the generation of counts of methylated reads and total reads covering each CpG for each sample, including mapping of sequencing reads to a reference genome with and without bisulfite conversion. You can use the software of your preference for this step (one option is Bismark), as long as you are able to obtain counts of methylation and coverage (as opposed to solely methylation proportions, as discussed below).
This package uses a specific data structure to store and manipulate
bisulfite sequencing data introduced by the bsseq package. This data
structure is a class called
BSseq. Objects of the class
all pertinent information for a bisulfite sequencing experiment, including
the number of reads corresponding to methylation, and the total number
of reads at each
CpG site, the location of each CpG site, and experimental metadata on the
samples. Note that here we focus on CpG methylation, since this is the
most common form of methylation in humans and many other organisms; take
care when applying this method to other types of methylation and make sure
that it will
be able to scale to the number of methylation sites, and that similar
assumptions can be made regarding spatial correlation. Also note that
the default settings for smoothing parameters and spacing/gap parameters
are set to values that we found useful, but may need to be altered for
datasets for other organisms.
To store your data in a
BSseq object, make sure you have the following
genomic positions, including chromosome and location, for methylation loci.
a (matrix) of M (Methylation) values, describing the number of reads supporting methylation covering a single loci. Each row in this matrix is a methylation loci and each column is a sample.
a (matrix) of Cov (Coverage) values, describing the total number of reads covering a single loci. Each row in this matrix is a methylation loci and each column is a sample.
The following code chunk assumes that
pos are vectors of
chromosome names and positions, respectively, for each CpG in the dataset.
You can also provide a
GRanges object instead of
also assumes that the matrices of methylation and coverage values (described
above) are named
Cov, respectively. Note,
Cov can also
be data stored on-disk (not in memory) using HDF5 files with the
DelayedMatrix with the
trt objects are
vectors with sample labels and condition labels for each sample. A condition
label could be something like
treatment or control, a tissue type, or a continous measurement.
This is the covariate for which you wish to test for differences in
methylation. Once the
BSseq object is constructed and the sample covariate
information is added, DMRs are obtained by running the
A continuous covariate is assumed if the data type of the
continuous, with the exception of if there are only two unique values
(then a two group comparison is carried out).
bs <- BSseq(chr = chr, pos = pos, M = M, Cov = Cov, sampleNames = sampleNames) pData(bs)$Condition <- trt regions <- dmrseq(bs=bs, testCovariate="Condition")
For more information on constructing and manipulating
see the bsseq vignettes.
read.bismarkfunction to read bismark files into
BSseqobjects. See below for more details.
Please post dmrseq questions to the Bioconductor support site, which serves as a searchable knowledge base of questions and answers:
Posting a question and tagging with "dmrseq" will automatically send an alert to the package authors to respond on the support site. See the first question in the list of Frequently Asked Questions (FAQ) for information about how to construct an informative post.
As input, the dmrseq package expects count data as obtained, e.g.,
from Bisulfite-sequencing. The value in the i-th row and the j-th column of
M matrix tells how many methylated reads can be assigned to CpG i
in sample j. Likewise, the value in the i-th row and the j-th column of
Cov matrix tells how many total reads can be assigned to CpG i
in sample j. Although we might be tempted to combine these matrices into
one matrix that contains the methylation proportion (
Cov) at each CpG
site, it is critical to notice that this would be throwing away a lot of
information. For example, some sites have much higher coverage than others,
and naturally, we have more confidence in those with many reads mapping to them.
If we only kept the proportions, a CpG with 2 out of 2 reads methylated would
be treated the same as a CpG with 30 out of 30 reads methylated.
To use dmrseq, you need to have at least 2 samples in each condition. Without this replicates, it is impossible to distinguish between biological variability due to condition/covariate of interest, and inter-individual variability within condition.
If your experiment contains additional samples, perhaps from other conditions
that are not of interest in the current test, these should be filtered out
prior to running dmrseq. Rather than creating a new filtered object,
the filtering step can be included in the call to the main function
For more details, see the
Filtering CpGs and samples Section.
If you used Bismark for mapping and methylation level extraction, you can
read.bismark function from the bsseq package to read the
data directly into
The following example is from the help page of the function. After running
Bismark's methylation extractor, you should have output files with names
that end in
.bismark.cov.gz. You can specify a vector of file names with
file argument, and a corresponding vector of
sampleNames. It is
recommended that you set
rmZeroCov to TRUE in order to remove CpGs with
no coverage in any of the samples, and set
strandCollapse to TRUE in order
to combine CpGs on opposite strands into one observation (since CpG methylation)
library(dmrseq) infile <- system.file("extdata/test_data.fastq_bismark.bismark.cov.gz", package = 'bsseq') bismarkBSseq <- read.bismark(files = infile, rmZeroCov = TRUE, strandCollapse = FALSE, verbose = TRUE) bismarkBSseq
See the bsseq help pages for more information on using this function.
If you haven't used Bismark, but you have count data for number of methylated
reads and total coverage for each CpG, along with their corresponding chromosome
and position information, you can construct a
BSseq object from scratch,
like below. Notice that the
Cov matrices have the same dimension, and
pos have the same number of elements as rows in the count matrices
(which corresponds to the number of CpGs). Also note that the number of columns
in the count matrices matches the number of elements in
sampleNames and the
condition variable 'celltype`.
data("BS.chr21") M <- getCoverage(BS.chr21, type="M") Cov <- getCoverage(BS.chr21, type="Cov") chr <- as.character(seqnames(BS.chr21)) pos <- start(BS.chr21) celltype <- pData(BS.chr21)$CellType sampleNames <- sampleNames(BS.chr21)
head(M) head(Cov) head(chr) head(pos) dim(M) dim(Cov) length(chr) length(pos) print(sampleNames) print(celltype) bs <- BSseq(chr = chr, pos = pos, M = M, Cov = Cov, sampleNames = sampleNames) show(bs)
rm(M, Cov, pos, chr, bismarkBSseq)
The example data contains CpGs from chromosome 21 for four samples
from @Lister2009. To load this data directly (already in the
Two of the samples are replicates of the cell type 'imr90'
and the other two are replicates of the cell type 'h1'. Now that we have the
data loaded into a
BSseq object, we can use dmrseq
to find regions of the genome where these two cell types have significantly
different methylation levels. But first, we need to add the sample metadata
that indicates which samples are from which cell type (the
varialbe above). This information, which we call 'metadata',
will be used by the
dmrseq function to decide
which samples to compare to one another. The next section shows how to add
this information to the
To add sample metadata, including the covariate of interest, you can add it
BSseq object by adding columns to the
pData slot. You must have at least
one column of
pData, which contains the covariate of interest. Additional
columns are optional.
pData(bs)$CellType <- celltype pData(bs)$Replicate <- substr(sampleNames, nchar(sampleNames), nchar(sampleNames)) pData(bs)
We will then tell the
dmrseq function which metadata variable to use
for testing for methylation differences by setting the
parameter equal to its column name.
Note that unlike in bsseq, you do not need to carry out the smoothing step
with a separate function. In addition, you should not use bsseq's
function to smooth the methylation levels, since dmrseq smooths in a very
different way. Briefly, dmrseq smooths methylation differences, so it
carries out the smoothing step once. This is automatically done with the main
dmrseq function. bsseq on the other hand, smooths each sample
independently, so smoothing needs to be carried out once per sample.
For pairwise comparisons, dmrseq analyzes all CpGs that have at least one
read in at least one sample per group.
Thus, if your dataset contains CpGs with zero reads in every sample within a
group, you should filter them out prior to running
bsseq object contains extraneous samples that are part of the
experiment but not the differential methylation testing of interest, these
should be filtered out as well.
bsseq objects is straightforward:
If we wish to remove all CpGs that have no coverage in at least one sample and only keep samples with a CellType of "imr90" or "h1", we would do so with:
# which loci and sample indices to keep loci.idx <- which(DelayedMatrixStats::rowSums2(getCoverage(bs, type="Cov")==0) == 0) sample.idx <- which(pData(bs)$CellType %in% c("imr90", "h1")) bs.filtered <- bs[loci.idx, sample.idx]
Note that this is a trivial example, since our toy example object
already contains only loci with coverage at least one read in all samples as well
as only samples from the "imr90" and "h1" conditions.
Also note that instead of creating a separate object, the filtering step
can be combined with the call to
dmrseq by replacing the
bs input with a
There are two ways to adjust for covariates in the dmrseq model. The first way
is to specify the
adjustCovariate parameter of the
dmrseq() function as
a column of the
pData() slot that contains the covariate you
would like to adjust for. This will include that covariate directly in the
model. This is ideal if the adjustment covariate is continuous or has more
than two groups.
The second way is to specify the
matchCovariate parameter of the
function as a column of the
pData() slot that contains the covariate you
would like to match on. This will restrict the permutations considered to only
those where the
matchCovariate is balanced. For example, the
could represent the sex of each sample. In that case, a permutation that
includes all males in one group and all females in another would not be
considered (since there is a plausible biological difference that may induce
the null distribution to resemble non-null). This matching adjustment is ideal
for two-group comparisons.
The standard differential expression analysis steps are wrapped
into a single function,
dmrseq. The estimation steps performed
by this function are described briefly below, as well as in
more detail in the dmrseq paper. Here we run the results for a subset
of 20,000 CpGs in the interest of computation time.
testCovariate <- "CellType" regions <- dmrseq(bs=bs[240001:260000,], cutoff = 0.05, testCovariate=testCovariate)
Progress messages are printed to the console if
verbose is TRUE.
condition h1 vs imr90, tells you that positive methylation
differences mean h1 has higher methylation than imr90 (see below for
The results object is a
GRanges object with the coordiates
of each candidate region, and contains the following metadata columns (which
can be extracted with the
L= the number of CpGs contained in the region,
area= the sum of the smoothed beta values
beta= the coefficient value for the condition difference (Note: if the test covariate is categorical with more than 2 groups, there will be more than one beta column),
stat= the test statistic for the condition difference,
pval= the permutation p-value for the significance of the test statistic, and
qval= the q-value for the test statistic (adjustment for multiple comparisons to control false discovery rate).
index = anIRanges` containing the indices of the region's first CpG to last CpG.
The above steps are carried out on a very small subset of data (20,000 CpGs).
This package loads data into memory one chromosome at a
time. For on human data, this means objects with a few million
entries per sample (since there are roughly 28.2 million total CpGs in the human
genome, and the largest chromosomes will have more than 2 million CpGs).
This means that whole-genome
BSseq objects for several samples can use up
several GB of RAM. In order to improve speed, the package allows for easy
parallel processing of chromosomes, but be aware that using more cores will
also require the use of more RAM.
To use more cores, use the
register function of
BiocParallel. For example,
the following chunk (not evaluated here), would register 4 cores, and
then the functions above would
split computation over these cores.
dmrseq is a two-stage approach that first detects candidate regions and then explicitly evaluates statistical significance at the region level while accounting for known sources of variability. Candidate DMRs are defined by segmenting the genome into groups of CpGs that show consistent evidence of differential methylation. Because the methylation levels of neighboring CpGs are highly correlated, we first smooth the signal to combat loss of power due to low coverage as done in bsseq.
In the second stage, we compute a statistic for each candidate DMR that takes into account variability between biological replicates and spatial correlation among neighboring loci. Significance of each region is assessed via a permutation procedure which uses a pooled null distribution that can be generated from as few as two biological replicates, and false discovery rate is controlled using the Benjamini-Hochberg procedure.
For more details, refer to the dmrseq paper [@Korthauer183210].
The default smoothing parameters (
are designed to focus on local DMRs, generally in the range of hundreds to
thousands of bases. In some applications, such as cancer, it is of interest
to effectively 'zoom out' in order to detect larger (lower-resolution)
methylation blocks on the order of hundreds of thousands to millions of bases.
To do so, you can
block argument to true, which will only include candidate regions with
blockSize basepairs (default = 5000). This setting will also merge
candidate regions that (1) are in the same direction and (2) are less than 1kb
apart with no covered CpGs separating them. The region-level model used is also
slightly modified - instead of a loci-specific intercept for each CpG in the
region, the intercept term is modeled as a natural spline with one interior
knot per each 10kb of length (up to 10 interior knots).
In addition, detecting large-scale blocks requires that
the smoothing window be increased to minimize the impact of noisy local
methylation measurements. To do so, the values of the
smoothing parameters should be increased. For example, to use a smoothing window
that captures at least 500 CpGs or 50,000 basepairs that are spaced apart by no
more than 1e6 bases, use
In addition, to avoid a block being broken up simply due to a gap with no
covered CpGs, you can increase the
testCovariate <- "CellType" blocks <- dmrseq(bs=bs[120001:125000,], cutoff = 0.05, testCovariate=testCovariate, block = TRUE, minInSpan = 500, bpSpan = 5e4, maxGapSmooth = 1e6, maxGap = 5e3) head(blocks)
The top hit is
r signif(width(blocks)/1e3, 3) thousand basepairs wide.
In general, it also might be advised to decrease the cutoff when detecting
blocks, since a smaller methylation
difference might be biologically significant if it is maintained
over a large genomic region. Note that block-finding can be more computationally
intensive since we are fitting region-level models to large numbers of CpGs at a
time. In the toy example above we are only searching over 5,000 CpGs (which
r signif((max(end(bs[120001:125000,])) -
thousand basepairs), so we do not find enough null
candidate regions to carry out inference and obtain significance levels.
How many regions were significant at the FDR (q-value) cutoff of 0.05? We
can find this by counting how many values in the
qval column of the
object were less than 0.05.
You can also subset the regions by an FDR cutoff.
sum(regions$qval < 0.05) # select just the regions below FDR 0.05 and place in a new data.frame sigRegions <- regions[regions$qval < 0.05,]
You can determine the proportion of regions with hyper-methylation by counting how many had a positive direction of effect (positive statistic).
sum(sigRegions$stat > 0) / length(sigRegions)
To interpret the direction of effect, note that for a two-group comparison dmrseq uses alphabetical order of the covariate of interest. The condition with a higher alphabetical rank will become the reference category. For example, if the two conditions are "A" and "B", the "A" group will be the reference category, so a positive direction of effect means that "B" is hyper-methylated relative to "A". Conversely, a negative direction of effect means that "B" is hypo-methylated relative to "A".
It can be useful to visualize individual DMRs, so we provide a plotting function that is based off of bsseq's plotting functions. There is also functionality to add annotations using the annotatr package to see the nearby CpG categories (island, shore, shelf, open sea) and nearby coding sequences.
To retrieve annotations for genomes supported by annotatr, use the
getAnnot, and pass this annotation object to the
function as the
# get annotations for hg18 annoTrack <- getAnnot("hg18") plotDMRs(bs, regions=regions[1,], testCovariate="CellType", annoTrack=annoTrack)
Here we also plot the top methylation block from the block analysis:
plotDMRs(bs, regions=blocks[1,], testCovariate="CellType", annoTrack=annoTrack)
It can also be helpful to visualize overall distributions of methylation values
and / or coverage. The function
plotEmpiricalDistribution will plot the
methylation values of
the covariate of interest (specified with
By changing the
type argument to
Cov, it will also plot the distribution of
coverage values. In addition, samples can be plotted separately by setting
bySample to true.
plotEmpiricalDistribution(bs, testCovariate="CellType", type="Cov", bySample=TRUE)
A plain-text file of the results can be exported using the base R functions write.csv or write.delim. We suggest using a descriptive file name indicating the variable and levels which were tested.
For a two-group comparison, it might be of interest to extract the raw mean
methylation differences over the DMRs. This can be done with the helper function
meanDiff. For example, we can extract the raw mean difference values for
the regions at FDR level 0.05 (using the
sigRegions object created
in the section
Explore how many regions were significant).
rawDiff <- meanDiff(bs, dmrs=sigRegions, testCovariate="CellType") str(rawDiff)
If you have multiple samples from the same condition (e.g. control samples),
simDMRS will split these into two artificial sample groups
and then add in silico DMRs. This can then be used to assess sensitivity
and specificity of DMR approaches, since we hope to be able to recover the
DMRs that were spiked in, but not identify too many other differences (since
we don't expect any biological difference between the two artificial sample
The use of this function is demonstrated below, although note that in this toy example, we do not have enough samples from the same biological condition to split into two groups, so instead we shuffle the cell types to create a null sample comparison.
data(BS.chr21) # reorder samples to create a null comparison BS.null <- BS.chr21[1:20000,c(1,3,2,4)] # add 100 DMRs BS.chr21.sim <- simDMRs(bs=BS.null, num.dmrs=100) # bsseq object with original null + simulated DMRs show(BS.chr21.sim$bs) # coordinates of spiked-in DMRs show(BS.chr21.sim$gr.dmrs) # effect sizes head(BS.chr21.sim$delta)
The resulting object is a list with the following elements:
GRangesobject containing the coordinates of the true spiked in DMRs
dmr.mncov: a numeric vector containing the mean coverage of the simulated DMRs
dmr.L: a numeric vector containing the sizes (number of CpG loci) of the simulated DMRs
BSSeqobject containing the original null data + simulated DMRs
delta: a numeric vector of effect sizes (proportion differences) of the simulated DMRs.
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