DNA methylation analysis in R

Introduction
R Basics
Genomics and R
RRBS analysis with R package methylKit

Analyzing RRBS methylation data with R
Basic analysis with R package methylKit

Altuna Akalın
ala2027@med.cornell.edu

EpiWorkshop 2013
Weill Cornell Medical College
Institute for Computational Biomedicine

Akalın, Altuna Analyzing RRBS methylation data with R

Introduction
R Basics
Genomics and R

Outline

1 Introduction

2 R Basics

3 Genomics and R

4 RRBS analysis with R package methylKit
Working with annotated methylation events


Introduction
R Basics
Genomics and R

Introduction

This tutorial will show how to analyze methylation data obtained from
Reduced Representation Bisulﬁte Sequencing (RRBS) experiments.
First, we will introduce:
R basics
Genomics and R: Bioconductor packages that will be useful
when working with genomic intervals.
How to use the package methylKit to analyze methylation
data.


Introduction
R Basics
Genomics and R

Before we start...

Download and unzip the data folder
http://methylkit.googlecode.com/files/
methylKitTutorialData_feb2012.zip
Make sure that you installed R 2.15.2 and RStudio
for Mac OSX : http://cran.r-project.org/bin/macosx/old/R-2.15.2.pkg
for windows :http://cran.r-project.org/bin/windows/base/old/2.15.2/
For linux : http://cran.r-project.org/src/base/R-2/R-2.15.2.tar.gz
http://www.rstudio.com/ide/download/


Introduction
R Basics
Genomics and R

R Basics

Here are some basic R operations and data structures that will be
good to know if you do not have prior experience with R.


Introduction
R Basics
Genomics and R

Computations in R
R can be used as an ordinary calculator. There are a few examples:
2 + 3 * 5 # Note the order of operations.

## [1] 17

log(10) # Natural logarithm with base e=2.718282

## [1] 2.303

4^2 # 4 raised to the second power

## [1] 16

3/2 # Division

## [1] 1.5

sqrt(16) # Square root

## [1] 4

Introduction
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Genomics and R

Vectors
R handles vectors easily and intuitively.
x <- c(1, 3, 2, 10, 5) #create a vector x with 5 components
x

## [1] 1 3 2 10 5

y <- 1:5 #create a vector of consecutive integers
y

## [1] 1 2 3 4 5

y + 2 #scalar addition

## [1] 3 4 5 6 7

2 * y #scalar multiplication

## [1] 2 4 6 8 10

y^2 #raise each component to the second power

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Matrices
A matrix refers to a numeric array of rows and columns. One of the
easiest ways to create a matrix is to combine vectors of equal length
using cbind(), meaning "column bind":
x <- c(1, 2, 3)
y <- c(4, 5, 6)
m1 <- cbind(x, y)
m1

## x y
## [1,] 1 4
## [2,] 2 5
## [3,] 3 6

t(m1) # transpose of m1

## [,1] [,2] [,3]
## x 1 2 3
## y 4 5 6


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Matrices

dim(m1) # 2 by 3 matrix

## [1] 3 2

You can also directly list the elements and specify the matrix:
m2 <- matrix(c(1, 3, 2, 5, -1, 2, 2, 3, 9), nrow = 3)
m2

## [,1] [,2] [,3]
## [1,] 1 5 2
## [2,] 3 -1 3
## [3,] 2 2 9


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Genomics and R

Data Frames

A data frame is more general than a matrix, in that different columns
can have different modes (numeric, character, factor, etc.). Small to
moderate size data frame can be constructed by data.frame()
function. For example, we illustrate how to construct a data frame
from genomic intervals or coordinates.
chr <- c("chr1", "chr1", "chr2", "chr2")
strand <- c("-", "-", "+", "+")
start <- c(200, 4000, 100, 400)
end <- c(250, 410, 200, 450)
mydata <- data.frame(chr, start, end, strand)


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Data Frames

names(mydata) <- c("chr", "start", "end", "strand") #variable names
mydata

## chr start end strand
## 1 chr1 200 250 -
## 2 chr1 4000 410 -
## 3 chr2 100 200 +
## 4 chr2 400 450 +

# OR this will work too
mydata <- data.frame(chr = chr, start = start,
end = end, strand = strand)
mydata

## chr start end strand
## 1 chr1 200 250 -
## 2 chr1 4000 410 -
## 3 chr2 100 200 +
## 4 chr2 400 450 +


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Data Frames

There are a variety of ways to identify the elements of a data frame .
mydata[2:4] # columns 2,3,4 of data frame

## start end strand
## 1 200 250 -
## 2 4000 410 -
## 3 100 200 +
## 4 400 450 +

# columns chr and start from data frame
mydata[c("chr", "start")]

## chr start
## 1 chr1 200
## 2 chr1 4000
## 3 chr2 100
## 4 chr2 400


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Data Frames

mydata$start # variable start in the data frame

## [1] 200 4000 100 400


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Lists
An ordered collection of objects (components). A list allows you to
gather a variety of (possibly unrelated) objects under one name.
# example of a list, a string, a numeric
# vector, a matrix, and a scalar
w <- list(name = "Fred", mynumbers = c(1, 2, 3),
mymatrix = matrix(1:4, ncol = 2), age = 5.3)
w

## $name
## [1] "Fred"
##
## $mynumbers
## [1] 1 2 3
##
## $mymatrix
## [,1] [,2]
## [1,] 1 3
## [2,] 2 4
##
## $age
## [1] 5.3

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Lists

You can extract elements of a list using the [[]] convention.
w[[3]] # 3rd component of the list

## [,1] [,2]
## [1,] 1 3
## [2,] 2 4

w[["mynumbers"]] # component named mynumbers in list

## [1] 1 2 3


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Graphics in R

R has great support for plotting and customizing plots. We will show
only a few below.
x <- rnorm(50) # sample 50 values from normal distr.
hist(x) # plot the histogram of those values
hist(x, main = "Hello histogram!!!") # change the title
y <- rnorm(50) # randomly sample 50 points from normal distr.
plot(x, y) #plot a scatter plot
y <- rnorm(50) # randomly sample 50 points from normal distr.
boxplot(x, y, main = "boxplots of random samples") #plot boxplots
plot(x, y, main = "scatterplot of random samples") #scatter plots


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Graphics in R

Saving plots
pdf(file = "mygraphs/myplot.pdf")
plot(x, y)
dev.off()
# OR

plot(x, y)
dev.copy(pdf, file = "mygraphs/myplot.pdf")
dev.off()


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Getting help on R functions/commands

Most R functions have great documentation on how to use them. Try
? and ??. ? will pull the documentation on the functions and ?? will
ﬁnd help pages on a vague topic. Try on R terminal:
?hist
??histogram


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Genomics and R

Installing packages

Where R packages live...
CRAN: http://cran.r-project.org/
Bioconductor: http://bioconductor.org/
R-forge: http://r-forge.r-project.org/
Github: http://github.com/
Googlecode: http://code.google.com/
How to install packages
install.packages("devtools")
source("http://bioconductor.org/biocLite.R")
biocLite("GenomicRanges")
Install from the source:
install.packages("devtools_1.1.tar.gz",repos=NULL,
type="source")


Introduction
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Genomics and R

Genomics and R

Bioconductor and CRAN has many packages that will help analyze
genomics data. Some of the most popular ones are GenomicRanges,
IRanges, Rsamtools and BSgenome. Check out
http:bioconductor.org for packages that suits your purpose.


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Genomics and R

Reading the genomics data

Most of the genomics data are in the form of genomic intervals
associated with a score. That means mostly the data will be in table
format with columns denoting chromosome, start positions, end
positions, strand and score. One of the popular formats is BED
format. In R, you can easily read tabular format data with
read.table() function. In addition, you can write data to a text ﬁle
with write.table().
# read enhancer marker BED file
enh.df = read.table("subset.enhancers.bed", header = FALSE)
# read CpG island BED file
cpgi.df = read.table("subset.cpgi.hg18.bed.txt",
header = FALSE)

# write data frames to text files
write.table(cpgi.df, "example.out.txt")


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Reading the genomics data

# check first lines to see how the data
# looks like
head(enh.df, 2)
head(cpgi.df, 2)
# get CpG islands on chr21
head(cpgi.df[cpgi.df$V1 == "chr21", ], 2)


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Using GenomicRanges package for operations on
genomic intervals

One of the most useful operations when working with genomic
intervals is the overlap operation
bioconductor packages: IRanges and GenomicRanges provide
efﬁcient ways to handle genomic interval data.
Below, we will show how to convert your data to GenomicRanges
objects/data structures and do overlap between enhancers and
CpG islands.


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Genomics and R

genomic intervals

# covert enhancer data frame to GenomicRanges object
library(GenomicRanges) # load the package
enh <- GRanges(seqnames=enh.df$V1,
ranges=IRanges(start=enh.df$V2,end=enh.df$V3) )
cpgi = GRanges(seqnames=cpgi.df$V1,
ranges=IRanges(start=cpgi.df$V2,end=cpgi.df$V3),
ids=cpgi.df$V4 )
# find enhancers overlapping with CpG islands
cpg.enh=subsetByOverlaps(enh, cpgi)


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genomic intervals
# number of enhancers overlapping with CpG
# islands
length(cpg.enh)
# number of all enhancers in the set
length(enh)
# plot histogram of lenths of CpG islands
hist(width(cpgi))

Histogram of width(cpgi)
1000
Frequency

500
0

0 2000
Akalın, Altuna
6000 10000
Analyzing RRBS methylation data with R

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Methylation proﬁle by Reduced Representation
Bisulﬁte Sequencing (RRBS)


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Genomics and R

Installing methylKit

In R terminal, install the package and the dependencies by typing the
following commands:
source("http://methylkit.googlecode.com/files/install.methylKit.R")
# installing the package with the
# dependencies
install.methylKit(ver = "0.5.6", dependencies = TRUE)

see
https://code.google.com/p/methylkit/#Installation
for more details on installing methylKit


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Flowchart for capabilities of methylKit

Aligned reads to the
Input option 1 genome
read.bismark()

Methylation % per
Input option 2 base
read()
Filtering by coverage

filterByCoverage()
Descriptive statistics
getMethylationStats()
getCoverageStats()
Window/region
based methylation
scores
Correlation regionCounts()
tileMethylCounts()
getCorrelation() Sample merge

unite()

Clustering and PCA
clusterSamples()
PCASamples()
Differential
methylation
calculateDiffMeth()

Annotation Visualization Coercion to
bedgraph() bioconductor and R Further analysis by
annotate.WithFeature()
and similar functions diffMethPerChr() objects user
as() getData()


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Reading the methylation data

Now methylKit is installed, we will load the package and start by
reading in the methylation call data. The methylation call files are
basically text files that contain percent methylation score per base. A
typical methylation call file looks like this:
## chrBase chr base strand coverage
## 1 chr20.12314 chr20 12314 F 21
## 2 chr20.12378 chr20 12378 R 13
## 3 chr20.12382 chr20 12382 R 13
## 4 chr20.12323 chr20 12323 F 21
## 5 chr20.55740 chr20 55740 R 53
## freqC freqT
## 1 95.24 4.76
## 2 76.92 23.08
## 3 38.46 61.54
## 4 85.71 14.29
## 5 67.92 32.08


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Most of the time bisulﬁte sequencing experiments have test and
control samples. The test samples can be from a disease tissue while
the control samples can be from a healthy tissue. You can read a set
of methylation call ﬁles that have test/control conditions giving
treatment vector option as follows:
library(methylKit)
file.list = list("test1.CpG.txt", "test2.CpG.txt",
"ctrl1.CpG.txt", "ctrl2.CpG.txt")
# read the files to a methylRawList object:
# myobj
myobj = read(file.list, sample.id = list("test1",
"test2", "ctrl1", "ctrl2"), assembly = "hg18",
treatment = c(1, 1, 0, 0), context = "CpG")


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Another way to read the methylation calls is directly from the
SAM files. The SAM files must be sorted and only SAM files from
Bismark aligner is supported at the moment. Check
read.bismark() function help to learn more about this.


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Quality check and basic features of the data
we can check the basic stats about the methylation data such as
coverage and percent methylation. We now have a methylRawList
object which contains methylation information per sample. The
following command prints out percent methylation statistics for
second sample: "test2"
getMethylationStats(myobj[[2]], plot = F, both.strands = F)

## methylation statistics per base
## summary:
## Min. 1st Qu. Median Mean 3rd Qu.
## 0.00 0.00 2.48 34.00 81.80
## Max.
## 100.00
## percentiles:
## 0% 10% 20% 30% 40%
## 0.000 0.000 0.000 0.000 0.000
## 50% 60% 70% 80% 90%
## 2.479 30.986 70.000 89.583 100.000
## 95% 99% 99.5% 99.9% 100%
## 100.000 100.000 100.000 100.000 100.000

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The following command plots the histogram for percent methylation
distribution.The ﬁgure below is the histogram and numbers on bars
denote what percentage of locations are contained in that bin.
Typically, percent methylation histogram should have two peaks on
both ends.
getMethylationStats(myobj[[2]], plot = T, both.strands = F)

Histogram of % CpG methylation
test2
52.9
80000
60000
Frequency

40000

13.4
20000

5.8
2.9 3.8
2.3 2.7
1.2 1.2 0.8 0.9 0.9 1.2 1 1.6 1 1.4 1.4 1.6 2
0

0 20 40 60 80 100

% methylation per base


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We can also plot the read coverage per base information in a similar
way. Experiments that are highly suffering from PCR duplication bias
will have a secondary peak towards the right hand side of the
histogram.
getCoverageStats(myobj[[2]], plot = T, both.strands = F)

Histogram of CpG coverage
test2
35000

22.2
30000

17.7
16.8
25000

14.4
13.9
20000

12.5
Frequency

15000
10000
5000

2

0.3 0 0 0 0 0 0 0 0 0 0
0

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

log10 of read coverage per base


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Filtering samples based on read coverage

It might be useful to ﬁlter samples based on coverage.The code
below ﬁlters a methylRawList and discards bases that have
coverage below 10X and also discards the bases that have more than
99.9th percentile of coverage in each sample.
filtered.myobj = filterByCoverage(myobj, lo.count = 10,
lo.perc = NULL, hi.count = NULL, hi.perc = 99.9)


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Sample Correlation
Uniting data sets

In order to do further analysis, we will need to get the bases covered
in all samples. The following function will merge all samples to one
object for base-pair locations that are covered in all samples.
meth = unite(myobj, destrand = FALSE)
# meth is a methylBase object

Let us take a look at the data content of methylBase object:
head(meth, 2)

## id chr start end
## 1 chr20.10100840 chr20 10100840 10100840
## 2 chr20.10100844 chr20 10100844 10100844
## strand coverage1 numCs1 numTs1 coverage2
## 1 + 29 2 27 11
## 2 + 29 2 27 11
## numCs2 numTs2 coverage3 numCs3 numTs3
## 1 0 11 43 2 41
## 2 1 10 42 2 40
## coverage4 numCs4 numTs4

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Sample Correlation
This function will either plot scatter plot and correlation coefﬁcients or
just print a correlation matrix.
getCorrelation(meth, plot = T)

CpG base correlation
0.0 0.4 0.8 0.0 0.4 0.8

test1

0.8
0.92 0.91 0.91

0.4
0.0
test2
0.8

0.89 0.89
y

0.4
0.0

0.0 0.4 0.8
ctrl1
0.8

0.8

0.8
x

0.97
y

y
0.4

0.4

0.4
0.0

0.0

0.0
0.0 0.4 0.8 0.0 0.4 0.8
ctrl2
0.8

0.8

0.8

x x
y

y

y
0.4

0.4

0.4
0.0

0.0

0.0

0.0 0.4 0.8 0.0 0.4 0.8 0.0 0.4 0.8

x x x

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Clustering your samples based on the methylation
proﬁles
methylKit can do hiearchical clustering.
clusterSamples(meth, dist = "correlation", method = "ward",
plot = TRUE)

CpG methylation clustering

0.35
0.30
0.25
0.20
Height

0.15
0.10
0.05
0.00

ctrl1

ctrl2

test1

test2

Samples
Distance method: "correlation"; Clustering method: "ward"

##
## Call:
## hclust(d = d, method Akalın, Altuna Analyzing RRBS methylation data with R
= HCLUST.METHODS[hclust.method])

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proﬁles
Principal Component Analysis (PCA) is another option. We can plot
PC1 (principal component 1) and PC2 (principal component 2) axis
and a scatter plot of our samples on those axis which will reveal how
they cluster.
PCASamples(meth)

CpG methylation PCA Analysis
150

test1
100
50
PC2

0

ctrl2
ctrl1
−50
−100

test2

−100 −50 0 50 100

PC1


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proﬁles
clustering multiple conditions

It is possible to cluster samples from more than 2 conditions. The
code below will not work since the tutorial does not have an example
dataset, but if you happen to work with multiple conditions you can
still cluster them in the way shown below.The members of the cluster
will be color coded based on the "treatment" option.
file.list = list("condA_sample1.txt", "condA_sample2.txt",
"condB_sample1.txt", "condB_sample1.txt",
"condC_sample1.txt", "condC_sample1.txt")
clist = read(file.list, sample.id = list("A1",
"A2", "B1", "B2", "C1", "C2"), assembly = "hg18",
treatment = c(2, 2, 1, 1, 0, 0), context = "CpG")
newMeth = unite(clist)
clusterSamples(newMeth)


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Getting differentially methylated bases

calculateDiffMeth() function is the main function to
calculate differential methylation.
Depending on the sample size per each set it will either use
Fisher’s exact or logistic regression to calculate P-values.
P-values will be adjusted to Q-values.

myDiff = calculateDiffMeth(meth)

calculateDiffMeth() can also use multiple cores for faster
calculation.
myDiff = calculateDiffMeth(meth, num.cores = 2)


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Getting differentially methylated bases

Following bit selects the bases that have q-value<0.01 and percent
methylation difference larger than 25%.
# get hyper methylated bases
myDiff25p.hyper = get.methylDiff(myDiff, difference = 25,
qvalue = 0.01, type = "hyper")
# get hypo methylated bases
myDiff25p.hypo = get.methylDiff(myDiff, difference = 25,
qvalue = 0.01, type = "hypo")
# get all differentially methylated bases
myDiff25p = get.methylDiff(myDiff, difference = 25,
qvalue = 0.01)


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Getting differentially methylated regions

methylKit can summarize methylation information over tiling
windows or over a set of predeﬁned regions (promoters, CpG islands,
introns, etc.) rather than doing base-pair resolution analysis.
# you might get warnings here, ignore them
tiles = tileMethylCounts(myobj, win.size = 1000,
step.size = 1000)

head(tiles[[1]], 2)

## id chr start end strand
## 1 chr20.12001.13000 chr20 12001 13000 *
## 2 chr20.55001.56000 chr20 55001 56000 *
## coverage numCs numTs
## 1 68 53 15
## 2 212 192 20


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Differential methylation events per chromosome
We can also visualize the distribution of hypo/hyper-methylated
bases/regions per chromosome using the following function.
# if plot=FALSE a list having per chromosome
# differentially methylation events will be
# returned
diffMethPerChr(myDiff, plot = FALSE, qvalue.cutoff = 0.01,
meth.cutoff = 25)

# if plot=TRUE a barplot will be plotted
diffMethPerChr(myDiff, plot = TRUE, qvalue.cutoff = 0.01,
meth.cutoff = 25)
% of hyper & hypo methylated regions per chromsome
qvalue<0.01 & methylation diff. >=25 %
hyper
hypo

chr22

chr21

chr20
0

2

4

6

8

10

% (percentage)


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Annotating differential methylation events
We can annotate our differentially methylated regions/bases based
on gene annotation. We need to read the gene annotation from a bed
ﬁle and annotate our differentially methylated regions with that
information. Similar gene annotation can be created using
GenomicFeatures package available from Bioconductor.
gene.obj <- read.transcript.features("subset.refseq.hg18.bed.txt")
# annotate differentially methylated Cs with
# promoter/exon/intron using annotation data
annotate.WithGenicParts(myDiff25p, gene.obj)

## summary of target set annotation with genic parts
## 8009 rows in target set
## --------------
## --------------
## percentage of target features overlapping with annotation :
## promoter exon intron intergenic
## 14.43 19.04 40.33 38.57
##
##
## percentage of target features overlapping with annotation (with pro
## promoter exonAkalın, Altuna
intron intergenic data with R
Analyzing RRBS methylation

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Similarly, we can read the CpG island annotation and annotate our
differentially methylated bases/regions with them.
# read the shores and flanking regions and
# name the flanks as shores and CpG islands
# as CpGi
cpg.obj = read.feature.flank("subset.cpgi.hg18.bed.txt",
feature.flank.name = c("CpGi", "shores"))
#
diffCpGann = annotate.WithFeature.Flank(myDiff25p,
cpg.obj$CpGi, cpg.obj$shores, feature.name = "CpGi",
flank.name = "shores")


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We can also read any BED ﬁle and annotate our differentially
methylated bases/regions with them.
# read the enhancer marker bed file
enh.b = read.bed("subset.enhancers.bed")
#

diffCpG.enh = annotate.WithFeature(myDiff25p,
enh.b, feature.name = "enhancers")


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After getting the annotation of differentially methylated regions, we
can get the distance to TSS and nearest gene name using the
getAssociationWithTSS function.
diffAnn = annotate.WithGenicParts(myDiff25p, gene.obj)

# target.row is the row number in myDiff25p
head(getAssociationWithTSS(diffAnn), 3)

## target.row dist.to.feature feature.name
## 427 1 -121458 NM_000214
## 428 2 271458 NR_038972
## 310 3 -1871 NM_018354
## feature.strand
## 427 -
## 428 -
## 310 -


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It is also desirable to get percentage/number of differentially
methylated regions that overlap with intron/exon/promoters
getTargetAnnotationStats(diffAnn, percentage = TRUE,
precedence = TRUE)

## promoter exon intron intergenic
## 14.43 14.11 32.89 38.57


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We can also plot the percentage of differentially methylated bases
overlapping with exon/intron/promoters
plotTargetAnnotation(diffAnn, precedence = TRUE,
main = "differential methylation annotation")

differential methylation annotation

promoter
exon
14 % intron
intergenic

14 %
33 %

39 %


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We can also plot the CpG island annotation the same way. The plot
below shows what percentage of differentially methylated bases are
on CpG islands, CpG island shores and other regions.
plotTargetAnnotation(diffCpGann, col = c("green",
"gray", "white"), main = "differential methylation annotation")

differential methylation annotation

CpGi
shores
other
23 %

22 %

55 %


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Regional analysis
We can also summarize methylation information over a set of deﬁned
regions such as promoters. The function below summarizes the
methylation information over a given set of promoter regions and
outputs a methylRaw or methylRawList object depending on the
input.
promoters = regionCounts(myobj, gene.obj$promoters)

head(promoters[[1]], 3)

## id chr start
## 1 chr20.33505636.33507636.NA chr20 33505636
## 2 chr20.8060295.8062295.NA chr20 8060295
## 3 chr20.3137055.3139055.NA chr20 3137055
## end strand coverage numCs numTs
## 1 33507636 + 727 4 723
## 2 8062295 + 954 15 939
## 3 3139055 + 553 2 551


Introduction
R Basics
Genomics and R

methylKit convenience functions

Most methylKit objects (methylRaw,methylBase and methylDiff)
can be coerced to GRanges objects from GenomicRanges package.
Coercing methylKit objects to GRanges will give users additional
ﬂexiblity when customising their analyses.
class(meth)
as(meth, "GRanges")
class(myDiff)
as(myDiff, "GRanges")


Introduction
R Basics
Genomics and R


We can also select rows from methylRaw, methylBase and methylDiff
objects with "select" function. An appropriate methylKit object will be
returned as a result of "select" function.
select(meth, 1:5) # select first 5 rows
# OR
meth[1:5, ] # select first 5 rows


Introduction
R Basics
Genomics and R


Another useful function is getData(). You can use this function to
extract data frames from methylRaw, methylBase and methylDiff
objects.
# get data frame and show first rows
head(getData(meth))
# get data frame and show first rows
head(getData(myDiff))


Introduction
R Basics
Genomics and R

Further information

information on RRBS and alike
RRBS http://www.nature.com/nprot/journal/v6/n4/
abs/nprot.2010.190.html
Agilent methyl-seq capture http:
//www.halogenomics.com/sureselect/methyl-seq

Some of the aligners and pipelines
Bismark http://www.bioinformatics.bbsrc.ac.uk/
projects/bismark/
AMP pipeline http://code.google.com/p/amp-errbs/
BSMAP http://code.google.com/p/bsmap/
other aligners and methods reviewed here
http://www.nature.com/nmeth/journal/v9/n2/full/
nmeth.1828.html

Introduction
R Basics
Genomics and R

Further information

More info on methylKit
The webpage for the package is
http://methylkit.googlecode.com/
Genome Biology paper for the package is at
http://genomebiology.com/2012/13/10/R87
Blog posts related to methylKit are at http:
//zvfak.blogspot.com/search/label/methylKit
The slides for this presentation is here:
methylKitTutorialSlides_2013.pdf
The tutorial for the 2012 EpiWorkshop is here:
methylKitTutorial_feb2012.pdf


DNA methylation analysis in R

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