Up to now, more than 10,000 methylation samples from the state-of-the-art
450K microarray have been made available through The Cancer Genome Atlas
portal (The Cancer Genome Atlas 2014) and the Gene Expression Omnibus (GEO) (Edgar, Domrachev, and Lash 2002).
Large-scale comparison studies, for instance between cancers or tissues,
become possible epigenome-widely. These large studies often require a
substantial amount of time spent on preprocessing the data and performing quality control. For such studies, it is not rare to encounter significant batch effects, and those can have a dramatic impact on the validity of the biological results (Leek et al. 2010), (Harper, Peters, and Gamble 2013). With that in mind, we developed
shinyMethyl to make the preprocessing of large 450K
datasets intuitive, enjoyable and reproducible.
is an interactive visualization tool for Illumina 450K methylation array
data based on the packages minfi and
A few mouse clicks allow the user to appreciate insightful
biological inter-array differences on a large scale. The goal of
shinyMethyl is two-fold: (1) summarize a high-dimensional 450K
array experiment into an exportable small-sized R object and (2)
launch an interactive visualization tool for quality control assessment
as well as exploration of global methylation patterns associated
with different phenotypes.
To take a quick look at how the interactive interface of
shinyMethyl works, we have included an example dataset in
the companion package shinyMethylData.
The dataset contains the extracted data of 369 Head and Neck cancer
samples downloaded from The
Cancer Genome Atlas (TCGA) data portal (The Cancer Genome Atlas 2014): 310 tumor samples,
50 matched normals and 9 replicates of a control cell line. The
shinyMethylSet object (see Section 3 for the definition
shinyMethylSet object) was created from the raw data (no
normalization) and is stored under the name
shinyMethylSet object was created from a
GenomicRatioSet containing the normalized data and the file is stored
under the name
summary.tcga.norm.rda. The samples were normalized
using functional normalization, a preprocessing procedure that we recently
developed for heterogeneous methylation data (Fortin et al. 2014).
shinyMethyl with this TCGA dataset, simply
type the following commands in a fresh R session:
The interactive interface will take a few seconds to be launched in your default HTML browser.
In this section, we describe how to launch an interactive visualization for your methylation dataset.
RGChannelSet is an object defined in
the raw intensities of the green and red channels of your 450K experiment.
To create an
RGChannelSet, you will need to have the raw files of
the experiment with extension .IDAT (we refer to those as .IDAT files).
In case you do not have these files, you might want to ask your collaborators
or your processing core if they have those. You absolutely need them to both
use the packages
shinyMethyl. The vignette in
minfi describes carefully how to read the data in for different
scenarios and how to construct an
RGChannelSet. Here, we show a
quick way to create an
RGChannelSet from the .IDAT files
contained in the package
We need to tell R which directory contains the .IDAT files and the experiment sheet:
baseDir <- system.file("extdata", package = "minfiData")
# baseDir <- "/home/yourDirectoryPath"
We also need to read in the experiment sheet:
targets <- read.450k.sheet(baseDir)
Finally, we construct the
RGSet <- read.450k.exp(base = baseDir, targets = targets)
minfi allows to see the phenotype
data of the samples:
pd <- pData(RGSet)
RGChannelSet created in the
previous section, we create a
shinyMethylSet by using the command
myShinyMethylSet <- shinySummarize(RGSet)
This is a small object containing all of the necessary information extracted from a RGChannelSet to launch shinyMethyl.
To launch a
shinyMethyl session, simply pass your
shinyMethylSet object to the
The different figures at the end of the vignette explain how to use each of the
shinyMethyl also offers the possibility to visualize normalized
data that are stored in a
GenomicRatioSet object. For instance,
suppose we normalize the data by using the quantile normalization algorithm
minfi (this function returns a
object by default):
GRSet.norm <- preprocessQuantile(RGSet)
We can then create two separate
shinyMethylSet objects corresponding
to the raw and normalized data respectively:
summary <- shinySummarize(RGSset)
summary.norm <- shinySummarize(GRSet.norm)
To launch the
shinyMethyl interface, use
the first argument being the
shinyMethylSet extracted from the raw data
and the second argument being the
shinyMethylSet extracted from the
data as follows:
shinyMethylSet object contains several summary data from a 450K
experiment: the names of the samples, a data frame for the phenotype, a
list of quantiles for the M and Beta values, a list of quantiles for the
methylated and unmethylated channels intensities and a list of quantiles
for the copy numbers, the green and red intensities of different control
probes, and the principal component analysis (PCA) results performed on the
Beta values. One can access the different summaries by using the slot
@. The slot names can be obtained with the function
slotNames as follows:
##  "sampleNames" "phenotype" "mQuantiles" "betaQuantiles"
##  "methQuantiles" "unmethQuantiles" "cnQuantiles" "greenControls"
##  "redControls" "pca" "originObject" "array"
For instance, one can retrieve the phenotype by
## gender caseControlStatus plate position
## 5775446049_R01C01 MALE Normal 577544 R01C01
## 5775446049_R01C02 FEMALE Normal 577544 R01C02
## 5775446049_R02C01 MALE Tumor 577544 R02C01
## 5775446049_R02C02 MALE Tumor 577544 R02C02
## 5775446049_R03C01 MALE Tumor 577544 R03C01
## 5775446049_R03C02 FEMALE Tumor 577544 R03C02
shinyMethyl also contain different accessor functions to
access the slots. Please see the manual for more information.
This is an example of interactive visualization and quality control assessment.
The three plots react simultaneously to the user mouse clicks and selected
samples are represented in black. In this scenario, colors represent batch,
but colors can be chosen to reflect the phenotype of the samples as well via
the left-hand-side panel. The three different plots are: A) Plot of the quality
controls probes reacting to the left-hand-side panel; the current plot shows
the bisulfite conversion probes intensities. B) Quality control plot as
minfi: the median intensity of the M channel against the
median intensity of the U channel. Samples with bad quality would cluster
away from the cloud shown in the current plot. For this dataset, all samples
look good. C) Densities of the methylation intensities (can be chosen to be
Beta-values or M-values, and can be chosen by probe type). The current plot
shows the M-value densities for Infinium I probes, for the raw data.
The dashed and solid lines in black correspond to the two samples selected
by the user and match to the dots circled in black in the left-hand plots.
The left-hand-side panel allows users to select different tuning parameters
for the plots, as well as different phenotypes for the colors. The user can
click on the samples that seem to have low quality, and can download the
names of the samples in a csv file for further analysis
(not shown in the screenshot).}