1 Introduction

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. shinyMethyl is an interactive visualization tool for Illumina 450K methylation array data based on the packages minfi and shiny.

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.

2 Example dataset

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 first shinyMethylSet object (see Section 3 for the definition of a shinyMethylSet object) was created from the raw data (no normalization) and is stored under the name summary.tcga.raw.rda; the second 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).

To launch shinyMethyl with this TCGA dataset, simply type the following commands in a fresh R session:

runShinyMethyl(summary.tcga.raw, summary.tcga.norm)

The interactive interface will take a few seconds to be launched in your default HTML browser.

3 Creating your own dataset visualization

In this section, we describe how to launch an interactive visualization for your methylation dataset.

3.1 Step 1: creating a RGChannelSet object with minfi

An RGChannelSet is an object defined in minfi containing 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 minfi and 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 minfiData.


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 RGChannelSet using read.450k.exp:

RGSet <- read.450k.exp(base = baseDir, targets = targets)

The function pData in minfi allows to see the phenotype data of the samples:

pd <- pData(RGSet)

3.2 Step 2: creating a shinyMethylSet object

From the RGChannelSet created in the previous section, we create a shinyMethylSet by using the command shinySummarize

myShinyMethylSet <- shinySummarize(RGSet)

This is a small object containing all of the necessary information extracted from a RGChannelSet to launch shinyMethyl.

3.3 Step 3: launching the interactive shiny interface

To launch a shinyMethyl session, simply pass your shinyMethylSet object to the runShinyMethyl function as follows:


4 How to use the different panels

The different figures at the end of the vignette explain how to use each of the shinyMethyl panels.

5 Advanced option: visualization of normalized data

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 implemented in minfi (this function returns a GenomicRatioSet 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 runShinyMethyl with the first argument being the shinyMethylSet extracted from the raw data and the second argument being the shinyMethylSet extracted from the data as follows:

runShinyMethyl(summary, summary.norm)

6 What does a shinyMethylSet contain?

A 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 operator @. The slot names can be obtained with the function slotNames as follows:

##  [1] "sampleNames"     "phenotype"       "mQuantiles"      "betaQuantiles"  
##  [5] "methQuantiles"   "unmethQuantiles" "cnQuantiles"     "greenControls"  
##  [9] "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.

7 Figures

7.1 Quality control figure

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 implemented in 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).}