missMethyl: Analysing Illumina HumanMethylation BeadChip Data

In missMethyl: Analysing Illumina HumanMethylation BeadChip Data

Introduction

The r BiocStyle::Biocpkg("missMethyl") package contains functions to analyse methylation data from Illumina's HumanMethylation450 and MethylationEPIC beadchip. These arrays are a cost-effective alternative to whole genome bisulphite sequencing, and as such are widely used to profile DNA methylation. Specifically, r BiocStyle::Biocpkg("missMethyl") contains functions to perform SWAN normalisation [@Maksimovic2012],perform differential methylation analysis using RUVm [@Maksimovic2015], differential variability analysis [@Phipson2014] and gene set enrichment analysis [@Phipson2016]. As our lab's research into specialised analyses of these arrays continues we anticipate that the package will be updated with new functions.

Raw data files are in IDAT format, which can be read into R using the r BiocStyle::Biocpkg("minfi") package [@Aryee2014]. Statistical analyses are usually performed on M-values, and $\beta$ values are used for visualisation, both of which can be extracted from MethylSet data objects, which is a class of object created by r BiocStyle::Biocpkg("minfi"). For detecting differentially variable CpGs we recommend that the analysis is performed on M-values. All analyses described here are performed at the CpG site level.

Reading data into R

We will use the data in the r BiocStyle::Biocpkg("minfiData") package to demonstrate the functions in r BiocStyle::Biocpkg("missMethyl"). The example dataset has 6 samples across two slides. There are 3 cancer samples and 3 normal sample. The sample information is in the targets file. An essential column in the targets file is the Basename column which tells us where the idat files to be read in are located. The R commands to read in the data are taken from the r BiocStyle::Biocpkg("minfi") User's Guide. For additional details on how to read the IDAT files into R, as well as information regarding quality control please refer to the r BiocStyle::Biocpkg("minfi") User's Guide.

library(missMethyl)
library(limma)
library(minfi)
library(minfiData)

baseDir <- system.file("extdata", package = "minfiData")
targets <- read.metharray.sheet(baseDir)
targets[,1:9]
targets[,10:12]
rgSet <- read.metharray.exp(targets = targets)

The data is now an RGChannelSet object and needs to be normalised and converted to a MethylSet object.

Subset-quantile within array normalization (SWAN)

SWAN (subset-quantile within array normalization) is a within-array normalization method for Illumina 450k & EPIC BeadChips. Technical differencs have been demonstrated to exist between the Infinium I and Infinium II assays on a single Illumina HumanMethylation array [@Bibikova2011, @Dedeurwaerder2011]. Using the SWAN method substantially reduces the technical variability between the assay designs whilst maintaining important biological differences. The SWAN method makes the assumption that the number of CpGs within the 50bp probe sequence reflects the underlying biology of the region being interrogated. Hence, the overall distribution of intensities of probes with the same number of CpGs in the probe body should be the same regardless of assay type. The method then uses a subset quantile normalization approach to adjust the intensities of each array [@Maksimovic2012].

SWAN can take a MethylSet, RGChannelSet or MethyLumiSet as input. It should be noted that, in order to create the normalization subset, SWAN randomly selects Infinium I and II probes that have one, two and three underlying CpGs; as such, we recommend using set.seed before to ensure that the normalized intensities will be identical, if the normalization is repeated.

The technical differences between Infinium I and II assay designs can result in aberrant $\beta$ value distributions (Figure \@ref(fig:betasByType), panel "Raw"). Using SWAN corrects for the technical differences between the Infinium I and II assay designs and produces a smoother overall $\beta$ value distribution (Figure \@ref(fig:betasByType), panel "SWAN").

mSet <- preprocessRaw(rgSet)

mSetSw <- SWAN(mSet,verbose=TRUE)

par(mfrow=c(1,2), cex=1.25)
densityByProbeType(mSet[,1], main = "Raw")
densityByProbeType(mSetSw[,1], main = "SWAN")

Filter out poor quality probes

Poor quality probes can be filtered out based on the detection p-value. For this example, to retain a CpG for further analysis, we require that the detection p-value is less than 0.01 in all samples.

detP <- detectionP(rgSet)
keep <- rowSums(detP < 0.01) == ncol(rgSet)
mSetSw <- mSetSw[keep,]

Extracting $\beta$ and M-values

Now that the data has been SWAN normalised we can extract $\beta$ and M-values from the object. We prefer to add an offset to the methylated and unmethylated intensities when calculating M-values, hence we extract the methylated and unmethylated channels separately and perform our own calculation. For all subsequent analyses we use a random selection of 20000 CpGs to reduce computation time.

set.seed(10)
mset_reduced <- mSetSw[sample(1:nrow(mSetSw), 20000),]
meth <- getMeth(mset_reduced)
unmeth <- getUnmeth(mset_reduced)
Mval <- log2((meth + 100)/(unmeth + 100))
beta <- getBeta(mset_reduced)
dim(Mval)

par(mfrow=c(1,1))
plotMDS(Mval, labels=targets$Sample_Name, col=as.integer(factor(targets$status)))
legend("topleft",legend=c("Cancer","Normal"),pch=16,cex=1.2,col=1:2)

An MDS plot (Figure \@ref(fig:mdsplot)) is a good sanity check to make sure samples cluster together according to the main factor of interest, in this case, cancer and normal.

Testing for differential methylation using limma

To test for differential methylation we use the r BiocStyle::Biocpkg("limma") package [@Smyth2005], which employs an empirical Bayes framework based on Guassian model theory. First we need to set up the design matrix. There are a number of ways to do this, the most straightforward is directly from the targets file. There are a number of variables, with the status column indicating cancer/normal samples. From the person column of the targets file, we see that the cancer/normal samples are matched, with 3 individuals each contributing both a cancer and normal sample. Since the r BiocStyle::Biocpkg("limma") model framework can handle any experimental design which can be summarised by a design matrix, we can take into account the paired nature of the data in the analysis. For more complicated experimental designs, please refer to the r BiocStyle::Biocpkg("limma") User's Guide.

group <- factor(targets$status,levels=c("normal","cancer"))
id <- factor(targets$person)
design <- model.matrix(~id + group)
design

Now we can test for differential methylation using the lmFit and eBayes functions from r BiocStyle::Biocpkg("limma"). As input data we use the matrix of M-values.

fit.reduced <- lmFit(Mval,design)
fit.reduced <- eBayes(fit.reduced, robust=TRUE)

The numbers of hyper-methylated (1) and hypo-methylated (-1) can be displayed using the decideTests function in r BiocStyle::Biocpkg("limma") and the top 10 differentially methylated CpGs for cancer versus normal extracted using topTable.

summary(decideTests(fit.reduced))
top<-topTable(fit.reduced,coef=4)
top

Note that since we performed our analysis on M-values, the logFC and AveExpr columns are computed on the M-value scale. For interpretability and visualisation we can look at the $\beta$ values. The $\beta$ values for the top 4 differentially methylated CpGs shown in Figure \@ref(fig:top4).

cpgs <- rownames(top)
par(mfrow=c(2,2))
for(i in 1:4){
stripchart(beta[rownames(beta)==cpgs[i],]~design[,4],method="jitter",
group.names=c("Normal","Cancer"),pch=16,cex=1.5,col=c(4,2),ylab="Beta values",
vertical=TRUE,cex.axis=1.5,cex.lab=1.5)
title(cpgs[i],cex.main=1.5)
}

Removing unwanted variation when testing for differential methylation

Like other platforms, 450k array studies are subject to unwanted technical variation such as batch effects and other, often unknown, sources of variation. The adverse effects of unwanted variation have been extensively documented in gene expression array studies and have been shown to be able to both reduce power to detect true differences and to increase the number of false discoveries. As such, when it is apparent that data is significantly affected by unwanted variation, it is advisable to perform an adjustment to mitigate its effects.

r BiocStyle::Biocpkg("missMethyl") provides a r BiocStyle::Biocpkg("limma") inspired interface to functions from the CRAN package r BiocStyle::CRANpkg("ruv"), which enable the removal of unwanted variation when performing a differential methylation analysis [@Maksimovic2015].

RUVfit uses the RUV-inverse method by default, as this does not require the user to specify a $k$ parameter. The ridged version of RUV-inverse is also available by setting method = rinv. The RUV-2 and RUV-4 functions can also be used by setting method = ruv2 or method = ruv4, respectively, and specifying an appropriate value for k (number of components of unwanted variation to remove) where $0 \leq k < no. samples$.

All of the methods rely on negative control features to accurately estimate the components of unwanted variation. Negative control features are probes/genes/etc. that are known a priori to not truly be associated with the biological factor of interest, but are affected by unwanted variation. For example, in a microarray gene expression study, these could be house-keeping genes or a set of spike-in controls. Negative control features are extensively discussed in Gagnon-Bartsch and Speed [-@Gagnon-Bartsch2012] and Gagnon-Bartsch et al. [-@Gagnon-Bartsch2013]. Once the unwanted factors are accurately estimated from the data, they are adjusted for in the linear model that describes the differential analysis.

If the negative control features are not known a priori, they can be identified empirically. This can be achieved via a 2-stage approach, RUVm. Stage 1 involves performing a differential methylation analysis using RUV-inverse (by default) and the 613 Illumina negative controls (INCs) as negative control features. This will produce a list of CpGs ranked by p-value according to their level of association with the factor of interest. This list can then be used to identify a set of empirical control probes (ECPs), which will capture more of the unwanted variation than using the INCs alone. ECPs are selected by designating a proportion of the CpGs least associated with the factor of interest as negative control features; this can be done based on either an FDR cut-off or by taking a fixed percentage of probes from the bottom of the ranked list. Stage 2 involves performing a second differential methylation analysis on the original data using RUV-inverse (by default) and the ECPs. For simplicity, we are ignoring the paired nature of the cancer and normal samples in this example.

# get M-values for ALL probes
meth <- getMeth(mSet)
unmeth <- getUnmeth(mSet)
M <- log2((meth + 100)/(unmeth + 100))
# setup the factor of interest
grp <- factor(targets$status, labels=c(0,1))
# extract Illumina negative control data
INCs <- getINCs(rgSet)
head(INCs)
# add negative control data to M-values
Mc <- rbind(M,INCs)
# create vector marking negative controls in data matrix
ctl1 <- rownames(Mc) %in% rownames(INCs)
table(ctl1)
rfit1 <- RUVfit(Y = Mc, X = grp, ctl = ctl1) # Stage 1 analysis
rfit2 <- RUVadj(Y = Mc, fit = rfit1)

Now that we have performed an initial differential methylation analysis to rank the CpGs with respect to their association with the factor of interest, we can designate the CpGs that are least associated with the factor of interest based on FDR-adjusted p-value as ECPs.

top1 <- topRUV(rfit2, num=Inf, p.BH = 1)
head(top1)
ctl2 <- rownames(M) %in% rownames(top1[top1$p.BH_X1.1 > 0.5,])
table(ctl2)

We can then use the ECPs to perform a second differential methylation with RUV-inverse, which is adjusted for the unwanted variation estimated from the data.

# Perform RUV adjustment and fit
rfit3 <- RUVfit(Y = M, X = grp, ctl = ctl2) # Stage 2 analysis
rfit4 <- RUVadj(Y = M, fit = rfit3)
# Look at table of top results
topRUV(rfit4)

Alternative approach for RUVm stage 1

If the number of samples in your experiment is greater than the number of Illumina negative controls on the array platform used - 613 for 450k, 411 for EPIC - stage 1 of RUVm will not work. In such cases, we recommend performing a standard r BiocStyle::Biocpkg("limma") analysis in stage 1.

# setup design matrix
des <- model.matrix(~grp)
des
# limma differential methylation analysis
lfit1 <- lmFit(M, design=des)
lfit2 <- eBayes(lfit1) # Stage 1 analysis
# Look at table of top results
topTable(lfit2)

The results of this can then be used to define ECPs for stage 2, as in the previous example.

topl1 <- topTable(lfit2, num=Inf)
head(topl1)
ctl3 <- rownames(M) %in% rownames(topl1[topl1$adj.P.Val > 0.5,])
table(ctl3)

We can then use the ECPs to perform a second differential methylation with RUV-inverse as before.

# Perform RUV adjustment and fit
rfit5 <- RUVfit(Y = M, X = grp, ctl = ctl3) # Stage 2 analysis
rfit6 <- RUVadj(Y = M, fit = rfit5)
# Look at table of top results
topRUV(rfit6)

Visualising the effect of RUVm adjustment

To visualise the effect that the RUVm adjustment is having on the data, using an MDS plot for example, the getAdj function can be used to extract the adjusted values from the RUVm fit object produced by RUVfit. NOTE: The adjusted values should only be used for visualisations - it is NOT recommended that they are used in any downstream analysis.

Madj <- getAdj(M, rfit5) # get adjusted values

The MDS plots below show how the relationship between the samples changes with and without RUVm adjustment. RUVm reduces the distance between the samples in each group by removing unwanted variation. It can be useful to examine this type of plot when trying to decide on the best set of ECPs or to help select the optimal value of $k$, if using RUV-4 or RUV-2.

par(mfrow=c(1,2))
plotMDS(M, labels=targets$Sample_Name, col=as.integer(factor(targets$status)),
        main="Unadjusted", gene.selection = "common")
legend("right",legend=c("Cancer","Normal"),pch=16,cex=1,col=1:2)
plotMDS(Madj, labels=targets$Sample_Name, col=as.integer(factor(targets$status)),
        main="Adjusted: RUV-inverse", gene.selection = "common")
legend("topright",legend=c("Cancer","Normal"),pch=16,cex=1,col=1:2)

To illustrate how the getAdj function can be used to help select an appropriate value for $k$, we will run the second stage of the RUVm analysis using RUV-4 with two different $k$ values.

# Use RUV-4 in stage 2 of RUVm with k=1 and k=2
rfit7 <- RUVfit(Y = M, X = grp, ctl = ctl3,
                method = "ruv4", k=1) # Stage 2 with RUV-4, k=1
rfit9 <- RUVfit(Y = M, X = grp, ctl = ctl3,
                method = "ruv4", k=2) # Stage 2 with RUV-4, k=2
# get adjusted values
Madj1 <- getAdj(M, rfit7)
Madj2 <- getAdj(M, rfit9)

The following MDS plots show how the relationship between the samples changes from the unadjusted data to data adjusted with RUV-inverse and RUV-4 with two different $k$ values. For this small dataset, RUV-inverse appears to be removing far too much variation as we can see the samples in each group are completely overlapping. Using RUV-4 and choosing a smaller value for $k$ produces more sensible results.

par(mfrow=c(2,2))
plotMDS(M, labels=targets$Sample_Name, col=as.integer(factor(targets$status)),
        main="Unadjusted", gene.selection = "common")
legend("top",legend=c("Cancer","Normal"),pch=16,cex=1,col=1:2)
plotMDS(Madj, labels=targets$Sample_Name, col=as.integer(factor(targets$status)),
        main="Adjusted: RUV-inverse", gene.selection = "common")
legend("topright",legend=c("Cancer","Normal"),pch=16,cex=1,col=1:2)
plotMDS(Madj1, labels=targets$Sample_Name, col=as.integer(factor(targets$status)),
        main="Adjusted: RUV-4, k=1", gene.selection = "common")
legend("bottom",legend=c("Cancer","Normal"),pch=16,cex=1,col=1:2)
plotMDS(Madj2, labels=targets$Sample_Name, col=as.integer(factor(targets$status)),
        main="Adjusted: RUV-4, k=2", gene.selection = "common")
legend("bottomright",legend=c("Cancer","Normal"),pch=16,cex=1,col=1:2)

More information about the various RUV methods can be found at http://www-personal.umich.edu/~johanngb/ruv/, including links to all relevant publications. Further examples of RUV analyses, with code, can be found at https://github.com/johanngb/ruv-useR2018. The tutorials demonstrate how the various plotting functions available in the r BiocStyle::CRANpkg("ruv") package (which are not covered in this vignette) can be used to select sensible parameters and assess if the adjustment is "helping" your analysis.

Testing for differential variability (DiffVar)

Methylation data

Rather than testing for differences in mean methylation, we may be interested in testing for differences between group variances. For example, it has been hypothesised that highly variable CpGs in cancer are important for tumour progression [@Hansen2011]. Hence we may be interested in CpG sites that are consistently methylated in the normal samples, but variably methylated in the cancer samples.

In general we recommend at least 10 samples in each group for accurate variance estimation, however for the purpose of this vignette we perform the analysis on 3 vs 3. In this example, we are interested in testing for differential variability in the cancer versus normal group.

An important note on the coef parameter: please always explicitly state which columns of design matrix correspond to the groups that you are interested in testing for differential variability. The default setting for coef is to include all columns of the design matrix when calculating the Levene residuals, which is not suitable for when there are additional variables that need to be taken into account in the linear model. To avoid misspecification of the model, it is best to explicitly state the coef parameter. The additional nuisance or confounding variables will still be taken into account in the linear modelling step, however we find that including them when calculating Levene residuals often removes all the (possibly interesting) variation in the data.

When the design matrix includes an intercept term, the coef parameter must include both the intercept and groups of interest. Consider the design matrix that was used when performing the limma analysis:

design

The first column of the design matrix is the intercept term, and the fourth column tells us which samples are cancer and normal samples. The 2nd and 3rd columns correspond to the ID parameter, which is not interesting in terms of finding differentially variable CpGs, but may be important to include in the linear model. Hence for this example we would specify coef = c(1,4) in the call to varFit.

For methylation data, the varFit function will take either a matrix of M-values, $\beta$ values or a MethylSet object as input. If $\beta$ values are supplied, a logit transformation is performed. Note that as a default, varFit uses the robust setting in the r BiocStyle::Biocpkg("limma") framework, which requires the use of the r BiocStyle::CRANpkg("statmod") package.

fitvar <- varFit(Mval, design = design, coef = c(1,4))

The numbers of hyper-variable (1) and hypo-variable (-1) genes in cancer vs normal can be obtained using decideTests. In the cancer vs normal context, we would expect to see more variability in methylation in cancer compared to normal (i.e. hyper-variable).

summary(decideTests(fitvar))
topDV <- topVar(fitvar, coef=4)
topDV

An alternate parameterisation of the design matrix that does not include an intercept term can also be used (i.e. a cell means model), and specific contrasts tested with contrasts.varFit. Here we specify the design matrix such that the first two columns correspond to the normal and cancer groups, respectively. Note that we now specify coef=c(1,2).

design2 <- model.matrix(~0+group+id)
fitvar.contr <- varFit(Mval, design=design2, coef=c(1,2))
contr <- makeContrasts(groupcancer-groupnormal,levels=colnames(design2))
fitvar.contr <- contrasts.varFit(fitvar.contr,contrasts=contr)

The results are identical to before.

summary(decideTests(fitvar.contr))
topVar(fitvar.contr,coef=1)

The $\beta$ values for the top 4 differentially variable CpGs can be seen in Figure \@ref(fig:top4DV).

cpgsDV <- rownames(topDV)
par(mfrow=c(2,2))
for(i in 1:4){
stripchart(beta[rownames(beta)==cpgsDV[i],]~design[,4],method="jitter",
group.names=c("Normal","Cancer"),pch=16,cex=1.5,col=c(4,2),ylab="Beta values",
vertical=TRUE,cex.axis=1.5,cex.lab=1.5)
title(cpgsDV[i],cex.main=1.5)
}

RNA-Seq expression data

Testing for differential variability in expression data is straightforward if the technology is gene expression microarrays. The matrix of expression values can be supplied directly to the varFit function. For RNA-Seq data, the mean-variance relationship that occurs in count data needs to be taken into account. In order to deal with this issue, we apply a voom transformation [@Law2014] to obtain observation weights, which are then used in the linear modelling step. For RNA-Seq data, the varFit function will take a DGElist object as input.

To demonstrate this, we use data from the r BiocStyle::Biocpkg("tweeDEseqCountData") package. This data is part of the International HapMap project, consisting of RNA-Seq profiles from 69 unrelated Nigerian individuals [@Pickrell2010]. The only covariate is gender, so we can look at differentially variable expression between males and females. We follow the code from the r BiocStyle::Biocpkg("limma") vignette to read in and process the data before testing for differential variability.

First we load up the data and extract the relevant information.

library(tweeDEseqCountData)
data(pickrell1)
counts<-exprs(pickrell1.eset)
dim(counts)
gender <- pickrell1.eset$gender
table(gender)
rm(pickrell1.eset)
data(genderGenes)
data(annotEnsembl63)
annot <- annotEnsembl63[,c("Symbol","Chr")]
rm(annotEnsembl63)

We now have the counts, gender of each sample and annotation (gene symbol and chromosome) for each Ensemble gene. We can form a DGElist object using the r BiocStyle::Biocpkg("edgeR") package.

library(edgeR)
y <- DGEList(counts=counts, genes=annot[rownames(counts),])

We filter out lowly expressed genes by keeping genes with at least 1 count per million reads in at least 20 samples, as well as genes that have defined annotation. Finally we perform scaling normalisation.

isexpr <- rowSums(cpm(y)>1) >= 20
hasannot <- rowSums(is.na(y$genes))==0
y <- y[isexpr & hasannot,,keep.lib.sizes=FALSE]
dim(y)
y <- calcNormFactors(y)

We set up the design matrix and test for differential variability.

design.hapmap <- model.matrix(~gender)
fitvar.hapmap <- varFit(y, design = design.hapmap, coef=c(1,2))
fitvar.hapmap$genes <- y$genes

We can display the results of the test:

summary(decideTests(fitvar.hapmap))
topDV.hapmap <- topVar(fitvar.hapmap,coef=ncol(design.hapmap))
topDV.hapmap

The log counts per million for the top 4 differentially variable genes can be seen in Figure \@ref(fig:top4DVhapmap).

genesDV <- rownames(topDV.hapmap)
par(mfrow=c(2,2))
for(i in 1:4){
stripchart(cpm(y,log=TRUE)[rownames(y)==genesDV[i],]~design.hapmap[,ncol(design.hapmap)],method="jitter",
group.names=c("Female","Male"),pch=16,cex=1.5,col=c(4,2),ylab="Log counts per million",
vertical=TRUE,cex.axis=1.5,cex.lab=1.5)
title(genesDV[i],cex.main=1.5)
}

Gene ontology analysis

Once a differential methylation or differential variability analysis has been performed, it may be of interest to know which gene pathways are targeted by the significant CpG sites. Geeleher et al. [@Geeleher2013] showed there is a severe bias when performing gene ontology analysis with methylation data. This is due to the fact that there are differing numbers of probes per gene on several different array technologies. For the Illumina Infinium HumanMethylation450 array the number of probes per gene ranges from 1 to 1299, with a median of 15 probes per gene. For the EPIC array, the range is 1 to 1487, with a median of 20 probes per gene. This means that when annotating CpG sites to genes, a gene is more likely to be identified as differentially methylated if there are many CpG sites associated with the gene. We refer to this source of bias as "probe number bias".

One way to take into account this selection bias is to model the relationship between the number of probes per gene and the probability of being differentially methylated. This can be performed by adapting the r BiocStyle::Biocpkg("goseq") method of Young et al. [@Young2010]. Each gene then has a prior probability associated with it, and a modified version of a hypergeometric test can be performed, testing for over-representation of the selected genes in each gene set. The r BiocStyle::Biocpkg("BiasedUrn") package can be used to obtain p-values from the Wallenius' noncentral hypergeometric distribution, taking into account the odds of differential methylation for each gene set. Note that the r BiocStyle::Biocpkg("BiasedUrn") package can occassionally return p-values of 0. For the gene sets where a p-value of exactly zero is outputted we perform a hypergeometric test, which ensures non-zero p-values and hence false discovery rates.

We have recently uncovered a new source of bias in gene set testing for methylation array data [@Maksimovic2020.08.24.265702] that we refer to as "multi-gene bias". This second source of bias arises due to the fact that around 10% of gene-annotated CpGs are annotated to more than one gene, violating assumptions of independently measured genes. This can lead to some GO categories being identified as significantly enriched as they contain genes with methylation measurements from shared CpGs. This can occur for large gene families such as the protocadherin gamma gene cluster which happen to be over-represented in the GO category "GO:0007156: homophilic cell adhesion via plasma membrane adhesion molecules". This is now taken into account in the gene set testing functions in r BiocStyle::Biocpkg("missMethyl").

We have developed methods for both CpG and region level analyses. The gometh function performs gene set testing on GO categories or KEGG pathways for significant CpG sites. The gsameth function is a more generalised gene set testing function which can take as input a list of user specified gene sets. Note that for gsameth, the format for the gene ids for each gene in the gene set needs to be Entrez Gene IDs. For example, the entire curated gene set list (C2) from the Broad's Molecular Signatures Database can be specified as input. The R version of these lists can be downloaded from http://bioinf.wehi.edu.au/software/MSigDB/index.html. Both functions take a vector of significant CpG probe names as input. For a region level analysis, using the r BiocStyle::Biocpkg("DMRcate") package, for example, goregion and gsaregion can perform gene set enrichment analysis using the ranged object as input.

NOTE ON UPDATES IN SEPTEMBER 2020: We have added new functionality to the gene set testing functions to allow the user to limit the input CpGs to specific genomic features of interest using the genomic.features argument. This is based on the annotation information in the manifest files for the 450K and EPIC arrays. Possible values are ""ALL", "TSS200", "TSS1500", "Body", "1stExon", "3'UTR", "5'UTR" and "ExonBnd" (only for EPIC), and any combination can be specified. In order to include the significant genes that overlap with the gene set of interest, the sig.genes parameter can be set to TRUE. This adds an additional column to the results dataframe.

CpG level analysis

To illustrate how to use gometh, consider the results from the differential methylation analysis with RUVm.

top <- topRUV(rfit4, number = Inf, p.BH = 1)
table(top$p.BH_X1.1 < 0.01)

At a 1% false discovery rate cut-off, there are more than 60,000 CpG sites differentially methylated. These CpGs are annotated to almost 10,000 genes, which means that a gene ontology analysis is unlikely to be relevant or reveal anything biologically interesting. One option for selecting CpGs in this context is to apply not only a false discovery rate cut-off, but also a $\Delta\beta$ cut-off. However, for this dataset, taking a relatively large $\Delta\beta$ cut-off of 0.25 still leaves more than 30000 CpGs differentially methylated, which can be annotated to more than 6000 genes.

beta <- getBeta(mSet)
# make sure that order of beta values matches orer after analysis
beta <- beta[match(rownames(top),rownames(beta)),]
beta_norm <- rowMeans(beta[,grp==0])
beta_can <- rowMeans(beta[,grp==1])
Delta_beta <- beta_can - beta_norm
sigDM <- top$p.BH_X1.1 < 0.01 & abs(Delta_beta) > 0.25
table(sigDM)

Instead, we take the top 10000 CpG sites as input to gometh which can be annotated to around 2500 genes.

topCpGs<-topRUV(rfit4,number=10000)
sigCpGs <- rownames(topCpGs)
sigCpGs[1:10]

# Check number of genes that significant CpGs are annotated to
check <- getMappedEntrezIDs(sig.cpg = sigCpGs)
length(check$sig.eg)

Thegometh function takes as input a character vector of CpG names, and optionally, a character vector of all CpG sites tested. This is important to include if filtering of the CpGs has been performed prior to differential methylation analysis. If the all.cpg argument is omitted, all the CpGs on the array are used as background. To change the array type, the array.type argument can be specified as either "450K" or "EPIC". The default is "450K".

If the plot.bias argument is TRUE, a figure showing the relationship between the probability of differential methylation and the number of probes per gene will be displayed.

For testing of GO terms, the collection argument takes the value "GO", which is the default setting. For KEGG pathway analysis, set collection to "KEGG". The function topGSA shows the top enriched GO categories. The gsameth function is called for GO and KEGG pathway analysis with the appropriate inputs.

For GO testing on our example dataset:

library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
gst <- gometh(sig.cpg=sigCpGs, all.cpg=rownames(top), collection="GO",
              plot.bias=TRUE)
topGSA(gst, n=10)

Testing all GO categories (>20,000) can be a little slow. To demonstrate the genomic.features parameter, let us rather focus on KEGG pathways (~330 pathways).

gst.kegg <- gometh(sig.cpg=sigCpGs, all.cpg=rownames(top), collection="KEGG")
topGSA(gst.kegg, n=10)

We can limit the input CpGs to those in the promoter regions of genes:

gst.kegg.prom <- gometh(sig.cpg=sigCpGs, all.cpg=rownames(top), 
                        collection="KEGG", genomic.features = c("TSS200",
                                                                "TSS1500",
                                                                "1stExon"))
topGSA(gst.kegg.prom, n=10)

We can see if the results are different if we only include CpGs in gene bodies:

gst.kegg.body <- gometh(sig.cpg=sigCpGs, all.cpg=rownames(top), 
                        collection="KEGG", genomic.features = c("Body"))
topGSA(gst.kegg.body, n=10)

The KEGG pathways are quite different when limiting CpGs to those in gene bodies versus CpGs in promoters. To include the significant genes that overlap with each gene set, set the sig.genes parameter to TRUE.

gst.kegg.body <- gometh(sig.cpg=sigCpGs, all.cpg=rownames(top), 
                        collection="KEGG", genomic.features = c("Body"), 
                        sig.genes = TRUE)
topGSA(gst.kegg.body, n=5)

For a more generalised version of gene set testing in methylation data where the user can specify the gene set to be tested, the function gsameth can be used. To display the top 20 pathways, topGSA can be called. gsameth can take a single gene set, or a list of gene sets. The gene identifiers in the gene set must be Entrez Gene IDs. To demonstrate gsameth, we download and use the Hallmark gene set.

hallmark <- readRDS(url("http://bioinf.wehi.edu.au/MSigDB/v7.1/Hs.h.all.v7.1.entrez.rds"))
gsa <- gsameth(sig.cpg=sigCpGs, all.cpg=rownames(top), collection=hallmark)
topGSA(gsa, n=10)

Note that if it is of interest to obtain the Entrez Gene IDs that the significant CpGs are mapped to, the getMappedEntrezIDs function can be called.

Region level analysis

We have extended gometh and gsameth to perform gene set testing following a region analysis. The region gene set testing function analogues are goregion and gsaregion. Instead of inputting significant CpGs, a ranged object as typically outputted from region finding software is used. The CpGs overlapping the significant differentially methylated regions (DMRs) are extracted and the same statistical framework in gometh and gsameth is applied to test for enrichment of gene sets. We find that genes that have more CpGs measuring methylation are more likely to be called as differentially methylated regions, and hence it is important to take into account this bias when performing gene set testing.

To demonstrate goregion and gsaregion we will perform a region analysis on the cancer dataset using the r BiocStyle::Biocpkg("DMRcate") package.

library(DMRcate)

First, the matrix of M-values is annotated with the relevant annotation information about the probes such as their genomic position, gene annotation, etc. By default, this is done using the ilmn12.hg19 annotation. The limma pipeline is then used for differential methylation analysis to calculate moderated t-statistics.

myAnnotation <- cpg.annotate(object = M, datatype = "array", what = "M", 
                             arraytype = c("450K"), 
                             analysis.type = "differential", design = design, 
                             coef = 4)

Next we can use the dmrcate function to combine the CpGs to identify differentially methylated regions. We can use the extractRanges function to extract a GRanges object with the genomic location and the relevant statistics associated with each DMR. This object can then be used as input to goregion and gsaregion.

DMRs <- dmrcate(myAnnotation, lambda=1000, C=2)
results.ranges <- extractRanges(DMRs)
results.ranges

We can visualise the top DMR using the DMR.plot function:

cols <- c(2,4)[group]
names(cols) <-group
beta <- getBeta(mSet)
par(mfrow=c(1,1))
DMR.plot(ranges=results.ranges, dmr=2, CpGs=beta, phen.col=cols, 
         what="Beta", arraytype="450K", genome="hg19")

Now that we have performed our region analysis, we can use goregion and gsaregion to perform gene set testing. Setting plot.bias to TRUE, we can see strong probe number bias in the data. This can be interpretted that a gene is more likely to have a DMR called if it has more CpGs measuring methylation.

gst.region <- goregion(results.ranges, all.cpg=rownames(M), 
                       collection="GO", array.type="450K", plot.bias=TRUE)

topGSA(gst.region, n=10)

We can also test for enrichment of KEGG pathways:

gst.region.kegg <- goregion(results.ranges, all.cpg=rownames(M), 
                       collection="KEGG", array.type="450K")
topGSA(gst.region.kegg, n=10)

What is interesting is that although very similar pathways are highly ranked compared to the CpG level analysis, gene set testing on the regions is more powerful i.e. the p-values are more significant. In experiments where there are tens of thousands of significant CpGs from a CpG level analysis, we recommend that a good quality region analysis can be a more powerful approach for gene set enrichment analysis.

We can also perform gene set testing on the Hallmark gene sets using gsaregion:

gsa.region <- gsaregion(results.ranges, all.cpg=rownames(M), 
                        collection=hallmark)
topGSA(gsa.region, n=10)

Note that the DMR analysis can be further refined by imposing a $\Delta\beta$ value cut-off and changing various parameters. Please refer to the r BiocStyle::Biocpkg("DMRcate") package vignette for more details on how to do this.

Session information

sessionInfo()

References

missMethyl documentation built on Nov. 8, 2020, 7:51 p.m.