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An R package for identifying DNA methylation driven genes.
- Getting Started
- Introduction
- Data Access and Preprocessing
- Data Input for MethylMix
- Running MethylMix
- Plotting Outputs
- References
- Author Information
- Citation
- License
Installation
To install the MethylMix package, the easiest way is through Bioconductor:
source("http://bioconductor.org/biocLite.R")
biocLite(MethylMix)
Alternatively, MethylMix can be installed by downloading the appropriate file for
your platform from the Bioconductor website or by cloning the GitHub repository.
For Windows, start R and select the Packages menu, then Install package from local zip file.
Find and highlight the location of the zip file and click on open. For Linux/Unix, use the usual command
R CMD INSTALL or install from Bioconductor.
Loading
To load the MethylMix package in your R session, type library(MethylMix).
Help
Detailed information on MethylMix package functions can be obtained in the help files. For example, to view the help file
for the function MethylMix in a R session, use ?MethylMix.
DNA methylation is a mechanism whereby a methyl-group is added onto a CpG site. Methylation of these CpG sites is associated with gene silencing and is an important mechanism for normal tissue development, thus irregular methylation is often involved in diseases such as cancer. Recently, many high throughput data has been generated profiling CpG site methylation on a genome wide basis. This has created large amounts of data on DNA methylation for many diseases. Computational analysis of DNA methylation data is required to identify potentially aberrant DNA methylation compared to normal tissue. MethylMix (Gevaert 2015; Gevaert, Tibshirani & Plevritis 2015) was developed to tackle this question using a computational approach. MethylMix identifies differential and functional DNA methylation by using a beta mixture model to identify subpopulations of samples with different DNA methylation compared to normal tissue. Functional DNA methylation refers to a significant negative correlation based on matched gene expression data. MethylMix outputs hyper and hypomethylated genes, called MethylMix drivers, which can be used for downstream analysis. MethylMix was designed for cis-regulated promoter differential methylation and works best when specific CpG sites profiled are associated with a gene. For example, when using data from the 27k and 450k Illumina Infinium platforms.
The MethylMix package provides functions to access and preprocess data from The Cancer Genome Atlas (TCGA) portal through the 'curatedTCGAData' package. Given one of TCGA codified cancer types and an (optional) path to save processed output files, the downloading and preprocessing of the data can be executed using the GetData() function. For example, for ovarian cancer, the data can be downloaded and preprocessed as follows:
cancerSite <- "OV"
targetDirectory <- paste0(getwd(), "/")
GetData(cancerSite, targetDirectory)
All functions in the MethylMix package can be run in parallel, if the user provides a parallel setup, like the following:
cancerSite <- "OV"
targetDirectory <- paste0(getwd(), "/")
library(doParallel)
cl <- makeCluster(5)
registerDoParallel(cl)
GetData(cancerSite, targetDirectory)
stopCluster(cl)
The GetData() function downloads DNA methylation data and gene expression data. The methylation data is provided using 27k or 450k Illumina platforms. If both 27k and 450k files are found, the data is carefully combined. For gene expression, RNA sequencing data is used. The MethylMix package downloads RNA sequencing data for any TCGA cancer site specified. For the preprocessing of the data, we perform missing value estimation and batch correction (using Combat). Finally, as in the TCGA case, when only probe-level Illumina data is available, mapping probes to genes is recommended before building mixture models. This allows the algorithm to prioritize cis-regulated differential methylation by only focusing on differential methylation of CpG sites to their closest gene transcripts. Both the 27k and 450k Illumina Infinium platforms have database R packages that provide the necessary mapping information. We use the annotation to map probes to genes, before clustering the probes within each gene. This whole process can take a long time, depending of the size of the data.
It is also possible to perform each one of these tasks individually using other functions in the MethylMix package, as in the following example (a step-by-step invocation of the GetData() function):
cancerSite <- "OV"
targetDirectory <- paste0(getwd(), "/")
cl <- makeCluster(5)
registerDoParallel(cl)
# Download the methylation, gene expression, and sample annotation data
MAEO <- Download_Data(cancerSite)
# Methylation
METProcessedData <- Preprocess_DNAmethylation(cancerSite, MAEO)
saveRDS(METProcessedData, file = paste0(targetDirectory, "MET_", cancerSite, "_Processed.rds"))
# Gene expression
GEProcessedData <- Preprocess_GeneExpression(cancerSite, MAEO)
saveRDS(GEProcessedData, file = paste0(targetDirectory, "GE_", cancerSite, "_Processed.rds"))
# Clustering probes to genes
res <- ClusterProbes(METProcessedData[[1]], METProcessedData[[2]])
# Putting everything together
toSave <- list(METcancer = res[[1]], METnormal = res[[2]], GEcancer = GEProcessedData[[1]], GEnormal = GEProcessedData[[2]], ProbeMapping = res$ProbeMapping, PatientData = colData(MAEO))
saveRDS(toSave, file = paste0(targetDirectory, "data_", cancerSite, ".rds"))
stopCluster(cl)
For user supplied data not from TCGA, the user can provide DNA methylation beta-values (normal and cancer) and gene expression data in the form of data.matrix objects in R with the rows corresponding to the genes and the columns corresponding to the samples. Once the three matrices: METcancer, METnormal, and GEnormal are created, the last step is to cluster the methylation data:
METcancer = matrix(data = methylation_data, nrow = nb_of_genes, ncol = nb_of_samples)
METnormal = matrix(data = methylation_data, nrow = nb_of_genes, ncol = nb_of_samples)
GEcancer = matrix(data = expression_data, nrow = nb_of_genes, ncol = nb_of_samples)
ClusterProbes(MET_Cancer, MET_Normal, CorThreshold = 0.4)
If the data contains batches, the user must provide numeric batch data within the matrices. MethylMix can be applied on all Illumina arrays, including the Epic platform, and any array that outputs beta values. At the moment there are no restrictions for input of sequencing-based methylation data, as long as the data is formatted in the proper proportions. However, as mixture modeling is computationally expensive, MethylMix may require more time to finish in these cases.
To run MethylMix, three data sets of a particular disease are required. The first one is the methylation data for the disease samples, METcancer, which allows the identification of methylation states associated with a disease for each gene of interest. The second is a set of appropriate normal or baseline methylation data, METnormal, which is used to distinguish between hyper or increased methylation versus hypo or decreased methylation. Finally, the third data set is matched gene expression data for the disease samples, GEcancer, which is used to identify functional differential methylation by focusing only on differential methylation that has a significant inversely correlated effect on gene expression.
Each of these three data sets are matrix objects with genes in the
rows with unique rownames (e.g. gene symbols) and samples or patients in the
columns with unique patient names. The GetData function described before
saves an R object which contains these matrices in the correct format.
For example, small data sets from TCGA of glioblastoma samples are avaliable in the MethylMix package:
library(MethylMix)
library(doParallel)
data(METcancer)
data(METnormal)
data(GEcancer)
head(METcancer[, 1:4])
head(METnormal)
head(GEcancer[, 1:4])
Using the example glioblastoma data provided in the package, MethylMix can be used to identify hypomethylated and hypermethylated genes as follows:
MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal)
Or in parallel:
library(doParallel)
cl <- makeCluster(5)
registerDoParallel(cl)
MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal)
stopCluster(cl)
And if patient data is avaliable (patient data is automatically extracted from the built-in MAEO implementation), then it can be included in the MethylMix results as follows:
MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal, PatientData=PatientData)
The output from the MethylMix function is a list with the following elements:
MethylationDrivers: Genes identified as transcriptionally predictive and differentially methylated by MethylMix.NrComponents: An integer of the number of methylation states found for each driver gene.MixtureStates: A list with the DM-values for each driver gene.MethylationStates: A matrix with DM-values for all driver genes (rows) and all samples (columns). If PatientData is provided, this is a SummarizedExperiment object.Classifications: A matrix with integers indicating to which mixture component each cancer sample was assigned to, for each gene.Models: Beta mixture model parameters for each driver gene.
Differential Methylation values (DM-values) are defined as the difference between the methylation mean in one mixture component of cancer samples and the methylation mean in the normal samples, for a given gene. These outputs can be viewed in R as follows:
MethylMixResults$MethylationDrivers
MethylMixResults$NrComponents
MethylMixResults$MixtureStates
MethylMixResults$MethylationStates[, 1:5]
MethylMixResults$Classifications[, 1:5]
MethylMixResults$Models
The MethylMix package also provides functions to visually represent the findings:
# Plot the most famous methylated gene for glioblastoma
plots <- MethylMix_PlotModel("MGMT", MethylMixResults, METcancer)
plots$MixtureModelPlot
# Plot MGMT also with its normal methylation variation
plots <- MethylMix_PlotModel("MGMT", MethylMixResults, METcancer, METnormal = METnormal)
plots$MixtureModelPlot
# Plot a MethylMix model for another gene
plots <- MethylMix_PlotModel("ZNF217", MethylMixResults, METcancer, METnormal = METnormal)
plots$MixtureModelPlot
# Also plot the inverse correlation with gene expression (creates two separate plots)
plots <- MethylMix_PlotModel("MGMT", MethylMixResults, METcancer, GEcancer, METnormal)
plots$MixtureModelPlot
plots$CorrelationPlot
# Plot all functional and differential genes
for (gene in MethylMixResults$MethylationDrivers) {
MethylMix_PlotModel(gene, MethylMixResults, METcancer, METnormal = METnormal)
}
Gevaert O. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020
Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8
Pierre-Louis Cedoz, Marcos Prunello, Kevin Brennan, Olivier Gevaert; MethylMix 2.0: an R package for identifying DNA methylation genes. Bioinformatics. doi:10.1093/bioinformatics/bty156
| Olivier Gevaert |
|---|
| Stanford Center for Biomedical Informatics |
| Department of Medicine |
| 1265 Welch Road |
| Stanford CA, 94305-5479 |
If you use MethylMix in your work, please cite:
Gevaert, O. (2017). MethylMix: Identifying methylation driven cancer genes. R package version 2.11.0.
MethylMix is licensed under the GPL-2 license. See the LICENSE for more information.