MethReg: estimating regulatory potential of DNA methylation in gene transcription

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Transcription factors (TFs) are proteins that facilitate the transcription of DNA into RNA. A number of recent studies have observed that the binding of TFs onto DNA can be affected by DNA methylation, and in turn, DNA methylation can also be added or removed by proteins associated with transcription factors [@bonder2017disease; @banovich2014methylation; @zhu2016transcription].

To provide functional annotations for differentially methylated regions (DMRs) and differentially methylated CpG sites (DMS), MethReg performs integrative analyses using matched DNA methylation and gene expression along with Transcription Factor Binding Sites (TFBS) data. MethReg evaluates, prioritizes and annotates DNA methylation regions (or sites) with high regulatory potential that works synergistically with TFs to regulate target gene expressions, without any additional ChIP-seq data.

The results from MethReg can be used to generate testable hypothesis on the synergistic collaboration of DNA methylation changes and TFs in gene regulation. MethReg can be used either to evaluate regulatory potentials of candidate regions or to search for methylation coupled TF regulatory processes in the entire genome.


MethReg is a Bioconductor package and can be installed through BiocManager::install().

if (!"BiocManager" %in% rownames(installed.packages()))
BiocManager::install("MethReg", dependencies = TRUE)

After the package is installed, it can be loaded into R workspace by


MethReg workflow

The figure below illustrates the workflow for MethReg. Given matched array DNA methylation data and RNA-seq gene expression data, MethReg additionally incorporates TF binding information from ReMap2020 [@remap2020] or the JASPAR2020 [@JASPAR2020; @fornes2020jaspar] database, and optionally additional TF-target gene interaction databases, to perform both promoter and distal (enhancer) analysis.

In the unsupervised mode, MethReg analyzes all CpGs on the Illumina arrays. In the supervised mode, MethReg analyzes and prioritizes differentially methylated CpGs identified in EWAS.

There are three main steps: (1) create a dataset with triplets of CpGs, TFs that bind near the CpGs, and putative target genes, (2) for each triplet (CpG, TF, target gene), apply integrative statistical models to DNA methylation, target gene expression, and TF expression values, and (3) visualize and interpret results from statistical models to estimate individual and joint impacts of DNA methylation and TF on target gene expression, as well as annotate the roles of TF and CpG methylation in each triplet.

The results from the statistical models will also allow us to identify a list of CpGs that work synergistically with TFs to influence target gene expression.

jpeg::readJPEG("workflow_methReg.jpg") %>% grid::grid.raster()

Analysis illustration

Input data

For illustration, we will use chromosome 21 data from 38 TCGA-COAD (colon cancer) samples.

Input DNA methylation dataset

The DNA methylation dataset is a matrix or SummarizedExperiment object with methylation beta or M-values. If there are potential confounding factors (e.g. batch effect, age, sex) in the dataset, this matrix would contain residuals from fitting linear regression instead (see details Section 5 "Controlling effects from confounding variables" below).

The samples are in the columns and methylation regions or probes are in the rows.

Analysis for individual CpGs data

We will analyze all CpGs on chromosome 21 in this vignette.

However, oftentimes, the methylation data can also be, for example, differentially methylated sites (DMS) or differentially methylated regions (DMRs) obtained in an epigenome-wide association study (EWAS) study.

```{R warning=FALSE} data("dna.met.chr21")


We will first create a SummarizedExperiment object with the function make_dnam_se. This function will use the Sesame R/Bioconductor package to map the array probes into genomic regions. You cen set human genome version (hg38 or hg19) and the array type ("450k" or "EPIC") <- make_dnam_se(
  dnam = dna.met.chr21,
  genome = "hg38",
  arrayType = "450k",
  betaToM = FALSE, # transform beta to m-values 
  verbose = FALSE # hide informative messages

Analysis of DMRs

Differentially Methylated Regions (DMRs) associated with phenotypes such as tumor stage can be obtained from R packages such as coMethDMR, comb-p, DMRcate and many others. The methylation levels in multiple CpGs within the DMRs need to be summarized (e.g. using medians), then the analysis for DMR will proceed in the same way as those for CpGs.

Input gene expression dataset

The gene expression dataset is a matrix with log2 transformed and normalized gene expression values. If there are potential confounding factors (e.g. batch effect, age, sex) in the dataset, this matrix can also contain residuals from linear regression instead (see Section 6 "Controlling effects from confounding variables" below).

The samples are in the columns and the genes are in the rows.


We will also create a SummarizedExperiment object for the gene expression data. This object will contain the genomic information for each gene. <- make_exp_se(
  exp = gene.exp.chr21.log2,
  genome = "hg38",
  verbose = FALSE

Creating triplet dataset

Creating triplet dataset using distance based approaches and JASPAR2020

In this section, regions refer to the regions where CpGs are located.

The function create_triplet_distance_based provides three different methods to link a region to a target gene:

  1. Mapping the region to the closest gene (target.method = "genes.promoter.overlap")
  2. Mapping the region to a specific number of genes upstream down/upstream of the region (target.method = "nearby.genes") [@silva2019elmer].
  3. Mapping the region to all the genes within a window (default size = 500 kbp around the region, i.e. +- 250 kbp from start or end of the region) (target.method = "window") [@reese2019epigenome].
png::readPNG("mapping_target_strategies.png") %>% grid::grid.raster()

For the analysis of probes in gene promoter region, we recommend setting method = "genes.promoter.overlap", or method = "closest.gene". For the analysis of probes in distal regions, we recommend setting either method = "window" or method = "nearby.genes". Note that the distal analysis will be more time and resource consuming.

To link regions to TF using JASPAR2020, MethReg uses motifmatchr [@motifmatchr] to scan these regions for occurrences of motifs in the database. JASPAR2020 is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles [@JASPAR2020; @fornes2020jaspar], which contains more the 500 human TF motifs.

The argument will be used to extend the region when scanning for the motifs, for example, a of 50 will add 25 bp upstream and 25 bp downstream of the original region.

As an example, the following scripts link CpGs with the probes in gene promoter region (method 1. above)

```{R, message = FALSE, results = "hide"} triplet.promoter <- create_triplet_distance_based( region =, target.method = "genes.promoter.overlap", genome = "hg38", target.promoter.upstream.dist.tss = 2000, target.promoter.downstream.dist.tss = 2000, = 500, = 1e-08, cores = 1

Alternatively, we can also link each probe with genes within 
$500 kb$ window (method 2). 

```{R, message = FALSE, results = "hide"}
# Map probes to genes within 500kb window
triplet.distal.window <- create_triplet_distance_based(
  region =,
    genome = "hg38", 
    target.method = "window",
    target.window.size = 500 * 10^3,
    target.rm.promoter.regions.from.distal.linking = TRUE, = 500,  = 1e-08,
    cores = 1

For method 3, to map probes to 5 nearest upstream and downstream genes:

```{R, message = FALSE, results = "hide"}

Map probes to 5 genes upstream and 5 downstream

triplet.distal.nearby.genes <- create_triplet_distance_based( region =, genome = "hg38", target.method = "nearby.genes", target.num.flanking.genes = 5, target.window.size = 500 * 10^3, target.rm.promoter.regions.from.distal.linking = TRUE, = 500, = 1e-08, cores = 1

#### Creating triplet dataset using distance based approaches and REMAP2020

Instead of using JASPAR2020 motifs, we will be using REMAP2020 catalogue of 
TF peaks which can be access using the package `ReMapEnrich`.

if (!"BiocManager" %in% rownames(installed.packages()))
BiocManager::install("remap-cisreg/ReMapEnrich", dependencies = TRUE)

To download REMAP2020 catalogue (~1Gb) the following functions are used:

```{R, eval = FALSE} library(ReMapEnrich) remapCatalog2018hg38 <- downloadRemapCatalog("/tmp/", assembly = "hg38") remapCatalog <- bedToGranges(remapCatalog2018hg38)

The function `create_triplet_distance_based` will accept any Granges with TF 
information in the same format as the `remapCatalog` one.

```{R, eval = FALSE}
# Triplets promoter using remap
triplet.promoter.remap <- create_triplet_distance_based(
  region =,
  genome = "hg19",
  target.method =  "genes.promoter.overlap", = remapCatalog, = 500, = 10^6,

Creating triplet dataset using regulon-based approaches

The human regulons from the dorothea database will be used as an example:

if (!"BiocManager" %in% rownames(installed.packages()))
BiocManager::install("dorothea", dependencies = TRUE)
regulons.dorothea <- dorothea::dorothea_hs
regulons.dorothea %>% head

Using the regulons, you can calculate enrichment scores for each TF across all samples using dorothea and viper. <- get_tf_ES(
  exp = %>% SummarizedExperiment::assay(),
  regulons = regulons.dorothea

Finally, triplets can be identified using TF-target from regulon databases with the function create_triplet_regulon_based.

```{R, message = FALSE, results = "hide"} triplet.regulon <- create_triplet_regulon_based( region =, genome = "hg38", = 500, = regulons.dorothea, = 10^6 # 1Mbp )

triplet.regulon %>% head

Example of triplet data frame

The triplet is a data frame with the following columns:

triplet.promoter$distance_region_target_tss %>% range
triplet.promoter %>% head

Note that there may be multiple rows for a CpG region, when multiple target gene and/or TFs are found close to it.

Evaluating the regulatory potential of CpGs (or DMRs)

Because TF binding to DNA can be influenced by (or influences) DNA methylation levels nearby [@yin2017impact], target gene expression levels are often resulted from the synergistic effects of both TF and DNA methylation. In other words, TF activities in gene regulation is often affected by DNA methylation.

Our goal then is to highlight DNA methylation regions (or CpGs) where these synergistic DNAm and TF collaborations occur. We will perform analyses using the 3 datasets described above in Section 3:

Analysis using model with methylation by TF interaction

The function interaction_model assess the regulatory impact of DNA methylation on TF regulation of target genes via the following approach:

considering DNAm values as a binary variable - we define a binary variable DNAm Group for DNA methylation values (high = 1, low = 0). That is, samples with the highest DNAm levels (top 25 percent) has high = 1, samples with lowest DNAm levels (bottom 25 pecent) has high = 0.

Note that in this implementation, only samples with DNAm values in the first and last quartiles are considered.

$$log_2(RNA target) \sim log_2(TF) + \text{DNAm Group} + log_2(TF) * \text{DNAm Group}$$

```{R interaction_model, message = FALSE, results = "hide", eval = TRUE} results.interaction.model <- interaction_model( triplet = triplet.promoter, dnam =, exp =, sig.threshold = 0.05, fdr = TRUE, = TRUE, = TRUE )

The output of `interaction_model` function will be a data frame with the following variables:

* `pval_<variable>`: p-value for a tested variable (methylation or TF), given the other variables included in the model.
* `estimate_<variable>`: estimated effect for a variable. If estimate > 0, increasing values 
of the variable corresponds to increased outcome values (target gene expression). 
If estimate < 0, increasing values of the variable correspond to decreased target gene expression levels.

The following columns are provided for the results of fitting **quartile model** to triplet data:

* direct effect of methylation:  
  + `quant_pval_metGrp`: p-value for binary DNA methylation variable
  + `quant_estimates_metGrp`: estimated DNA methylation effect

* direct effect of TF: 
  + `` : p-value for TF expression
  + ``: estimated TF effect

* synergistic effects of methylation and TF: 
  + ``: : p-value for DNA methylation by TF interaction
  + ``: estimated DNA methylation by TF interaction effect

# Results for quartile model
results.interaction.model %>% dplyr::select(
  ) %>% head

Stratified analysis by high and low DNA methylation levels

For triplets with significant $log_2(TF) × DNAm$ interaction effect identified above, we can further assess how gene regulation by TF changes when DNAm is high or low. To this end, the function stratified_model fits two separate models (see below) to only samples with the highest DNAm levels (top 25 percent), and then to only samples with lowest DNAm levels (bottom 25 percent), separately.

$$\text{Stratified Model: } log_2(RNA target) \sim log_2(TF)$$

```{R stratified_model, message = FALSE, warning = FALSE, results = "hide", eval = TRUE} results.stratified.model <- stratified_model( triplet = results.interaction.model, dnam =, exp = )

results.stratified.model %>% head

Visualization of data

The functions plot_interaction_model will create figures to visualize the data, in a way that corresponds to the linear model we considered above. It requires the output from the function interaction_model (a dataframe), the DNA methylation matrix and the gene expression matrix as input.

```{R plot_interaction_model, eval = TRUE, message = FALSE, results = "hide", warning = FALSE} plots <- plot_interaction_model( triplet.results = results.interaction.model[1,], dnam =, exp = )

```{R, fig.height = 8, fig.width = 13, eval = TRUE, fig.cap = "An example output from MethReg."}

The first row of the figures shows pairwise associations between DNA methylation, TF and target gene expression levels.

The second row of the figures show how much TF activity on target gene expression levels vary by DNA methylation levels. When TF by methylation interaction is significant (Section 4.1), we expect the association between TF and target gene expression vary depending on whether DNA methylation is low or high.

In this example, when DNA methylation is low, target gene expression is relatively independent of the amount of TF available. On the other hand, when DNA methylation level is high, more abundant TF corresponds to increased gene expression (an activator TF). One possibility is that DNA methylation might enhance TF binding in this case. This is an example where DNA methylation and TF work synergistically to affect target gene expression.

While the main goal of MethReg is to prioritize methylation CpGs, also note that without stratifying by DNA methylation, the overall TF-target effects (p = 0.142) is not as significant as the association in stratified analysis in high methylation samples (p = 0.00508). This demonstrates that by additionally modeling DNA methylation, we can also nominate TF – target associations that might have been missed otherwise.

Results interpretation

Shown below are some expected results from fitting Models 1 & 2 described in Section 4.1 above, depending on TF binding preferences. Please note that there can be more possible scenarios than those listed here, therefore, careful evaluation of the statistical models and visualization of data as described in Section 4 are needed to gain a good understanding of the multi-omics data.

png::readPNG("scenarios.png")  %>% grid::grid.raster()

Controlling effects from confounding variables

Both gene expressions and DNA methylation levels can be affected by age, sex, shifting in cell types, batch effects and other confounding (or covariate) variables. In this section, we illustrate analysis workflow that reduces confounding effects, by first extracting the residual data with the function get_residuals, before fitting the models discussed above in Section 4.

The get_residuals function will use gene expression (or DNA methylation data) and phenotype data as input. To remove confounding effects in gene expression data, we use the get_residuals function which extract residuals after fitting the following model for gene expression data: $$log_2(RNA target) \sim covariate_{1} + covariate_{2} + ... + covariate_{N}$$ or the following model for methylation data:

$$methylation.Mvalues \sim covariate_{1} + covariate_{2} + ... + covariate_{N}$$

```{R residuals, results = "hide", eval = FALSE} data("gene.exp.chr21.log2") data("clinical") metadata <- clinical[,c("sample_type","gender")]

gene.exp.chr21.residuals <- get_residuals(gene.exp.chr21, metadata) %>% as.matrix()

```{R, eval = FALSE}

```{R, results = "hide", eval = FALSE} data("dna.met.chr21") dna.met.chr21 <- make_se_from_dnam_probes( dnam = dna.met.chr21, genome = "hg38", arrayType = "450k", betaToM = TRUE ) dna.met.chr21.residuals <- get_residuals(dna.met.chr21, metadata) %>% as.matrix()

```{R, eval = FALSE}

The models described in Section 4.1 can then be applied to these residuals data using the interaction_model function:

```{R, message = FALSE, results = "hide", eval = FALSE} results <- interaction_model( triplet = triplet, dnam = dna.met.chr21.residuals, exp = gene.exp.chr21.residuals )

# Calculating enrichment scores

## Using dorothea and viper 

This example shows how to use dorothea regulons and viper to calculate 
enrichment scores for each TF across all samples.

regulons.dorothea <- dorothea::dorothea_hs
regulons.dorothea %>% head

```{R, message = FALSE, results = "hide"} <- get_tf_ES( exp = %>% SummarizedExperiment::assay(), regulons = regulons.dorothea )


Using dorothea and GSVA

```{R, message = FALSE, results = "hide"} regulons.dorothea <- dorothea::dorothea_hs regulons.dorothea$tf <- MethReg:::map_symbol_to_ensg( gene.symbol = regulons.dorothea$tf, genome = "hg38" ) regulons.dorothea$target <- MethReg:::map_symbol_to_ensg( gene.symbol = regulons.dorothea$target, genome = "hg38" ) split_tibble <- function(tibble, col = 'col') tibble %>% split(., .[, col]) regulons.dorothea.list <- regulons.dorothea %>% na.omit() %>% split_tibble('tf') %>% lapply(function(x){x[[3]]})

```{R, message = FALSE, results = "hide", eval = FALSE}
library(GSVA) <- gsva(
  expr = %>% SummarizedExperiment::assay(), 
  gset.idx.list = regulons.dorothea.list, 
  method = "gsva",
  kcdf = "Gaussian",
  abs.ranking = TRUE, = 5, = Inf, = 1L,
  mx.diff = TRUE,
  ssgsea.norm = TRUE,
  verbose = TRUE

Session information

{R,size = 'tiny'} sessionInfo()


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MethReg documentation built on Nov. 8, 2020, 8:01 p.m.