MethReg: estimating regulatory potential of DNA methylation in gene transcription
1 May 2024
Package
MethReg 1.14.0
Contents
1 Introduction
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 (Bonder et al. 2017; Banovich et al. 2014; Zhu, Wang, and Qian 2016).
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.
2 Installation
MethReg is a Bioconductor package and can be installed through BiocManager::install().
if (!"BiocManager" %in% rownames(installed.packages()))
install.packages("BiocManager")
BiocManager::install("MethReg", dependencies = TRUE)After the package is installed, it can be loaded into R workspace by
library(MethReg)
#> 3 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 (Chèneby et al. 2019) or the JASPAR2020 (???; ???) 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.
Figure 1: Figure 2 Workflow of MethReg
Data - MethReg required inputs are: (1) array-based DNA methylation data (HM450/EPIC) with beta-values, (2) RNA-seq data with normalized counts, (3) estimated TF activities from the RNA-seq data using GSVA or VIPER software. Creating triplets – there are multiple ways to create a CpG-TF-target gene triplet: (1) CpGs can be mapped to human TFs by using TF motifs from databases such as JASPAR or ReMap and scanning the CpGs region to identify if there is a binding site (2) CpG can be mapped to target genes using a distance-based approach, CpGs will be linked to a gene if it is on promoter region (+-1K bp away from the TSS), for a distal CpG it can be linked to either all genes within a fixed width (i.e. 500k bp), or a fixed number of genes upstream and downstream of the CpG location (3) TF-target genes can be retrieved from external databases (i.e. Cistrome Cancer; Dorothea). Using two different pairs (i.e. CpG-TF, TF-target gene), triplets can then be created. Analysis – each triplet will be evaluated using a robust linear model in which TF activity is estimated from GSVA/Viper software and DNAm.group is a binary variable used to model if the sample CpG belongs to the top 25% methylation levels or the lowest 25% methylation levels. Results – MethReg will output the prioritized triplets and classify both the TF role on the gene expression (repressor or activator) and the DNAm effect on the TF activity (Enhancing or attenuating).
4 Analysis illustration
4.1 Input data
For illustration, we will use chromosome 21 data from 38 TCGA-COAD (colon cancer) samples.
4.1.1 Input DNA methylation dataset
The DNA methylation dataset is a matrix or SummarizedExperiment object with methylation beta or M-values (The samples are in the columns and methylation regions or probes are in the rows). 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).
4.1.1.1 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.
data("dna.met.chr21")dna.met.chr21[1:5,1:5]
#> TCGA-3L-AA1B-01A TCGA-4N-A93T-01A TCGA-4T-AA8H-01A TCGA-5M-AAT4-01A TCGA-5M-AAT5-01A
#> cg00002080 0.6454046 0.5933725 0.54955509 0.81987982 0.79171160
#> cg00004533 0.9655396 0.9640490 0.96690671 0.95510446 0.96061252
#> cg00009944 0.5437705 0.2803064 0.42918909 0.60734630 0.47555585
#> cg00025591 0.4021317 0.7953653 0.41816364 0.33241304 0.67251468
#> cg00026030 0.1114705 0.1012902 0.06834467 0.08594876 0.06715677We 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”)
dna.met.chr21.se <- make_dnam_se(
dnam = dna.met.chr21,
genome = "hg38",
arrayType = "450k",
betaToM = FALSE, # transform beta to m-values
verbose = FALSE # hide informative messages
)
#> see ?sesameData and browseVignettes('sesameData') for documentation
#> loading from cache
#> require("GenomicRanges")dna.met.chr21.se
#> class: RangedSummarizedExperiment
#> dim: 2918 38
#> metadata(2): genome arrayType
#> assays(1): ''
#> rownames(2918): chr21:10450634-10450635 chr21:10520974-10520975 ...
#> chr21:46670216-46670217 chr21:46670596-46670597
#> rowData names(53): address_A address_B ... MASK_general probeID
#> colnames(38): TCGA-3L-AA1B-01A TCGA-4N-A93T-01A ... TCGA-A6-5656-01B TCGA-A6-5657-01A
#> colData names(1): samples
SummarizedExperiment::rowRanges(dna.met.chr21.se)[1:4,1:4]
#> GRanges object with 4 ranges and 4 metadata columns:
#> seqnames ranges strand | address_A address_B channel
#> <Rle> <IRanges> <Rle> | <integer> <integer> <character>
#> chr21:10450634-10450635 chr21 10450634-10450635 * | 74716393 <NA> Both
#> chr21:10520974-10520975 chr21 10520974-10520975 * | 29756401 20622400 Red
#> chr21:10521044-10521045 chr21 10521044-10521045 * | 15617483 <NA> Both
#> chr21:10521122-10521123 chr21 10521122-10521123 * | 33810384 37781360 Grn
#> designType
#> <character>
#> chr21:10450634-10450635 II
#> chr21:10520974-10520975 I
#> chr21:10521044-10521045 II
#> chr21:10521122-10521123 I
#> -------
#> seqinfo: 26 sequences from an unspecified genome; no seqlengths4.1.1.2 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.
4.1.2 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.
data("gene.exp.chr21.log2")
gene.exp.chr21.log2[1:5,1:5]
#> TCGA-3L-AA1B-01A TCGA-4N-A93T-01A TCGA-4T-AA8H-01A TCGA-5M-AAT4-01A
#> ENSG00000141956 14.64438 14.65342 14.09232 14.60680
#> ENSG00000141959 19.33519 20.03720 19.76128 19.57854
#> ENSG00000142149 17.27832 16.02392 18.16079 15.84463
#> ENSG00000142156 20.38689 18.83080 18.02720 18.91380
#> ENSG00000142166 17.89172 18.06625 18.47187 17.40467
#> TCGA-5M-AAT5-01A
#> ENSG00000141956 14.58640
#> ENSG00000141959 18.27442
#> ENSG00000142149 14.79654
#> ENSG00000142156 18.71926
#> ENSG00000142166 16.71412We will also create a SummarizedExperiment object for the gene expression data. This object will contain the genomic information for each gene.
gene.exp.chr21.se <- make_exp_se(
exp = gene.exp.chr21.log2,
genome = "hg38",
verbose = FALSE
)
gene.exp.chr21.se
#> class: RangedSummarizedExperiment
#> dim: 752 38
#> metadata(1): genome
#> assays(1): ''
#> rownames(752): ENSG00000141956 ENSG00000141959 ... ENSG00000281420 ENSG00000281903
#> rowData names(2): ensembl_gene_id external_gene_name
#> colnames(38): TCGA-3L-AA1B-01A TCGA-4N-A93T-01A ... TCGA-A6-5656-01B TCGA-A6-5657-01A
#> colData names(1): samples
SummarizedExperiment::rowRanges(gene.exp.chr21.se)[1:5,]
#> GRanges object with 5 ranges and 2 metadata columns:
#> seqnames ranges strand | ensembl_gene_id external_gene_name
#> <Rle> <IRanges> <Rle> | <character> <character>
#> ENSG00000141956 chr21 41798225-41879482 - | ENSG00000141956 PRDM15
#> ENSG00000141959 chr21 44300051-44327376 + | ENSG00000141959 PFKL
#> ENSG00000142149 chr21 31873020-32044633 + | ENSG00000142149 HUNK
#> ENSG00000142156 chr21 45981769-46005050 + | ENSG00000142156 COL6A1
#> ENSG00000142166 chr21 33324429-33359864 + | ENSG00000142166 IFNAR1
#> -------
#> seqinfo: 24 sequences from an unspecified genome; no seqlengths4.1.3 Creating triplet dataset
4.1.3.1 Creating triplet dataset using distance based approaches and JASPAR2022
In this section, regions refer to the regions where CpGs are located.
4.1.3.1.1 Linking a region to a target gene
To evaluate the DNA methylation effect on the expression of a gene, first we need to define which are the possible affected genes. For this we initially define if the DNA methylation occurred withing a promoter regions, defined as 2 kbp upstream and 2 kbp downstream of the transcription start site (TSS), or in a non-promoter region, also known as distal regions, that could behave like enhancer of the gene expression.
Enhancers can increase the transcription of genes and are found in different locations (upstream or downstream of genes, within introns). Their functional complexity lies in the possibility genes located more distantly than the neighboring genes and being able to regulate multiple genes (Pennacchio et al. 2013). Also, enhancer–promoter looping could happen at two sequences within approximately 1 Mb of each other (Pennacchio et al. 2013). Williamson, Hill, and Bickmore (2011) also highlighted not only that a proportion of enhancers are situated hundreds to thousands of kilobases from their target genes, often in large gene-poor regions, but also the promiscuous activity when placed within gene-rich domains.
These promoters and enhancers interactions could be further identified using Chromosome conformation capture techniques such as 3C, 4C, Hi-C. However, in the lack of this information one could use the position information in the genome to link an enhancer to a candidate target gene. Such problem is also identified in the GWAS studies, for example, Brodie, Azaria, and Ofran (2016) found that affected genes are often up to \(2 Mbps\) away from the associated SNP and highlighted that some studies suggested to use a cutoff of \(500 Kbps\) since enhancers and repressors may be as distant as \(500 Kbps\) from their genes. The issue of this method is that with a big window in gene-rich regions would map to several genes, and a small window might not map the gene-poor region, making the decision on the window size very difficult. Another method was presented by Yao et al. (2015) which provided a linkage method based on a fixed quantity of genes upstream and downstream of the enhancers.
MethReg offer two methods for enhancer linking 1) a window-based method similar to the ones in the GWAS studies, 2) a fixed number of genes upstream and downstream of the DNA methylation loci similar to the one suggested by Yao et al. (2015), and one method for promoter linking, which maps to the gene of the promoter region.
The function create_triplet_distance_based provides those three different methods to
link a region to a target gene:
- Mapping the region to the closest gene (
target.method = "genes.promoter.overlap") - Mapping the region to a specific number of genes upstream down/upstream
of the region (
target.method = "nearby.genes") (Silva et al. 2019). - 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") (Reese et al. 2019).
Figure 2: Different target linking strategies
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 JASPAR2022, MethReg uses motifmatchr (Schep 2020) 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 (Baranasic 2022; Castro-Mondragon et al. 2021), which contains
more the 500 human TF motifs.
The motif search width of the scanned region is one important parameter. Although TF recognizes short specific DNA sequence motifs (\(6–12 bp\)) (Leporcq et al. 2020), the output of a ChIP-seq experiment can include peaks longer than \(1000 bp\) (Boeva 2016), but most of the motifs are found \(\pm\) \(50-75 bp\) from the TF peak center (Heinz et al. 2010). Also, recently, it has been shown by Grossman et al. (2018) that TFs have different positional bindings around nucleosome-depleted regions of DNA, which could range from \(\pm200bp\) around the center of the DNaseI-hypersensitive (DHS) sites defined by the Roadmap Epigenomics project and Wang et al. (2019) showed that the methylation levels at UM (unmethylated motifs) and MM (methylated Motifs) were also altered within that range. Since a single CpG is only 1bp, to predict if the methylation at the loci would affect the TF binding site, we suggest using a motif search window no bigger than \(400bp\).
The argument motif.search.window.size will be used to extend the region when scanning
for the motifs, for example, a motif.search.window.size 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)
triplet.promoter <- create_triplet_distance_based(
region = dna.met.chr21.se,
target.method = "genes.promoter.overlap",
genome = "hg38",
target.promoter.upstream.dist.tss = 2000,
target.promoter.downstream.dist.tss = 2000,
motif.search.window.size = 400,
motif.search.p.cutoff = 1e-08,
cores = 1
)Alternatively, we can also link each probe with genes within \(500 kb\) window (method 2).
# Map probes to genes within 500kb window
triplet.distal.window <- create_triplet_distance_based(
region = dna.met.chr21.se,
genome = "hg38",
target.method = "window",
target.window.size = 500 * 10^3,
target.rm.promoter.regions.from.distal.linking = TRUE,
motif.search.window.size = 500,
motif.search.p.cutoff = 1e-08,
cores = 1
)For method 3, to map probes to 5 nearest upstream and downstream genes:
# Map probes to 5 genes upstream and 5 downstream
triplet.distal.nearby.genes <- create_triplet_distance_based(
region = dna.met.chr21.se,
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,
motif.search.window.size = 400,
motif.search.p.cutoff = 1e-08,
cores = 1
)4.1.3.2 Creating triplet dataset using distance based approaches and REMAP2020
Instead of using JASPAR2020 motifs, we will be using REMAP2018 catalogue of
TF peaks which can be access either using the package ReMapEnrich
or a most updated version (RemMap2022) is available online at https://remap.univ-amu.fr/download_page
if (!"BiocManager" %in% rownames(installed.packages()))
install.packages("BiocManager")
BiocManager::install("remap-cisreg/ReMapEnrich", dependencies = TRUE)To download REMAP2018 catalogue (~1Gb) the following functions are used:
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.
#-------------------------------------------------------------------------------
# Triplets promoter using remap
#-------------------------------------------------------------------------------
triplet.promoter.remap <- create_triplet_distance_based(
region = dna.met.chr21.se,
genome = "hg19",
target.method = "genes.promoter.overlap",
TF.peaks.gr = remapCatalog,
motif.search.window.size = 400,
max.distance.region.target = 10^6,
) 4.1.3.3 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()))
install.packages("BiocManager")
BiocManager::install("dorothea", dependencies = TRUE)regulons.dorothea <- dorothea::dorothea_hs
regulons.dorothea %>% head
#> # A tibble: 6 × 4
#> tf confidence target mor
#> <chr> <chr> <chr> <dbl>
#> 1 ADNP D ATF7IP 1
#> 2 ADNP D DYRK1A 1
#> 3 ADNP D TLK1 1
#> 4 ADNP D ZMYM4 1
#> 5 ADNP D ABCC1 1
#> 6 ADNP D ABCC6 1Using the regulons, you can calculate enrichment scores for each TF across all samples using dorothea and viper.
rnaseq.tf.es <- get_tf_ES(
exp = gene.exp.chr21.se %>% SummarizedExperiment::assay(),
regulons = regulons.dorothea
)
#> Warning in run_viper.matrix(input = exp, regulons = regulons, options = list(method = "scale", :
#> This function is deprecated, please check the package decoupleR to infer activities.Finally, triplets can be identified using TF-target from regulon databases with the function create_triplet_regulon_based.
triplet.regulon <- create_triplet_regulon_based(
region = dna.met.chr21.se,
genome = "hg38",
motif.search.window.size = 400,
tf.target = regulons.dorothea,
max.distance.region.target = 10^6 # 1Mbp
) triplet.regulon %>% head
#> # A tibble: 6 × 7
#> regionID target_symbol target TF_symbol TF confidence distance_region_targ…¹
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 chr21:28290328-28290328 CCT8 ENSG00000… ALX3 ENSG… E 783468
#> 2 chr21:28885020-28885021 CCT8 ENSG00000… ALX3 ENSG… E 188775
#> 3 chr21:28885068-28885069 CCT8 ENSG00000… ALX3 ENSG… E 188727
#> 4 chr21:28889202-28889203 CCT8 ENSG00000… ALX3 ENSG… E 184593
#> 5 chr21:28995965-28995966 CCT8 ENSG00000… ALX3 ENSG… E 77830
#> 6 chr21:29001707-29001708 CCT8 ENSG00000… ALX3 ENSG… E 72088
#> # ℹ abbreviated name: ¹distance_region_target_tss4.1.3.4 Example of triplet data frame
The triplet is a data frame with the following columns:
target: gene identifier (obtained from row names of the gene expression matrix),regionID: region/CpG identifier (obtained from row names of the DNA methylation matrix)TF: gene identifier (obtained from the row names of the gene expression matrix)
str(triplet.promoter)
#> tibble [32,543 × 7] (S3: tbl_df/tbl/data.frame)
#> $ regionID : chr [1:32543] "chr21:10520974-10520975" "chr21:10520974-10520975" "chr21:10520974-10520975" "chr21:10520974-10520975" ...
#> $ probeID : chr [1:32543] "cg18453969" "cg18453969" "cg18453969" "cg18453969" ...
#> $ target_symbol : chr [1:32543] "TPTE" "TPTE" "TPTE" "TPTE" ...
#> $ target : chr [1:32543] "ENSG00000274391" "ENSG00000274391" "ENSG00000274391" "ENSG00000274391" ...
#> $ TF_symbol : chr [1:32543] "BHLHE22" "PITX1" "GSC" "GSC2" ...
#> $ TF : chr [1:32543] "ENSG00000180828" "ENSG00000069011" "ENSG00000133937" "ENSG00000063515" ...
#> $ distance_region_target_tss: num [1:32543] -577 -577 -577 -577 -577 -577 -577 -577 -577 -577 ...
triplet.promoter$distance_region_target_tss %>% range
#> [1] -1999 1989
triplet.promoter %>% head
#> # A tibble: 6 × 7
#> regionID probeID target_symbol target TF_symbol TF distance_region_targ…¹
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 chr21:10520974-10520975 cg18453969 TPTE ENSG00000… BHLHE22 ENSG… -577
#> 2 chr21:10520974-10520975 cg18453969 TPTE ENSG00000… PITX1 ENSG… -577
#> 3 chr21:10520974-10520975 cg18453969 TPTE ENSG00000… GSC ENSG… -577
#> 4 chr21:10520974-10520975 cg18453969 TPTE ENSG00000… GSC2 ENSG… -577
#> 5 chr21:10520974-10520975 cg18453969 TPTE ENSG00000… HIC2 ENSG… -577
#> 6 chr21:10520974-10520975 cg18453969 TPTE ENSG00000… MEIS1 ENSG… -577
#> # ℹ abbreviated name: ¹distance_region_target_tssNote that there may be multiple rows for a CpG region, when multiple target gene and/or TFs are found close to it.
4.2 Evaluating the regulatory potential of CpGs (or DMRs)
Because TF binding to DNA can be influenced by (or influences) DNA methylation levels nearby (Yin et al. 2017), 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:
- An input DNA methylation matrix
- An input Gene expression matrix
- The created triplet data frame
4.2.1 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.
\[log_2(RNA target) \sim log_2(TF) + \text{DNAm Group} + log_2(TF) * \text{DNAm Group}\]
results.interaction.model <- interaction_model(
triplet = triplet.promoter,
dnam = dna.met.chr21.se,
exp = gene.exp.chr21.se,
dnam.group.threshold = 0.1,
sig.threshold = 0.05,
fdr = T,
stage.wise.analysis = FALSE,
filter.correlated.tf.exp.dnam = F,
filter.triplet.by.sig.term = T
)
#> Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in dplyr 1.1.0.
#> ℹ Please use `reframe()` instead.
#> ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()` always returns an
#> ungrouped data frame and adjust accordingly.
#> ℹ The deprecated feature was likely used in the MethReg package.
#> Please report the issue at <https://github.com/TransBioInfoLab/MethReg/issues/>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.The output of interaction_model function will be a data frame with the following variables:
<variable>_pvalue: p-value for a tested variable (methylation or TF), given the other variables included in the model.<variable>_estimate: 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:
RLM_DNAmGroup_pvalue: p-value for binary DNA methylation variableRLM_DNAmGroup_estimate: estimated DNA methylation effect
- direct effect of TF:
RLM_TF_pvalue: p-value for TF expressionRLM_TF_estimate: estimated TF effect
- synergistic effects of methylation and TF:
RLM_DNAmGroup:TF_pvalue: : p-value for DNA methylation by TF interactionRLM_DNAmGroup:TF_estimate: estimated DNA methylation by TF interaction effect
Selecting the significant triplet!
RLM_DNAmGroup:TF_pvalue: raw p-value obtained from fitting robust linear model, without any multiple comparison correction
If stage.wise.analysis = TRUE is selected, MethReg implements the stage-wise procedure for testing interactions by first aggregating all CpG-TF-target gene triplets associated with the same CpG as a group. Then it performs two steps:
In the screening step, MethReg tests the null hypothesis that any of the individual triplets mapped to a CpG has a significant DNAm × TF effect.
In the confirmation step, MethReg tests each triplet associated with the CpG selected in the screening step while controlling FWER as described in Van den Berge et al. (2017, PMID: 28784146)
RLM_DNAmGroup:TF_region_stage_wise_adj_pvalue: p-value obtained in step (1) described above
RLM_DNAmGroup:TF_triplet_stage_wise_adj_pvalue: this is multiple comparison corrected p-value for each triplet obtained in step (2) above, and should be used to select triplets
For a easier visualization of the results please refer to the export_results_to_table function.
# Results for quartile model
results.interaction.model %>% dplyr::select(
c(1,4,5,grep("RLM",colnames(results.interaction.model)))
) %>% head
#> regionID target TF_symbol RLM_DNAmGroup_pvalue RLM_DNAmGroup_fdr
#> 1 chr21:17611485-17611486 ENSG00000280594 BACH1 0.008769449 0.01753890
#> 2 chr21:41879303-41879304 ENSG00000141956 ETS2 0.044083747 0.08816749
#> 3 chr21:46286242-46286243 ENSG00000182362 ETS2 0.008407302 0.01681460
#> RLM_TF_pvalue RLM_TF_fdr RLM_DNAmGroup:TF_pvalue RLM_DNAmGroup:TF_fdr RLM_DNAmGroup_estimate
#> 1 0.02659345 0.05318690 0.008160234 0.01632047 -16.782510
#> 2 0.01651490 0.03302981 0.059441204 0.11888241 9.235738
#> 3 0.02598837 0.05197675 0.012364430 0.02472886 -16.085545
#> RLM_TF_estimate RLM_DNAmGroup:TF_estimate
#> 1 0.4514233 0.9459172
#> 2 0.3990901 -0.3929210
#> 3 -0.3510502 0.67298564.2.2 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.
\[\text{Stratified Model: } log_2(RNA target) \sim log_2(TF)\]
results.stratified.model <- stratified_model(
triplet = results.interaction.model,
dnam = dna.met.chr21.se,
exp = gene.exp.chr21.se,
dnam.group.threshold = 0.25
)results.stratified.model %>% head
#> regionID probeID target_symbol target TF_symbol TF
#> 1 chr21:17611485-17611486 cg04464940 BTG3-AS1 ENSG00000280594 BACH1 ENSG00000156273
#> 2 chr21:41879303-41879304 cg17014849 PRDM15 ENSG00000141956 ETS2 ENSG00000157557
#> 3 chr21:46286242-46286243 cg21945459 YBEY ENSG00000182362 ETS2 ENSG00000157557
#> distance_region_target_tss target_region met.IQR RLM_DNAmGroup_pvalue RLM_DNAmGroup_fdr
#> 1 -257 chr21:17611744-17633199 0 0.008769449 0.01753890
#> 2 177 chr21:41798225-41879482 0 0.044083747 0.08816749
#> 3 -98 chr21:46286342-46297751 0 0.008407302 0.01681460
#> RLM_TF_pvalue RLM_TF_fdr RLM_DNAmGroup:TF_pvalue RLM_DNAmGroup:TF_fdr RLM_DNAmGroup_estimate
#> 1 0.02659345 0.05318690 0.008160234 0.01632047 -16.782510
#> 2 0.01651490 0.03302981 0.059441204 0.11888241 9.235738
#> 3 0.02598837 0.05197675 0.012364430 0.02472886 -16.085545
#> RLM_TF_estimate RLM_DNAmGroup:TF_estimate Model.quantile
#> 1 0.4514233 0.9459172 Robust Linear Model
#> 2 0.3990901 -0.3929210 Robust Linear Model
#> 3 -0.3510502 0.6729856 Robust Linear Model
#> Target_gene_DNAm_high_vs_Target_gene_DNAm_low_wilcoxon_pvalue
#> 1 0.03038282
#> 2 0.03038282
#> 3 0.03038282
#> TF_DNAm_high_vs_TF_DNAm_low_wilcoxon_pvalue
#> 1 0.03038282
#> 2 0.47048642
#> 3 0.31232142
#> % of target genes not expressed in DNAm_low and DNAm_high DNAm_low_RLM_target_vs_TF_pvalue
#> 1 0 % 0.1650152
#> 2 0 % 0.5185605
#> 3 0 % 0.7965329
#> DNAm_low_RLM_target_vs_TF_estimate DNAm_high_RLM_target_vs_TF_pvalue
#> 1 0.4487346 0.03507941
#> 2 0.1102264 0.52581775
#> 3 0.3231508 0.09615865
#> DNAm_high_RLM_target_vs_TF_estimate DNAm.effect TF.role
#> 1 0.6058538 <NA> NA
#> 2 0.3056055 ns NA
#> 3 1.1994286 <NA> NA4.2.3 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.
plots <- plot_interaction_model(
triplet.results = results.interaction.model[1,],
dnam = dna.met.chr21.se,
exp = gene.exp.chr21.se,
dnam.group.threshold = 0.25
)plots
#> $`chr21:17611485-17611486_TF_ENSG00000156273_target_ENSG00000280594`
Figure 3: 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 shows how much TF activity on target gene expression levels vary varies by DNA methylation levels. When TF by methylation interaction is significant (Section 4.1), we expect the association between TF and target gene expression to 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 the 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 = 0.971) are not as significant as the association in stratified analysis in high methylation samples (p = 0.0096). This demonstrates that by additionally modeling DNA methylation, we can also nominate TF – target associations that might have been missed otherwise.
Note that because of the small sample size (only 38 samples) included in this example for illustration, the P-value for high methylation samples (p = 0.096)
is only marginally significant.
In real data analysis, we
expect MethReg to work well with at least 100 matched samples measured
with both methylations and gene expressions,
and we recommend using a more stringent significance threshold (i.e., FDR < 0.05).
See details in our published paper (Silva et al. 2022, PMID: 35100398).
4.3 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.
Figure 4: Scenarios modeled by MethReg
A and B: DNA methylation decreases TF activity. In A TF is a repressor of the target gene, while in B TF is an activator. C and D: DNA methylation increases TF activity. In C TF is a repressor of the target gene, while in D TF is an activator. E and F: DNA methylation inverts the TF role. In E when DNA methylation levels are low the TF works as a repressor, when DNA methylation levels are high the TF acts as an activator. In F when the DNA methylation levels are low the TF works as an activator, when methylation levels are high the TF acts a repressor.
5 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:
\[methylation.Mvalues \sim covariate_{1} + covariate_{2} + ... + covariate_{N}\]
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()gene.exp.chr21.residuals[1:5,1:5]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()dna.met.chr21.residuals[1:5,1:5]The models described in Section 4.1 can then be applied to these residuals
data using the interaction_model function:
results <- interaction_model(
triplet = triplet,
dnam = dna.met.chr21.residuals,
exp = gene.exp.chr21.residuals
)6 Calculating enrichment scores
6.1 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
#> # A tibble: 6 × 4
#> tf confidence target mor
#> <chr> <chr> <chr> <dbl>
#> 1 ADNP D ATF7IP 1
#> 2 ADNP D DYRK1A 1
#> 3 ADNP D TLK1 1
#> 4 ADNP D ZMYM4 1
#> 5 ADNP D ABCC1 1
#> 6 ADNP D ABCC6 1rnaseq.tf.es <- get_tf_ES(
exp = gene.exp.chr21.se %>% SummarizedExperiment::assay(),
regulons = regulons.dorothea
)
#> Warning in run_viper.matrix(input = exp, regulons = regulons, options = list(method = "scale", :
#> This function is deprecated, please check the package decoupleR to infer activities.rnaseq.tf.es[1:4,1:4]
#> TCGA-3L-AA1B-01A TCGA-4N-A93T-01A TCGA-4T-AA8H-01A TCGA-5M-AAT4-01A
#> ENSG00000101126 0.5107344 -2.1708007 -1.4257370 -2.29950338
#> ENSG00000101544 -1.0332572 -0.2855890 0.8007206 0.14008977
#> ENSG00000139154 -0.9773648 0.2275618 0.9888562 -2.01317607
#> ENSG00000160224 0.2112202 -0.9044230 0.1509887 -0.017175186.2 Using dorothea and GSVA
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]]})library(GSVA)
rnaseq.tf.es.gsva <- gsva(
expr = gene.exp.chr21.se %>% SummarizedExperiment::assay(),
gset.idx.list = regulons.dorothea.list,
method = "gsva",
kcdf = "Gaussian",
abs.ranking = TRUE,
min.sz = 5,
max.sz = Inf,
parallel.sz = 1L,
mx.diff = TRUE,
ssgsea.norm = TRUE,
verbose = TRUE
)7 Session information
sessionInfo()
#> R version 4.4.0 beta (2024-04-15 r86425)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
#> [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] BSgenome.Hsapiens.UCSC.hg38_1.4.5 BSgenome_1.72.0
#> [3] rtracklayer_1.64.0 BiocIO_1.14.0
#> [5] Biostrings_2.72.0 XVector_0.44.0
#> [7] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
#> [9] IRanges_2.38.0 S4Vectors_0.42.0
#> [11] MethReg_1.14.0 sesameData_1.21.10
#> [13] ExperimentHub_2.12.0 AnnotationHub_3.12.0
#> [15] BiocFileCache_2.12.0 dbplyr_2.5.0
#> [17] BiocGenerics_0.50.0 dplyr_1.1.4
#> [19] BiocStyle_2.32.0
#>
#> loaded via a namespace (and not attached):
#> [1] splines_4.4.0 bitops_1.0-7 filelock_1.0.3
#> [4] tibble_3.2.1 R.oo_1.26.0 preprocessCore_1.66.0
#> [7] XML_3.99-0.16.1 DirichletMultinomial_1.46.0 lifecycle_1.0.4
#> [10] pwalign_1.0.0 rstatix_0.7.2 doParallel_1.0.17
#> [13] lattice_0.22-6 MASS_7.3-60.2 backports_1.4.1
#> [16] magrittr_2.0.3 openxlsx_4.2.5.2 plotly_4.10.4
#> [19] sass_0.4.9 rmarkdown_2.26 jquerylib_0.1.4
#> [22] yaml_2.3.8 zip_2.3.1 cowplot_1.1.3
#> [25] DBI_1.2.2 CNEr_1.40.0 RColorBrewer_1.1-3
#> [28] abind_1.4-5 zlibbioc_1.50.0 sfsmisc_1.1-18
#> [31] purrr_1.0.2 mixtools_2.0.0 R.utils_2.12.3
#> [34] RCurl_1.98-1.14 pracma_2.4.4 rappdirs_0.3.3
#> [37] GenomeInfoDbData_1.2.12 seqLogo_1.70.0 parallelly_1.37.1
#> [40] annotate_1.82.0 codetools_0.2-20 DelayedArray_0.30.0
#> [43] tidyselect_1.2.1 farver_2.1.1 UCSC.utils_1.0.0
#> [46] matrixStats_1.3.0 GenomicAlignments_1.40.0 jsonlite_1.8.8
#> [49] wheatmap_0.2.0 e1071_1.7-14 survival_3.6-4
#> [52] motifmatchr_1.26.0 iterators_1.0.14 foreach_1.5.2
#> [55] segmented_2.0-4 tools_4.4.0 progress_1.2.3
#> [58] dorothea_1.15.1 TFMPvalue_0.0.9 Rcpp_1.0.12
#> [61] glue_1.7.0 gridExtra_2.3 SparseArray_1.4.0
#> [64] mgcv_1.9-1 decoupleR_2.10.0 xfun_0.43
#> [67] MatrixGenerics_1.16.0 withr_3.0.0 BiocManager_1.30.22
#> [70] fastmap_1.1.1 fansi_1.0.6 caTools_1.18.2
#> [73] digest_0.6.35 R6_2.5.1 mime_0.12
#> [76] colorspace_2.1-0 GO.db_3.19.1 gtools_3.9.5
#> [79] poweRlaw_0.80.0 RSQLite_2.3.6 R.methodsS3_1.8.2
#> [82] utf8_1.2.4 tidyr_1.3.1 generics_0.1.3
#> [85] data.table_1.15.4 class_7.3-22 htmlwidgets_1.6.4
#> [88] prettyunits_1.2.0 httr_1.4.7 S4Arrays_1.4.0
#> [91] TFBSTools_1.42.0 pkgconfig_2.0.3 gtable_0.3.5
#> [94] blob_1.2.4 htmltools_0.5.8.1 carData_3.0-5
#> [97] bookdown_0.39 scales_1.3.0 Biobase_2.64.0
#> [100] png_0.1-8 knitr_1.46 tzdb_0.4.0
#> [103] reshape2_1.4.4 rjson_0.2.21 nlme_3.1-164
#> [106] curl_5.2.1 proxy_0.4-27 cachem_1.0.8
#> [109] stringr_1.5.1 BiocVersion_3.19.1 KernSmooth_2.23-22
#> [112] parallel_4.4.0 viper_1.38.0 AnnotationDbi_1.66.0
#> [115] restfulr_0.0.15 pillar_1.9.0 grid_4.4.0
#> [118] vctrs_0.6.5 pscl_1.5.9 ggpubr_0.6.0
#> [121] car_3.1-2 xtable_1.8-4 JASPAR2024_0.99.6
#> [124] evaluate_0.23 readr_2.1.5 tinytex_0.50
#> [127] magick_2.8.3 cli_3.6.2 compiler_4.4.0
#> [130] Rsamtools_2.20.0 rlang_1.1.3 crayon_1.5.2
#> [133] ggsignif_0.6.4 labeling_0.4.3 bcellViper_1.39.0
#> [136] plyr_1.8.9 stringi_1.8.3 viridisLite_0.4.2
#> [139] BiocParallel_1.38.0 munsell_0.5.1 lazyeval_0.2.2
#> [142] Matrix_1.7-0 hms_1.1.3 sesame_1.22.0
#> [145] bit64_4.0.5 ggplot2_3.5.1 KEGGREST_1.44.0
#> [148] SummarizedExperiment_1.34.0 highr_0.10 kernlab_0.9-32
#> [151] broom_1.0.5 memoise_2.0.1 bslib_0.7.0
#> [154] bit_4.0.5Bibliography
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