docs/tutorial.md
In perishky/dmrff: Identifying differentially methylated regions efficiently with power and control
title: "Identifying differentially methylated regions using dmrff"
output:
pdf_document: default
word_document:
highlight: tango
html_document:
toc: true
Identifying differentially methylated regions using dmrff
Download and prepare an example DNA methylation dataset
We'll use a small cord blood DNA methylation dataset
available on GEO: GSE79056.
library(geograbi) ## https://github.com/yousefi138
## Loading required package: XML
## Loading required package: data.table
samples <- geograbi.get.samples("GSE79056")
## Tue Dec 12 19:02:34 2023 reading
vars <- geograbi.extract.characteristics(samples)
methylation <- geograbi.get.data("GSE79056") ## 86Mb
## Tue Dec 12 19:02:35 2023 reading
We'll be investigating associations with gestational age.
colnames(vars)[which(colnames(vars) == "ga weeks")] <- "ga"
vars$ga <- as.numeric(vars$ga)
Test DNA methylation associations
Prepare surrogate variables to handle unknown confounding.
library(sva, quietly=T)
## This is mgcv 1.8-41. For overview type 'help("mgcv-package")'.
## construct EWAS model
mod <- model.matrix(~ gender + ga, vars)
## construct null model
mod0 <- mod[,1]
## to save time, SVA will be applied to a random selection of 5000 CpG sites
set.seed(20191029)
random.idx <- sample(1:nrow(methylation), 5000)
methylation.sva <- methylation[random.idx,]
## missing methylation values are replaced with mean values
methylation.mean <- rowMeans(methylation.sva, na.rm=T)
idx <- which(is.na(methylation.sva), arr.ind=T)
if (nrow(idx) > 0)
methylation.sva[idx] <- methylation.mean[idx[,"row"]]
sva.fit <- sva(methylation.sva, mod=mod, mod0=mod0)
## Number of significant surrogate variables is: 3
## Iteration (out of 5 ):1 2 3 4 5
Add surrogate variables to the model.
design <- cbind(mod, sva.fit$sv)
Test the associations using limma.
library(limma)
fit <- lmFit(methylation, design)
fit <- eBayes(fit)
Save the summary statistics for gestational age.
stats <- data.frame(estimate=fit$coefficients[,"ga"],
se=sqrt(fit$s2.post) * fit$stdev.unscaled[,"ga"],
p.value=fit$p.value[,"ga"])
Add genomic coordinates for CpG sites (Illumina Beadchips)
dmrff and all other methods for identifying differentially methylated regions
require information about the genomic locations of all CpG sites.
If your data was generated using one of the Illumina BeadChips, then it is possible
to annotate your EWAS summary statistics using the appropriate
Bioconductor annotation package.
For the Illumina 450K microaray, we use the
IlluminaHumanMethylation450kanno.ilmn12.hg19 package.
if (!require(IlluminaHumanMethylation450kanno.ilmn12.hg19, quietly=T)) {
install.packages("BiocManager")
BiocManager::install("IlluminaHumanMethylation450kanno.ilmn12.hg19")
}
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## Welcome to Bioconductor
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## strsplit
## locfit 1.5-9.6 2022-07-11
## Setting options('download.file.method.GEOquery'='auto')
## Setting options('GEOquery.inmemory.gpl'=FALSE)
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
If we were using the more recent Illumina MethylationEPIC BeadChip,
then we would load the IlluminaHumanMethylationEPICanno.ilm10b2.hg19 package instead.
We construct an annotation data frame as follows:
data(list="IlluminaHumanMethylation450kanno.ilmn12.hg19")
data(Locations)
data(Other)
annotation <- cbind(as.data.frame(Locations), as.data.frame(Other))
We then add the annotation to the stats object
(Warning: we assume that the row names of the stats object
are the Illumina CpG site identifiers, e.g. cg05775921).
common <- intersect(rownames(methylation), rownames(annotation))
annotation <- annotation[match(common, rownames(annotation)),]
stats <- stats[match(common, rownames(stats)),]
methylation <- methylation[match(common, rownames(methylation)),]
stats <- cbind(stats, annotation)
Apply dmrff to summary statistics
If dmrff is not already installed, we install it and then load it:
if (!require(dmrff, quietly=T)) {
library(devtools)
install_github("perishky/dmrff")
}
library(dmrff)
Below we assume that you have already performed an epigenome-wide association analysis
and have loaded your summary statistics in R as a data frame stats
which has the following columns:
- estimate (regression coefficient),
- se (standard error of the coefficient),
- p.value,
- chr (chromosome of the CpG site),
- pos (position of the CpG site on the chromosome).
We also assume that you have loaded your DNA methylation dataset in R as matrix methylation
for which rows correspond to CpG sites and columns to samples.
The DNA methylation levels are necessary for dmrff to calculate
and adjust for dependencies between CpG sites.
dmrff can then be applied as follows.
dmrs <- dmrff(estimate=stats$estimate,
se=stats$se,
p.value=stats$p.value,
methylation=methylation,
chr=stats$chr,
pos=stats$pos,
maxgap=500,
verbose=T)
## [dmrff.candidates] Tue Dec 12 19:03:40 2023 Found 74588 candidate regions.
The algorithm will then identify differentially methylated regions by:
- Identifying regions composed of CpG sites with EWAS p-values < 0.05 and consist direction of effect (at most 500bp between CpG sites -- see
maxgap parameter).
- Meta-analysing EWAS statistics within each region as well as sub-regions to identify the sub-region with lowest meta-analysed p-value.
- Adjusting meta-analysed p-values for multiple tests (all EWAS tests + sub-region meta-analysis tests).
- Finally, returning the DMR results.
Our output dmrs is a data frame listing all genomic regions tested
along with summary statistics for each.
We just keep regions with > 1 CpG site and Bonferroni adjusted p < 0.05.
dmrs <- dmrs[which(dmrs$p.adjust < 0.05 & dmrs$n > 1),]
There are 1368 such DMRs.
Here are the first 10 regions:
kable(dmrs[1:10,])
| |chr | start| end| n| B| S| estimate| se| z| p.value| p.adjust|
|:--|:-----|--------:|--------:|--:|----------:|---------:|----------:|---------:|----------:|-------:|---------:|
|1 |chr6 | 32119616| 32121420| 39| 0.0594703| 0.0036198| 0.0594703| 0.0036198| 16.429200| 0| 0.0000000|
|3 |chr6 | 33245474| 33245779| 17| -0.1546681| 0.0117290| -0.1546681| 0.0117290| -13.186791| 0| 0.0000000|
|5 |chr6 | 33245893| 33246094| 7| -0.1404528| 0.0162813| -0.1404528| 0.0162813| -8.626631| 0| 0.0000000|
|10 |chr6 | 31867847| 31869088| 32| -0.0591950| 0.0041669| -0.0591950| 0.0041669| -14.205932| 0| 0.0000000|
|12 |chr6 | 32036179| 32038958| 53| 0.0392814| 0.0050978| 0.0392814| 0.0050978| 7.705565| 0| 0.0000000|
|14 |chr11 | 14993378| 14993642| 4| 0.1616448| 0.0199695| 0.1616448| 0.0199695| 8.094569| 0| 0.0000000|
|15 |chr11 | 14994230| 14994606| 5| 0.1211192| 0.0196781| 0.1211192| 0.0196781| 6.155039| 0| 0.0004818|
|23 |chr6 | 30458158| 30459295| 11| -0.1580182| 0.0116069| -0.1580182| 0.0116069| -13.614168| 0| 0.0000000|
|24 |chr6 | 30459540| 30460600| 9| -0.0923506| 0.0112549| -0.0923506| 0.0112549| -8.205369| 0| 0.0000000|
|27 |chr17 | 41278141| 41278425| 10| -0.1191146| 0.0189142| -0.1191146| 0.0189142| -6.297623| 0| 0.0001940|
We can also list the information for each CpG site in these regions.
sites <- dmrff.sites(dmrs, stats$chr, stats$pos)
sites <- cbind(sites, stats[sites$site, c("UCSC_RefGene_Name", "estimate", "p.value")])
sites <- cbind(sites, dmr=dmrs[sites$region,c("start","end","z","p.adjust")])
Here are the sites in the largest DMR:
max.region <- which.max(dmrs$n)
kable(sites[which(sites$region == max.region),],row.names=F)
| region| site|chr | pos|UCSC_RefGene_Name | estimate| p.value| dmr.start| dmr.end| dmr.z| dmr.p.adjust|
|------:|------:|:----|--------:|:-----------------|----------:|---------:|---------:|--------:|--------:|------------:|
| 5| 403116|chr6 | 32036179|TNXB | 0.0010315| 0.0109635| 32036179| 32038958| 7.705565| 0|
| 5| 418198|chr6 | 32036258|TNXB | 0.0000935| 0.8952558| 32036179| 32038958| 7.705565| 0|
| 5| 409882|chr6 | 32036278|TNXB | 0.0005737| 0.4059512| 32036179| 32038958| 7.705565| 0|
| 5| 70316|chr6 | 32036356|TNXB | 0.0076859| 0.0305838| 32036179| 32038958| 7.705565| 0|
| 5| 44051|chr6 | 32036404|TNXB | 0.0007714| 0.2085628| 32036179| 32038958| 7.705565| 0|
| 5| 465690|chr6 | 32036449|TNXB | 0.0009136| 0.0543140| 32036179| 32038958| 7.705565| 0|
| 5| 356956|chr6 | 32036530|TNXB | 0.0008525| 0.1268730| 32036179| 32038958| 7.705565| 0|
| 5| 406883|chr6 | 32036532|TNXB | 0.0012313| 0.0300202| 32036179| 32038958| 7.705565| 0|
| 5| 90566|chr6 | 32036611|TNXB | 0.0006175| 0.2440241| 32036179| 32038958| 7.705565| 0|
| 5| 14425|chr6 | 32036677|TNXB | 0.0024381| 0.2242297| 32036179| 32038958| 7.705565| 0|
| 5| 338599|chr6 | 32036709|TNXB | 0.0000897| 0.8567340| 32036179| 32038958| 7.705565| 0|
| 5| 351435|chr6 | 32036724|TNXB | 0.0000125| 0.9839411| 32036179| 32038958| 7.705565| 0|
| 5| 301883|chr6 | 32036743|TNXB | 0.0006430| 0.2947373| 32036179| 32038958| 7.705565| 0|
| 5| 231708|chr6 | 32036822|TNXB | 0.0037987| 0.0000617| 32036179| 32038958| 7.705565| 0|
| 5| 462157|chr6 | 32036848|TNXB | 0.0008652| 0.2164398| 32036179| 32038958| 7.705565| 0|
| 5| 390942|chr6 | 32036897|TNXB | 0.0010422| 0.1459314| 32036179| 32038958| 7.705565| 0|
| 5| 475183|chr6 | 32036911|TNXB | -0.0001132| 0.8998336| 32036179| 32038958| 7.705565| 0|
| 5| 330755|chr6 | 32036954|TNXB | 0.0028732| 0.0002115| 32036179| 32038958| 7.705565| 0|
| 5| 240567|chr6 | 32037018|TNXB | 0.0024086| 0.0001065| 32036179| 32038958| 7.705565| 0|
| 5| 291118|chr6 | 32037097|TNXB | -0.0003098| 0.9055158| 32036179| 32038958| 7.705565| 0|
| 5| 352756|chr6 | 32037148|TNXB | 0.0009316| 0.1774843| 32036179| 32038958| 7.705565| 0|
| 5| 103583|chr6 | 32037290|TNXB | 0.0020771| 0.0009626| 32036179| 32038958| 7.705565| 0|
| 5| 138987|chr6 | 32037339|TNXB | 0.0012636| 0.0084907| 32036179| 32038958| 7.705565| 0|
| 5| 38523|chr6 | 32037342|TNXB | 0.0017828| 0.0004507| 32036179| 32038958| 7.705565| 0|
| 5| 417928|chr6 | 32037421|TNXB | 0.0006383| 0.2333117| 32036179| 32038958| 7.705565| 0|
| 5| 160017|chr6 | 32037426|TNXB | 0.0009770| 0.1394028| 32036179| 32038958| 7.705565| 0|
| 5| 179334|chr6 | 32037453|TNXB | 0.0011397| 0.0251615| 32036179| 32038958| 7.705565| 0|
| 5| 379817|chr6 | 32037474|TNXB | 0.0004407| 0.4323696| 32036179| 32038958| 7.705565| 0|
| 5| 52129|chr6 | 32037587|TNXB | 0.0009903| 0.1194331| 32036179| 32038958| 7.705565| 0|
| 5| 60243|chr6 | 32037602|TNXB | 0.0004996| 0.4616771| 32036179| 32038958| 7.705565| 0|
| 5| 272610|chr6 | 32037632|TNXB | 0.0002757| 0.6319254| 32036179| 32038958| 7.705565| 0|
| 5| 386420|chr6 | 32037653|TNXB | 0.0010359| 0.0942346| 32036179| 32038958| 7.705565| 0|
| 5| 300711|chr6 | 32037735|TNXB | 0.0015226| 0.0277306| 32036179| 32038958| 7.705565| 0|
| 5| 428514|chr6 | 32037751|TNXB | 0.0003291| 0.5704414| 32036179| 32038958| 7.705565| 0|
| 5| 110400|chr6 | 32037800|TNXB | -0.0004129| 0.7202410| 32036179| 32038958| 7.705565| 0|
| 5| 449994|chr6 | 32037847|TNXB | 0.0028153| 0.0000569| 32036179| 32038958| 7.705565| 0|
| 5| 76776|chr6 | 32037913|TNXB | 0.0006452| 0.1327238| 32036179| 32038958| 7.705565| 0|
| 5| 87741|chr6 | 32037916|TNXB | 0.0014233| 0.0020986| 32036179| 32038958| 7.705565| 0|
| 5| 444167|chr6 | 32037978|TNXB | 0.0012293| 0.0209276| 32036179| 32038958| 7.705565| 0|
| 5| 5081|chr6 | 32038011|TNXB | -0.0010160| 0.2819558| 32036179| 32038958| 7.705565| 0|
| 5| 448813|chr6 | 32038027|TNXB | 0.0017132| 0.0062513| 32036179| 32038958| 7.705565| 0|
| 5| 298918|chr6 | 32038045|TNXB | 0.0009735| 0.3071656| 32036179| 32038958| 7.705565| 0|
| 5| 416617|chr6 | 32038097|TNXB | 0.0008797| 0.1731802| 32036179| 32038958| 7.705565| 0|
| 5| 434939|chr6 | 32038155|TNXB | 0.0018101| 0.0032325| 32036179| 32038958| 7.705565| 0|
| 5| 470733|chr6 | 32038177|TNXB | 0.0023991| 0.0048218| 32036179| 32038958| 7.705565| 0|
| 5| 337001|chr6 | 32038185|TNXB | 0.0025098| 0.0009209| 32036179| 32038958| 7.705565| 0|
| 5| 389831|chr6 | 32038230|TNXB | 0.0016509| 0.0134805| 32036179| 32038958| 7.705565| 0|
| 5| 276348|chr6 | 32038462|TNXB | 0.0010248| 0.0718441| 32036179| 32038958| 7.705565| 0|
| 5| 374532|chr6 | 32038541|TNXB | 0.0027082| 0.0002163| 32036179| 32038958| 7.705565| 0|
| 5| 272153|chr6 | 32038747|TNXB | 0.0028779| 0.0028012| 32036179| 32038958| 7.705565| 0|
| 5| 411763|chr6 | 32038881|TNXB | 0.0020330| 0.0027048| 32036179| 32038958| 7.705565| 0|
| 5| 254265|chr6 | 32038887|TNXB | 0.0006585| 0.3466269| 32036179| 32038958| 7.705565| 0|
| 5| 13100|chr6 | 32038958|TNXB | 0.0026444| 0.0000209| 32036179| 32038958| 7.705565| 0|
Here we generate a simple plot of the largest DMR:
dmrff.plot(
dmrs$chr[max.region], dmrs$start[max.region], dmrs$end[max.region],
stats$estimate, stats$se, stats$chr, stats$pos, ci=T, expand=1)
## Loading required package: ggplot2
perishky/dmrff documentation built on Jan. 4, 2024, 10:23 p.m.
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title: "Identifying differentially methylated regions using dmrff" output: pdf_document: default word_document: highlight: tango html_document: toc: true
Identifying differentially methylated regions using dmrff
Download and prepare an example DNA methylation dataset
We'll use a small cord blood DNA methylation dataset available on GEO: GSE79056.
library(geograbi) ## https://github.com/yousefi138
## Loading required package: XML
## Loading required package: data.table
samples <- geograbi.get.samples("GSE79056")
## Tue Dec 12 19:02:34 2023 reading
vars <- geograbi.extract.characteristics(samples)
methylation <- geograbi.get.data("GSE79056") ## 86Mb
## Tue Dec 12 19:02:35 2023 reading
We'll be investigating associations with gestational age.
colnames(vars)[which(colnames(vars) == "ga weeks")] <- "ga"
vars$ga <- as.numeric(vars$ga)
Test DNA methylation associations
Prepare surrogate variables to handle unknown confounding.
library(sva, quietly=T)
## This is mgcv 1.8-41. For overview type 'help("mgcv-package")'.
## construct EWAS model
mod <- model.matrix(~ gender + ga, vars)
## construct null model
mod0 <- mod[,1]
## to save time, SVA will be applied to a random selection of 5000 CpG sites
set.seed(20191029)
random.idx <- sample(1:nrow(methylation), 5000)
methylation.sva <- methylation[random.idx,]
## missing methylation values are replaced with mean values
methylation.mean <- rowMeans(methylation.sva, na.rm=T)
idx <- which(is.na(methylation.sva), arr.ind=T)
if (nrow(idx) > 0)
methylation.sva[idx] <- methylation.mean[idx[,"row"]]
sva.fit <- sva(methylation.sva, mod=mod, mod0=mod0)
## Number of significant surrogate variables is: 3
## Iteration (out of 5 ):1 2 3 4 5
Add surrogate variables to the model.
design <- cbind(mod, sva.fit$sv)
Test the associations using limma.
library(limma)
fit <- lmFit(methylation, design)
fit <- eBayes(fit)
Save the summary statistics for gestational age.
stats <- data.frame(estimate=fit$coefficients[,"ga"],
se=sqrt(fit$s2.post) * fit$stdev.unscaled[,"ga"],
p.value=fit$p.value[,"ga"])
Add genomic coordinates for CpG sites (Illumina Beadchips)
dmrff and all other methods for identifying differentially methylated regions
require information about the genomic locations of all CpG sites.
If your data was generated using one of the Illumina BeadChips, then it is possible to annotate your EWAS summary statistics using the appropriate Bioconductor annotation package.
For the Illumina 450K microaray, we use the
IlluminaHumanMethylation450kanno.ilmn12.hg19 package.
if (!require(IlluminaHumanMethylation450kanno.ilmn12.hg19, quietly=T)) {
install.packages("BiocManager")
BiocManager::install("IlluminaHumanMethylation450kanno.ilmn12.hg19")
}
##
## Attaching package: 'BiocGenerics'
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## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
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## The following objects are masked from 'package:matrixStats':
##
## anyMissing, rowMedians
##
## Attaching package: 'Biostrings'
## The following object is masked from 'package:base':
##
## strsplit
## locfit 1.5-9.6 2022-07-11
## Setting options('download.file.method.GEOquery'='auto')
## Setting options('GEOquery.inmemory.gpl'=FALSE)
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
If we were using the more recent Illumina MethylationEPIC BeadChip,
then we would load the IlluminaHumanMethylationEPICanno.ilm10b2.hg19 package instead.
We construct an annotation data frame as follows:
data(list="IlluminaHumanMethylation450kanno.ilmn12.hg19")
data(Locations)
data(Other)
annotation <- cbind(as.data.frame(Locations), as.data.frame(Other))
We then add the annotation to the stats object
(Warning: we assume that the row names of the stats object
are the Illumina CpG site identifiers, e.g. cg05775921).
common <- intersect(rownames(methylation), rownames(annotation))
annotation <- annotation[match(common, rownames(annotation)),]
stats <- stats[match(common, rownames(stats)),]
methylation <- methylation[match(common, rownames(methylation)),]
stats <- cbind(stats, annotation)
Apply dmrff to summary statistics
If dmrff is not already installed, we install it and then load it:
if (!require(dmrff, quietly=T)) {
library(devtools)
install_github("perishky/dmrff")
}
library(dmrff)
Below we assume that you have already performed an epigenome-wide association analysis
and have loaded your summary statistics in R as a data frame stats
which has the following columns:
- estimate (regression coefficient),
- se (standard error of the coefficient),
- p.value,
- chr (chromosome of the CpG site),
- pos (position of the CpG site on the chromosome).
We also assume that you have loaded your DNA methylation dataset in R as matrix methylation
for which rows correspond to CpG sites and columns to samples.
The DNA methylation levels are necessary for dmrff to calculate
and adjust for dependencies between CpG sites.
dmrff can then be applied as follows.
dmrs <- dmrff(estimate=stats$estimate,
se=stats$se,
p.value=stats$p.value,
methylation=methylation,
chr=stats$chr,
pos=stats$pos,
maxgap=500,
verbose=T)
## [dmrff.candidates] Tue Dec 12 19:03:40 2023 Found 74588 candidate regions.
The algorithm will then identify differentially methylated regions by:
- Identifying regions composed of CpG sites with EWAS p-values < 0.05 and consist direction of effect (at most 500bp between CpG sites -- see
maxgapparameter). - Meta-analysing EWAS statistics within each region as well as sub-regions to identify the sub-region with lowest meta-analysed p-value.
- Adjusting meta-analysed p-values for multiple tests (all EWAS tests + sub-region meta-analysis tests).
- Finally, returning the DMR results.
Our output dmrs is a data frame listing all genomic regions tested
along with summary statistics for each.
We just keep regions with > 1 CpG site and Bonferroni adjusted p < 0.05.
dmrs <- dmrs[which(dmrs$p.adjust < 0.05 & dmrs$n > 1),]
There are 1368 such DMRs.
Here are the first 10 regions:
kable(dmrs[1:10,])
| |chr | start| end| n| B| S| estimate| se| z| p.value| p.adjust| |:--|:-----|--------:|--------:|--:|----------:|---------:|----------:|---------:|----------:|-------:|---------:| |1 |chr6 | 32119616| 32121420| 39| 0.0594703| 0.0036198| 0.0594703| 0.0036198| 16.429200| 0| 0.0000000| |3 |chr6 | 33245474| 33245779| 17| -0.1546681| 0.0117290| -0.1546681| 0.0117290| -13.186791| 0| 0.0000000| |5 |chr6 | 33245893| 33246094| 7| -0.1404528| 0.0162813| -0.1404528| 0.0162813| -8.626631| 0| 0.0000000| |10 |chr6 | 31867847| 31869088| 32| -0.0591950| 0.0041669| -0.0591950| 0.0041669| -14.205932| 0| 0.0000000| |12 |chr6 | 32036179| 32038958| 53| 0.0392814| 0.0050978| 0.0392814| 0.0050978| 7.705565| 0| 0.0000000| |14 |chr11 | 14993378| 14993642| 4| 0.1616448| 0.0199695| 0.1616448| 0.0199695| 8.094569| 0| 0.0000000| |15 |chr11 | 14994230| 14994606| 5| 0.1211192| 0.0196781| 0.1211192| 0.0196781| 6.155039| 0| 0.0004818| |23 |chr6 | 30458158| 30459295| 11| -0.1580182| 0.0116069| -0.1580182| 0.0116069| -13.614168| 0| 0.0000000| |24 |chr6 | 30459540| 30460600| 9| -0.0923506| 0.0112549| -0.0923506| 0.0112549| -8.205369| 0| 0.0000000| |27 |chr17 | 41278141| 41278425| 10| -0.1191146| 0.0189142| -0.1191146| 0.0189142| -6.297623| 0| 0.0001940|
We can also list the information for each CpG site in these regions.
sites <- dmrff.sites(dmrs, stats$chr, stats$pos)
sites <- cbind(sites, stats[sites$site, c("UCSC_RefGene_Name", "estimate", "p.value")])
sites <- cbind(sites, dmr=dmrs[sites$region,c("start","end","z","p.adjust")])
Here are the sites in the largest DMR:
max.region <- which.max(dmrs$n)
kable(sites[which(sites$region == max.region),],row.names=F)
| region| site|chr | pos|UCSC_RefGene_Name | estimate| p.value| dmr.start| dmr.end| dmr.z| dmr.p.adjust| |------:|------:|:----|--------:|:-----------------|----------:|---------:|---------:|--------:|--------:|------------:| | 5| 403116|chr6 | 32036179|TNXB | 0.0010315| 0.0109635| 32036179| 32038958| 7.705565| 0| | 5| 418198|chr6 | 32036258|TNXB | 0.0000935| 0.8952558| 32036179| 32038958| 7.705565| 0| | 5| 409882|chr6 | 32036278|TNXB | 0.0005737| 0.4059512| 32036179| 32038958| 7.705565| 0| | 5| 70316|chr6 | 32036356|TNXB | 0.0076859| 0.0305838| 32036179| 32038958| 7.705565| 0| | 5| 44051|chr6 | 32036404|TNXB | 0.0007714| 0.2085628| 32036179| 32038958| 7.705565| 0| | 5| 465690|chr6 | 32036449|TNXB | 0.0009136| 0.0543140| 32036179| 32038958| 7.705565| 0| | 5| 356956|chr6 | 32036530|TNXB | 0.0008525| 0.1268730| 32036179| 32038958| 7.705565| 0| | 5| 406883|chr6 | 32036532|TNXB | 0.0012313| 0.0300202| 32036179| 32038958| 7.705565| 0| | 5| 90566|chr6 | 32036611|TNXB | 0.0006175| 0.2440241| 32036179| 32038958| 7.705565| 0| | 5| 14425|chr6 | 32036677|TNXB | 0.0024381| 0.2242297| 32036179| 32038958| 7.705565| 0| | 5| 338599|chr6 | 32036709|TNXB | 0.0000897| 0.8567340| 32036179| 32038958| 7.705565| 0| | 5| 351435|chr6 | 32036724|TNXB | 0.0000125| 0.9839411| 32036179| 32038958| 7.705565| 0| | 5| 301883|chr6 | 32036743|TNXB | 0.0006430| 0.2947373| 32036179| 32038958| 7.705565| 0| | 5| 231708|chr6 | 32036822|TNXB | 0.0037987| 0.0000617| 32036179| 32038958| 7.705565| 0| | 5| 462157|chr6 | 32036848|TNXB | 0.0008652| 0.2164398| 32036179| 32038958| 7.705565| 0| | 5| 390942|chr6 | 32036897|TNXB | 0.0010422| 0.1459314| 32036179| 32038958| 7.705565| 0| | 5| 475183|chr6 | 32036911|TNXB | -0.0001132| 0.8998336| 32036179| 32038958| 7.705565| 0| | 5| 330755|chr6 | 32036954|TNXB | 0.0028732| 0.0002115| 32036179| 32038958| 7.705565| 0| | 5| 240567|chr6 | 32037018|TNXB | 0.0024086| 0.0001065| 32036179| 32038958| 7.705565| 0| | 5| 291118|chr6 | 32037097|TNXB | -0.0003098| 0.9055158| 32036179| 32038958| 7.705565| 0| | 5| 352756|chr6 | 32037148|TNXB | 0.0009316| 0.1774843| 32036179| 32038958| 7.705565| 0| | 5| 103583|chr6 | 32037290|TNXB | 0.0020771| 0.0009626| 32036179| 32038958| 7.705565| 0| | 5| 138987|chr6 | 32037339|TNXB | 0.0012636| 0.0084907| 32036179| 32038958| 7.705565| 0| | 5| 38523|chr6 | 32037342|TNXB | 0.0017828| 0.0004507| 32036179| 32038958| 7.705565| 0| | 5| 417928|chr6 | 32037421|TNXB | 0.0006383| 0.2333117| 32036179| 32038958| 7.705565| 0| | 5| 160017|chr6 | 32037426|TNXB | 0.0009770| 0.1394028| 32036179| 32038958| 7.705565| 0| | 5| 179334|chr6 | 32037453|TNXB | 0.0011397| 0.0251615| 32036179| 32038958| 7.705565| 0| | 5| 379817|chr6 | 32037474|TNXB | 0.0004407| 0.4323696| 32036179| 32038958| 7.705565| 0| | 5| 52129|chr6 | 32037587|TNXB | 0.0009903| 0.1194331| 32036179| 32038958| 7.705565| 0| | 5| 60243|chr6 | 32037602|TNXB | 0.0004996| 0.4616771| 32036179| 32038958| 7.705565| 0| | 5| 272610|chr6 | 32037632|TNXB | 0.0002757| 0.6319254| 32036179| 32038958| 7.705565| 0| | 5| 386420|chr6 | 32037653|TNXB | 0.0010359| 0.0942346| 32036179| 32038958| 7.705565| 0| | 5| 300711|chr6 | 32037735|TNXB | 0.0015226| 0.0277306| 32036179| 32038958| 7.705565| 0| | 5| 428514|chr6 | 32037751|TNXB | 0.0003291| 0.5704414| 32036179| 32038958| 7.705565| 0| | 5| 110400|chr6 | 32037800|TNXB | -0.0004129| 0.7202410| 32036179| 32038958| 7.705565| 0| | 5| 449994|chr6 | 32037847|TNXB | 0.0028153| 0.0000569| 32036179| 32038958| 7.705565| 0| | 5| 76776|chr6 | 32037913|TNXB | 0.0006452| 0.1327238| 32036179| 32038958| 7.705565| 0| | 5| 87741|chr6 | 32037916|TNXB | 0.0014233| 0.0020986| 32036179| 32038958| 7.705565| 0| | 5| 444167|chr6 | 32037978|TNXB | 0.0012293| 0.0209276| 32036179| 32038958| 7.705565| 0| | 5| 5081|chr6 | 32038011|TNXB | -0.0010160| 0.2819558| 32036179| 32038958| 7.705565| 0| | 5| 448813|chr6 | 32038027|TNXB | 0.0017132| 0.0062513| 32036179| 32038958| 7.705565| 0| | 5| 298918|chr6 | 32038045|TNXB | 0.0009735| 0.3071656| 32036179| 32038958| 7.705565| 0| | 5| 416617|chr6 | 32038097|TNXB | 0.0008797| 0.1731802| 32036179| 32038958| 7.705565| 0| | 5| 434939|chr6 | 32038155|TNXB | 0.0018101| 0.0032325| 32036179| 32038958| 7.705565| 0| | 5| 470733|chr6 | 32038177|TNXB | 0.0023991| 0.0048218| 32036179| 32038958| 7.705565| 0| | 5| 337001|chr6 | 32038185|TNXB | 0.0025098| 0.0009209| 32036179| 32038958| 7.705565| 0| | 5| 389831|chr6 | 32038230|TNXB | 0.0016509| 0.0134805| 32036179| 32038958| 7.705565| 0| | 5| 276348|chr6 | 32038462|TNXB | 0.0010248| 0.0718441| 32036179| 32038958| 7.705565| 0| | 5| 374532|chr6 | 32038541|TNXB | 0.0027082| 0.0002163| 32036179| 32038958| 7.705565| 0| | 5| 272153|chr6 | 32038747|TNXB | 0.0028779| 0.0028012| 32036179| 32038958| 7.705565| 0| | 5| 411763|chr6 | 32038881|TNXB | 0.0020330| 0.0027048| 32036179| 32038958| 7.705565| 0| | 5| 254265|chr6 | 32038887|TNXB | 0.0006585| 0.3466269| 32036179| 32038958| 7.705565| 0| | 5| 13100|chr6 | 32038958|TNXB | 0.0026444| 0.0000209| 32036179| 32038958| 7.705565| 0|
Here we generate a simple plot of the largest DMR:
dmrff.plot(
dmrs$chr[max.region], dmrs$start[max.region], dmrs$end[max.region],
stats$estimate, stats$se, stats$chr, stats$pos, ci=T, expand=1)
## Loading required package: ggplot2
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