Introduction

ramr is an R package for detection of low-frequency aberrant methylation events in large data sets obtained by methylation profiling using array or high-throughput bisulfite sequencing. In addition, package provides functions to visualize found aberrantly methylated regions (AMRs), to generate sets of all possible regions to be used as reference sets for enrichment analysis, and to generate biologically relevant test data sets for performance evaluation of AMR/DMR search algorithms.

This readme contains condensed info on ramr usage. For more, please check function-specific help pages and vignettes within the R environment or at GitHub pages.

Current Features

Identification of aberrantly methylated regions (AMRs)
- filtering by interquartile range (IQR)
- filtering by fitting non-weighted, weighted, or one-and-zero inflated beta distributions
AMR visualization
Generation of reference sets for third-party analyses (e.g. enrichment)
Generation of test data sets for performance evaluation of algorithms for search of differentially (DMR) or aberrantly (AMR) methylated regions

Installation

install via Bioconductor

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("ramr")

Install the latest version via install_github

library(devtools)
install_github("BBCG/ramr", build_vignettes=FALSE,
  repos=BiocManager::repositories(),
  dependencies=TRUE, type="source")

Citing the `ramr` package

Oleksii Nikolaienko, Per Eystein Lønning, Stian Knappskog, ramr: an R/Bioconductor package for detection of rare aberrantly methylated regions, Bioinformatics, 2021;, btab586, https://doi.org/10.1093/bioinformatics/btab586

The data underlying `ramr` manuscript

Replication Data for: “ramr: an R package for detection of rare aberrantly methylated regions, https://doi.org/10.18710/ED8HSD

`ramr` at Bioconductor

release, development version

How to Use

Please read package vignettes at GitHub pages or within the R environment: vignette("ramr", package="ramr"), or consult the function’s help pages for the extensive information on usage, parameters and output values.

ramr methods operate on objects of the class GRanges. The input object for AMR search must in addition contain metadata columns with sample beta values. A typical input object looks like this:

GRanges object with 383788 ranges and 845 metadata columns:
             seqnames    ranges strand |         GSM1235534         GSM1235535         GSM1235536 ...
                <Rle> <IRanges>  <Rle> |          <numeric>          <numeric>          <numeric> ...
  cg13869341     chr1     15865      * |  0.801634776091808  0.846486905008704   0.86732154737116 ...
  cg24669183     chr1    534242      * |  0.834138820071765  0.861974610731835  0.832557979806823 ...
  cg15560884     chr1    710097      * |  0.711275180750356   0.70461945838556  0.699487225634589 ...
  cg01014490     chr1    714177      * | 0.0769098196182058 0.0569443780518647 0.0623154673389864 ...
  cg17505339     chr1    720865      * |  0.876413362222415  0.885593263385521  0.877944732153869 ...
         ...      ...       ...    ... .                ...                ...                ... ...
  cg05615487    chr22  51176407      * |   0.84904178467798  0.836538383875097   0.81568519870099 ...
  cg22122449    chr22  51176711      * |  0.882444486059592  0.870804215405886  0.859269224277308 ...
  cg08423507    chr22  51177982      * |  0.886406345093286  0.882430879852752  0.887241923657461 ...
  cg19565306    chr22  51222011      * | 0.0719084295670266 0.0845209871264646 0.0689074604483659 ...
  cg09226288    chr22  51225561      * |  0.724145303755024  0.696281176451351  0.711459675603635 ...

This code shows how to do basic analysis with ramr using provided data files:

library(ramr)
data(ramr)

# search for AMRs
amrs <- getAMR(ramr.data, ramr.samples, ramr.method="beta", min.cpgs=5,
               merge.window=1000, qval.cutoff=1e-3)

# inspect
amrs
plotAMR(ramr.data, ramr.samples, amrs[1])

# generate the set of all possible genomic regions using sample data set and
# the same parameters as for AMR search
universe <- getUniverse(ramr.data, min.cpgs=5, merge.window=1000)

# enrichment analysis of AMRs using R library LOLA
library(LOLA)
hg19.coredb <- loadRegionDB(system.file("LOLACore", "hg19", package="LOLA"))
core.hits   <- runLOLA(amrs, universe, hg19.coredb, cores=1, redefineUserSets=TRUE)

The following code generates random AMRs and methylation beta values using provided data set as a template:

# unique random AMRs
amrs.unique <- simulateAMR(ramr.data, nsamples=10, regions.per.sample=2,
                           min.cpgs=5, merge.window=1000, dbeta=0.2)

# methylation data with AMRs
data.with.amrs <- simulateData(ramr.data, nsamples=10,
                               amr.ranges=amrs.unique, cores=2)
  
# that's how regions look like
library(gridExtra)
do.call("grid.arrange", c(plotAMR(data.with.amrs, amr.ranges=amrs.unique[1:2]), ncol=2))

The input (or template) object may be obtained using data from various sources. Here we provide two examples:

Using data from NCBI GEO

The following code pulls (NB: very large) raw files from NCBI GEO database, performs normalization and creates GRanges object for further analysis using ramr (system requirements: 22GB of disk space, 64GB of RAM)

library(minfi)
library(GEOquery)
library(GenomicRanges)
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)

# destination for temporary files
dest.dir <- tempdir()

# downloading and unpacking raw IDAT files
suppl.files <- getGEOSuppFiles("GSE51032", baseDir=dest.dir, makeDirectory=FALSE, filter_regex="RAW")
# The default timeout for downloading files in R 4.1 is 60 seconds.
# If code above fails because of that, change your timeout using 
# options(timeout=600)
untar(rownames(suppl.files), exdir=dest.dir, verbose=TRUE)
idat.files  <- list.files(dest.dir, pattern="idat.gz$", full.names=TRUE)
sapply(idat.files, gunzip, overwrite=TRUE)

# reading IDAT files
geo.idat <- read.metharray.exp(dest.dir)
colnames(geo.idat) <- gsub("(GSM\\d+).*", "\\1", colnames(geo.idat))

# processing raw data
genomic.ratio.set <- preprocessQuantile(geo.idat, mergeManifest=TRUE, fixOutliers=TRUE)

# creating the GRanges object with beta values
data.ranges <- granges(genomic.ratio.set)
data.betas  <- getBeta(genomic.ratio.set)
sample.ids  <- colnames(geo.idat)
mcols(data.ranges) <- data.betas

# data.ranges and sample.ids objects are now ready for AMR search using ramr

Using Bismark cytosine report files

library(methylKit)
library(GenomicRanges)

# file.list is a user-defined character vector with full file names of Bismark cytosine report files
file.list

# sample.ids is a user-defined character vector holding sample names
sample.ids

# methylation context string, defines if the reads covering both strands will be merged
context <- "CpG"

# fitting beta distribution (filtering using ramr.method "beta" or "wbeta") requires
# that most of the beta values are not equal to 0 or 1
min.beta <- 0.001
max.beta <- 0.999

# reading and uniting methylation values
meth.data.raw <- methRead(as.list(file.list), as.list(sample.ids), assembly="hg19", header=TRUE,
                          context=context, resolution="base", treatment=rep(0,length(sample.ids)),
                          pipeline="bismarkCytosineReport")
meth.data.utd <- unite(meth.data.raw, destrand=isTRUE(context=="CpG"))

# creating the GRanges object with beta values
data.ranges <- GRanges(meth.data.utd)
data.betas  <- percMethylation(meth.data.utd)/100
data.betas[data.betas<min.beta] <- min.beta
data.betas[data.betas>max.beta] <- max.beta
mcols(data.ranges) <- data.betas

# data.ranges and sample.ids objects are now ready for AMR search using ramr

License

Artistic License/GPL

ramr