ramr

Introduction

ramr is an R package for detection of low-frequency aberrant methylation events (epimutations) in large data sets obtained by methylation profiling using array or high-throughput methylation 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, i.e., epimutations)
• 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

Major improvements

v1.16 [BioC 3.21]

• Major rewrite of getAMR and simulateData functions, which are now much faster (C/C++, OpenMP threads) and more robust (correctly deal with methylation sequencing data that often contains 0 and 1 values)
• Old functions getAMR and simulateData as they were described in the ramr paper are now obsolete, but kept under different names (getAMR.obsolete and simulateData.obsolete, respectively) for consistency
• Cleaner and more robust AMR plotting

---

Installation

install via Bioconductor

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

BiocManager::install("ramr") ```

Install the latest version via install_github

r 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:

``` r library(ramr) data(ramr)

search for AMRs

amrs <- getAMR(data.ranges=ramr.data, compute="beta+binom", compute.estimate="amle", compute.weights="logInvDist", combine.min.cpgs=5, combine.threshold=1e-2, combine.window=1000)

inspect

amrs plotAMR(data.ranges=ramr.data, amr.ranges=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:

``` r

set the seed for reproducibility

set.seed(1)

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(template.ranges=ramr.data, nsamples=99, amr.ranges=amrs.unique, ncores=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)

``` r 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

``` r 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.betasmax.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