ramr ======== [](https://github.com/BBCG/ramr/actions) [](https://app.codecov.io/gh/BBCG/ramr/tree/devel) [](https://bioconductor.org/packages/release/bioc/html/ramr.html) # 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](https://bbcg.github.io/ramr/articles/ramr.html). ## 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 ```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](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](https://doi.org/10.18710/ED8HSD) ### *`ramr`* at Bioconductor [release](https://bioconductor.org/packages/release/bioc/html/ramr.html), [development version](https://bioconductor.org/packages/devel/bioc/html/ramr.html) ------- # How to Use Please read package vignettes at [GitHub pages](https://bbcg.github.io/ramr/articles/ramr.html) 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(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: ```r # 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) ```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.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