Introduction
The conumee package provides tools for performing copy-number variation (CNV) analysis using Illumina 450k methylation arrays. Although the primary purpose of 450k arrays is the detection of genome-wide DNA methylation levels [1], it can additionally be used to extract useful information about copy-number alterations, e.g. in clinical cancer samples. The approach was initially described in Sturm et al., 2012 [2]. Other tools following a similar strategy include ChAMP and CopyNumber450k [3, 4].
CNV analysis is performed using a two-step approach. First, the combined intensity values of both ‘methylated’ and ‘unmethylated’ channel of each CpG are normalized using a set of normal controls (i.e. with a flat genome not showing any copy-number alterations). This step is required for correcting for probe and sample bias (e.g. caused by GC-content, type I/II differences or technical variability). Secondly, neighboring probes are combined in a hybrid approach, resulting in bins of a minimum size and a minimum number of probes. This step is required to reduce remaining technical variability and enable meaningful segmentation results.
The conumee package has been written for seamless integration with frequently-used Bioconductor packages. It is recommended that 450k data is loaded using the minfi package [5]. Segmentation is performed using the circular binary segmentation (CBS) algorithm implemented in the DNAcopy package [6]. Processing time is typically less than a minute per sample.
Finally, the conumee package also provides a set of plotting methods to create publication-grade CNV plots of the whole genome, selected chromosomes or previously defined regions of interest. Writing of text-based output files for visualization in other tools (e.g. the IGV browser) or downstream processing (e.g. using GISTIC) is also supported.
Load data
The recommended input format are Mset objects generated from raw IDAT files using the minfi package. Depending on the analysis workflow, these objects might be already available. A good example data-set for testing the conumee package can be downloaded from TCGA (971.6 MB):
https://tcga-data.nci.nih.gov/tcgafiles/ftp_auth/distro_ftpusers/anonymous/tumor/brca/cgcc/jhu-usc.edu/humanmethylation450/methylation/jhu-usc.edu_BRCA.HumanMethylation450.Level_1.8.8.0.tar.gz
This data-set comprises of 42 primary breast cancer samples, 2 breast cancer cell lines and 16 control samples. Make sure to unpack the archive. For the purpose of this vignette, only two illustrative examples are downloaded using the read.450k.url method.
library("minfi")
library("conumee")
#RGsetTCGA <- read.450k.exp(base = "~/conumee_analysis/jhu-usc.edu_BRCA.HumanMethylation450.Level_1.8.8.0") # adjust path
RGsetTCGA <- read.450k.url() # use default parameters for vignette examples
MsetTCGA <- preprocessIllumina(RGsetTCGA)
MsetTCGA
## MethylSet (storageMode: lockedEnvironment)
## assayData: 485512 features, 2 samples
## element names: Meth, Unmeth
## An object of class 'AnnotatedDataFrame': none
## Annotation
## array: IlluminaHumanMethylation450k
## annotation: ilmn12.hg19
## Preprocessing
## Method: Illumina, bg.correct = TRUE, normalize = controls, reference = 1
## minfi version: 1.14.0
## Manifest version: 0.4.0
Alternatively, you can use the minfiData example data-set, comprising of 3 cancer samples and 3 normal controls.
library("minfiData")
data(MsetEx)
MsetEx
## MethylSet (storageMode: lockedEnvironment)
## assayData: 485512 features, 6 samples
## element names: Meth, Unmeth
## An object of class 'AnnotatedDataFrame'
## sampleNames: 5723646052_R02C02 5723646052_R04C01 ...
## 5723646053_R06C02 (6 total)
## varLabels: Sample_Name Sample_Well ... filenames (13 total)
## varMetadata: labelDescription
## Annotation
## array: IlluminaHumanMethylation450k
## annotation: ilmn12.hg19
## Preprocessing
## Method: Raw (no normalization or bg correction)
## minfi version: 1.7.7
## Manifest version: 0.4.0
The CopyNumber450k package provides a large data-set of 52 control samples which can be used for normalization.
library("CopyNumber450kData")
data(RGcontrolSetEx)
MsetControls <- preprocessIllumina(RGcontrolSetEx)
If raw IDAT files are unavailable, data can also be supplied by importing text-based input files, e.g. as generated by GenomeStudio or downloaded from the GEO repository. More details are given later.
Create annotation object
To begin with the CNV analysis, an annotation object, generated by the CNV.create_anno function, is required. This object holds information which only needs to be generated once, irrespective of the number of samples that are processed.
Arguments bin_minprobes and bin_minsize define the minimum number of probes per bin and the minimum bin size (default values that were optimized for 450k data are 15 and 50000, respectively). Argument exclude_regions defines regions to be excluded (e.g. polymorphic regions, an example is given in data(exclude_regions)). Please see ?CNV.create_anno for more details.
Argument detail_regions defines regions to be examined in detail (e.g. dedicated detail plots or text output, see below). For example, detail regions can contain known oncogenes or tumor suppressor genes. These regions should either be supplied as a path to a BED file or as a GRanges object (an example is given in data(detail_regions)). The start and stop coordinates indicate the regions in which probes are analyzed in detail. The plotting range of detail plots are defined in a second set of start and stop coordinates in the GRanges object values (or the 7th and 8th column in the BED file).
data(exclude_regions)
data(detail_regions)
head(detail_regions, n = 2)
## GRanges object with 2 ranges and 5 metadata columns:
## seqnames ranges strand | name
## <Rle> <IRanges> <Rle> | <character>
## [1] chr11 [69453874, 69469242] + | CCND1
## [2] chr19 [30300902, 30315215] + | CCNE1
## thick score probes_gene probes_promoter
## <IRanges> <integer> <integer> <integer>
## [1] [68961558, 69961557] 70 38 32
## [2] [29808059, 30808058] 16 10 6
## -------
## seqinfo: 11 sequences from an unspecified genome; no seqlengths
anno <- CNV.create_anno(exclude_regions = exclude_regions, detail_regions = detail_regions)
## using genome annotations from UCSC
## getting 450k annotations
## - 470870 probes used
## importing regions to exclude from analysis
## importing regions for detailed analysis
## creating bins
## - 53891 bins created
## merging bins
## - 15820 bins remaining
anno
## CNV annotation object
## created : Thu Apr 16 22:42:18 2015
## @genome : 22 chromosomes
## @gap : 313 regions
## @probes : 470870 probes
## @exclude : 3 regions (overlapping 570 probes)
## @detail : 20 regions (overlapping 768 probes)
## @bins : 15820 bins (min/avg/max size: 50/168.5/5000kb, probes: 15/29.7/478)
Combine intensity values
Intensity values of the ‘methylated’ and ‘unmethylated’ channel are combined using the CNV.load function. Input can be either an Mset object (recommended, see above), or a data.frame or matrix object containing intensities generated by GenomeStudio or downloaded from GEO (imported using e.g. read.table, should work without reformatting for most tables, please refer to ?CNV.load for more details).
data(tcgaBRCA.sentrix2name) # TCGA sample IDs are supplied with the conumee package
sampleNames(MsetTCGA) <- tcgaBRCA.sentrix2name[sampleNames(MsetTCGA)]
tcga.data <- CNV.load(MsetTCGA)
tcga.controls <- grep("-11A-", names(tcga.data))
names(tcga.data)
## [1] "TCGA-AR-A1AU-01A-11D-A12R-05" "TCGA-AR-A1AY-01A-21D-A12R-05"
tcga.data
## CNV data object
## created :
## @intensity : available (2 samples, 485512 probes)
minfi.data <- CNV.load(MsetEx)
minfi.controls <- pData(MsetEx)$status == "normal"
controls.data <- CNV.load(MsetControls)
Output plots and text files
The conumee package supports two types of plots:
- The
CNV.genomeplot method produces plots of the complete genome or of one or multiple chromosomes. Intensity values of each bin are plotted in colored dots. Segments are shown as blue lines. See ?CNV.genomeplot for more details.
- The
CNV.detailplot methods produces plots of individual detail regions, as defined in CNV.create_anno. Intensity values of individual probes are plotted in colored crosses. Bins are shown as blue lines. CNV.detailplot_wrap is a wrapper function that produces a single plot of all detail regions.
Text output is generated using the CNV.write method. Parameter what specifies if “probes”, “bins”, “detail” or “segments” should be returned. If parameter file is specified, the output is written into a file, otherwise a data.frame is returned. See ?CNV.write for more details.
#pdf("~/conumee_analysis/CNVplots.pdf", height = 9, width = 18)
CNV.genomeplot(x)
CNV.genomeplot(y)
CNV.genomeplot(y, chr = "chr6")
CNV.genomeplot(z)
CNV.genomeplot(z, chr = "chr10")
CNV.detailplot(z, name = "PTEN")
CNV.detailplot_wrap(z)
#dev.off()
head(CNV.write(y, what = "segments"), n = 5)
## ID chrom loc.start loc.end num.mark bstat
## 1 TCGA-AR-A1AU-01A-11D-A12R-05 chr1 635684 89150000 728 55.16184
## 2 TCGA-AR-A1AU-01A-11D-A12R-05 chr1 89400000 118475000 161 44.01507
## 3 TCGA-AR-A1AU-01A-11D-A12R-05 chr1 119000000 249195311 704 NA
## 4 TCGA-AR-A1AU-01A-11D-A12R-05 chr10 105000 135462374 840 NA
## 5 TCGA-AR-A1AU-01A-11D-A12R-05 chr11 130000 50516927 326 39.93951
## pval seg.mean seg.median
## 1 0 0.008 0.006
## 2 0 -0.252 -0.248
## 3 NA 0.103 0.112
## 4 NA -0.007 -0.007
## 5 0 0.150 0.143
#CNV.write(y, what = "segments", file = "~/conumee_analysis/TCGA-AR-A1AU.CNVsegments.seg") # adjust path
#CNV.write(y, what = "bins", file = "~/conumee_analysis/TCGA-AR-A1AU.CNVbins.igv")
#CNV.write(y, what = "detail", file = "~/conumee_analysis/TCGA-AR-A1AU.CNVdetail.txt")
#CNV.write(y, what = "probes", file = "~/conumee_analysis/TCGA-AR-A1AU.CNVprobes.igv")
Session info
sessionInfo()
## R version 3.2.0 RC (2015-04-08 r68161)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.9.5 (Mavericks)
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] CopyNumber450kData_1.3.1
## [2] minfiData_0.9.1
## [3] conumee_1.0.0
## [4] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.2.1
## [5] IlluminaHumanMethylation450kmanifest_0.4.0
## [6] minfi_1.14.0
## [7] bumphunter_1.8.0
## [8] locfit_1.5-9.1
## [9] iterators_1.0.7
## [10] foreach_1.4.2
## [11] Biostrings_2.36.0
## [12] XVector_0.8.0
## [13] GenomicRanges_1.20.0
## [14] GenomeInfoDb_1.4.0
## [15] IRanges_2.2.0
## [16] S4Vectors_0.6.0
## [17] lattice_0.20-31
## [18] Biobase_2.28.0
## [19] BiocGenerics_0.14.0
##
## loaded via a namespace (and not attached):
## [1] mclust_5.0.0 base64_1.1
## [3] Rcpp_0.11.5 Rsamtools_1.20.0
## [5] digest_0.6.8 plyr_1.8.1
## [7] futile.options_1.0.0 RSQLite_1.0.0
## [9] evaluate_0.6 zlibbioc_1.14.0
## [11] GenomicFeatures_1.20.0 annotate_1.46.0
## [13] preprocessCore_1.30.0 rmarkdown_0.5.1
## [15] splines_3.2.0 BiocParallel_1.2.0
## [17] stringr_0.6.2 RCurl_1.95-4.5
## [19] biomaRt_2.24.0 rtracklayer_1.28.0
## [21] multtest_2.24.0 pkgmaker_0.22
## [23] htmltools_0.2.6 GEOquery_2.34.0
## [25] DNAcopy_1.42.0 quadprog_1.5-5
## [27] codetools_0.2-11 matrixStats_0.14.0
## [29] XML_3.98-1.1 reshape_0.8.5
## [31] GenomicAlignments_1.4.0 MASS_7.3-40
## [33] bitops_1.0-6 grid_3.2.0
## [35] nlme_3.1-120 xtable_1.7-4
## [37] registry_0.2 DBI_0.3.1
## [39] formatR_1.1 genefilter_1.50.0
## [41] doRNG_1.6 limma_3.24.0
## [43] futile.logger_1.4 nor1mix_1.2-0
## [45] BiocStyle_1.6.0 lambda.r_1.1.7
## [47] RColorBrewer_1.1-2 siggenes_1.42.0
## [49] tools_3.2.0 illuminaio_0.10.0
## [51] rngtools_1.2.4 survival_2.38-1
## [53] yaml_2.1.13 AnnotationDbi_1.30.0
## [55] beanplot_1.2 knitr_1.9
References
1. Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, Delano D, Zhang L, Schroth GP, Gunderson KL, Fan JB, Shen R: High density DNA methylation array with single CpG site resolution. Genomics 2011, 98:288–295.
2. Sturm D, Witt H, Hovestadt V, Khuong-Quang D-A, Jones DT, Konermann C, Pfaff E, Toenjes M, Sill M, Bender S, Kool M, Zapatka M, Becker N, Zucknick M, Hielscher T, Liu XY, Fontebasso AM, Ryzhova M, Albrecht S, Jacob K, Wolter M, Ebinger M, Schuhmann MU, Meter T van, Fruehwald MC, Hauch H, Pekrun A, Radlwimmer B, Niehues T, Komorowski G von, et al.: Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 2012, 22:425–437.
3. Feber A, Guilhamon P, Lechner M, Fenton T, Wilson GA, Thirlwell C, Morris TJ, Flanagan AM, Teschendorff AE, Kelly JD, Beck S: Using high-density DNA methylation arrays to profile copy number alterations. Genome Biology 2014, 15.
4. Papillon-Cavanagh S, Fortin J, De Jay N, Majewski J: CopyNumber450k: an R package for CNV inference using illumina 450k DNA methylation assay. Submitted
5. Aryee MJ, Jaffe AE, Hector C, Christine L, Feinberg AP, Hansen KD, Irizarry RA: Minfi: a flexible and comprehensive bioconductor package for the analysis of infinium DNA methylation microarrays. Bioinformatics 2014, 30:1363–1369.
6. Olshen AB, Venkatraman E, Lucito R, Wigler M: Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 2004, 5:557–572.