# Generating realistic data with known truth using the `jointseg` package

#### M. Pierre-Jean, G. Rigaill, P. Neuvial

#### 2017-11-28

This vignette describes how to use the `jointseg` package to partition bivariate DNA copy number signals from SNP array data into segments of constant parent-specific copy number. We demonstrate the use of the `PSSeg` function of this package for applying two different strategies. Both strategies consist in first identifying a list of candidate change points through a fast (greedy) segmentation method, and then to prune this list is using dynamic programming [1]. The segmentation method presented here is Recursive Binary Segmentation (RBS, [2]). We refer to [3] for a more comprehensive performance assessment of this method and other segmentation methods. segmentation, change point model, binary segmentation, dynamic programming, DNA copy number, parent-specific copy number.

Please see Appendix for citing `jointseg`.

HERE

This vignette illustrates how the `jointseg` package may be used to generate a variety of copy-number profiles from the same biological ``truth’’. Such profiles have been used to compare the performance of segmentation methods in [3].

## Citing `jointseg`

    citation("jointseg")

    ##
    ## To cite package 'jointseg' in publications, please use the
    ## following references:
    ##
    ##   Morgane Pierre-Jean, Guillem Rigaill and Pierre Neuvial ().
    ##   jointseg: Joint segmentation of multivariate (copy number)
    ##   signals.R package version 1.0.1.
    ##
    ##   Morgane Pierre-Jean, Guillem Rigaill and Pierre Neuvial.
    ##   Performance evaluation of DNA copy number segmentation methods.
    ##   Briefings in Bioinformatics (2015) 16 (4): 600-615.

## Setup

The parameters are defined as follows:

    n <- 1e4                                 ## signal length
    bkp <- c(2334, 6121)                     ## breakpoint positions
    regions <- c("(1,1)", "(1,2)", "(0,2)")  ## copy number regions

    ylims <- cbind(c(0, 5), c(-0.1, 1.1))
    colG <- rep("#88888855", n)
    hetCol <- "#00000088"

For convenience we define a custom plot function for this vignette:

    plotFUN <- function(dataSet, tumorFraction) {
        regDat <- acnr::loadCnRegionData(dataSet=dataSet, tumorFraction=tumorFraction)
        sim <- getCopyNumberDataByResampling(n, bkp=bkp,
                                             regions=regions, regData=regDat)
        dat <- sim$profile
        wHet <- which(dat$genotype==1/2)
        colGG <- colG
        colGG[wHet] <- hetCol
        plotSeg(dat, sim$bkp, col=colGG)
    }

## Affymetrix data

    ds <- "GSE29172"

    pct <- 1
    plotFUN(ds, pct)

Data set GSE29172 : 1 % tumor cells

    plotFUN(ds, pct)

Data set GSE29172 : 1 % tumor cells (another resampling)

    pct <- 0.7
    plotFUN(ds, pct)

Data set GSE29172 : 0.7 % tumor cells

    pct <- 0.5
    plotFUN(ds, pct)

Data set GSE29172 : 0.5 % tumor cells

## Illumina data

    ds <- "GSE11976"

## Session information

    sessionInfo()

    ## R version 3.3.2 RC (2016-10-26 r71594)
    ## Platform: x86_64-apple-darwin13.4.0 (64-bit)
    ## Running under: OS X El Capitan 10.11.6
    ##
    ## locale:
    ## [1] C/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] jointseg_1.0.1 knitr_1.15.1
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] Rcpp_0.12.10       matrixStats_0.52.2 digest_0.6.12
    ##  [4] rprojroot_1.1      acnr_1.0.0         backports_1.0.5
    ##  [7] magrittr_1.5       evaluate_0.10      highr_0.6
    ## [10] stringi_1.1.2      rmarkdown_1.5      tools_3.3.2
    ## [13] stringr_1.1.0      yaml_2.1.14        htmltools_0.3.5
    ## [16] DNAcopy_1.48.0

## References

[1] Bellman, Richard. 1961. “On the Approximation of Curves by Line Segments Using Dynamic Programming.” Communications of the ACM 4 (6). ACM: 284.

[2] Gey, Servane, et al. 2008. “Using CART to Detect Multiple Change Points in the Mean for Large Sample.” https://hal.archives-ouvertes.fr/hal-00327146.

[3] Pierre-Jean, Morgane, et al. 2015. “Performance Evaluation of DNA Copy Number Segmentation Methods.” Briefings in Bioinformatics, no. 4: 600-615.