makeRBD {BubbleTree} | R Documentation

## makeRBD

### Description

make the RBD object

### Usage

    makeRBD(.Object, ...)

    ## S4 method for signature 'RBD'
    makeRBD(.Object, snp.gr, cnv.gr, unimodal.kurtosis = -0.1)

### Arguments

`.Object` | the object
`...` | other input (not needed)
`snp.gr` | SNP GenomicRanges object
`cnv.gr` | CNV GenomicRanges object
`unimodal.kurtosis` | kurtosis

### Value

RBD object

### Examples

    # load sample files
    load(system.file("data", "cnv.gr.rda", package="BubbleTree"))
    load(system.file("data", "snp.gr.rda", package="BubbleTree"))

    # load annotations
    load(system.file("data", "centromere.dat.rda", package="BubbleTree"))
    load(system.file("data", "cyto.gr.rda", package="BubbleTree"))
    load(system.file("data", "cancer.genes.minus2.rda", package="BubbleTree"))
    load(system.file("data", "vol.genes.rda", package="BubbleTree"))
    load(system.file("data", "gene.uni.clean.gr.rda", package="BubbleTree"))

    # initialize RBD object
    r <- new("RBD", unimodal.kurtosis=-0.1)

    # create new RBD object with GenomicRanges objects for SNPs and CNVs
    rbd <- makeRBD(r, snp.gr, cnv.gr)
    head(rbd)

    # create a new prediction
    btreepredictor <- new("BTreePredictor", rbd=rbd, max.ploidy=6, prev.grid=seq(0.2,1, by=0.01))
    pred <- btpredict(btreepredictor)

    # create rbd plot
    btreeplotter <- new("BTreePlotter", max.ploidy=5, max.size=10)
    btree <- drawBTree(btreeplotter, pred@rbd)
    print(btree)

    # create rbd.adj plot
    btreeplotter <- new("BTreePlotter", branch.col="gray50")
    btree <- drawBTree(btreeplotter, pred@rbd.adj)
    print(btree)

    # create a combined plot with rbd and rbd.adj that shows the arrows indicating change
    # THIS IS VERY MESSY WITH CURRENT DATA from Dong
    btreeplotter <- new("BTreePlotter", max.ploidy=5, max.size=10)
    arrows <- trackBTree(btreeplotter,
                         pred@rbd,
                         pred@rbd.adj,
                         min.srcSize=0.01,
                         min.trtSize=0.01)

    btree <- drawBTree(btreeplotter, pred@rbd) + arrows
    print(btree)

    # create a plot with overlays of significant genes
    btreeplotter <- new("BTreePlotter", branch.col="gray50")
    annotator <- new("Annotate")

    comm <- btcompare(vol.genes, cancer.genes.minus2)

    sample.name <- "22_cnv_snv"

    btree <- drawBTree(btreeplotter, pred@rbd.adj) +
        ggplot2::labs(title=sprintf("%s (%s)", sample.name, info(pred)))

    out <- pred@result$dist  %>%
        filter(seg.size >= 0.1 ) %>%
        arrange(gtools::mixedorder(as.character(seqnames)), start)

    ann <- with(out, {
        annoByGenesAndCyto(annotator,
                           as.character(out$seqnames),
                           as.numeric(out$start),
                           as.numeric(out$end),
                           comm$comm,
                           gene.uni.clean.gr=gene.uni.clean.gr,
                           cyto.gr=cyto.gr)
    })

    out$cyto <- ann$cyto
    out$genes <- ann$ann

    btree <- btree + drawFeatures(btreeplotter, out)
    print(btree)

    # print out purity and ploidy values
    info <- info(pred)
    cat("\nPurity/Ploidy: ", info, "\n")

[Package BubbleTree version 2.18.0 Index]