# BubbleTree Tutorial

Wei Zhu, Michael Kuziora, Todd Creasy, Brandon Higgs

#### 2020-04-28

# Contents

# 1 Introduction

To evaluate the aneuploidy and prevalence of clonal or quasiclonal tumors, we developed a novel tool to characterize the mosaic tumor genome on the basis of one major assumption: the genome of a heterogeneous multi-cell tumor biopsy can be sliced into a chain of segments that are characterized by homogeneous somatic copy number alternations (SCNAs) and B allele frequencies (BAFs). The model, termed BubbleTree, utilizes both SCNAs and the interconnected BAFs as markers of tumor clones to extract tumor clonality estimates. BubbleTree is an intuitive and powerful approach to jointly identify ASCN, tumor purity and (sub)clonality, which aims to improve upon current methods to characterize the tumor karyotypes and ultimately better inform cancer diagnosis, prognosis and treatment decisions.

# 2 Quickstart to Using BubbleTree

To perform a BubbleTree analysis, data pertaining to the position and B allele frequency of heterozygous snps in the tumor sample and segmented copy number information including the position, number of markers/segment and log2 copy number ratio between tumor and normal samples must first be obtained.

## 2.1 Preparing Data for BubbleTree

BubbleTree was developed using both whole exome sequencing (WES) and whole genome sequening (WGS) NGS data from paired tumor/normal biopsies, but this model should also be applicable to array comparative genomic hybridization (aCGH) and single nucleotide polymorphism (SNP) array data.

There are many methods for generating and processing sequencing data in preparation for BubbleTree analysis. In the following section we provide example workflows starting from WES NGS which can be adapted as needed to alternate inputs.

## 2.2 Preparing Sequence Variation Data

The primary BubbleTree requirement for sequence variant information is a GRanges object containing the B alelle frequencies and genomic positions of variants known to be heterozygous in the paired normal sample.

Mapped reads from tumor and normal tissue can be processed with mutation caller software such as VarScan or MUTECT. In this example, we use a hypothetical vcf file from VarScan output which contains mutation calls from both normal and tumor samples.

### 2.2.1 Preparing BAF Data From VarScan

Assume that you have loaded the data snp.dat like this:

    head(snp.dat)

      CHROM    POS  ID REF ALT QUAL FILTER LT.rna.dp LN.rna.dp ON.rna.dp OT.rna.dp BT.wes.dp LT.wes.dp LN.wes.dp ON.wes.dp
    1  chr1  54757 rs202000650   T   G    .   PASS        NA        NA        NA        NA        NA        NA        NA        NA
    2  chr1 564636           .   C   T    .   PASS        NA        NA        NA        NA        NA        NA        NA        NA
    3  chr1 564862   rs1988726   T   C    .   PASS        NA        NA        NA        NA        NA        NA        NA        NA
    4  chr1 564868   rs1832728   T   C    .   PASS        NA        NA        NA        NA        NA        NA        NA        NA
    5  chr1 565454   rs7349151   T   C    .   PASS        NA        NA        NA        NA        NA        NA        NA        NA
    6  chr1 565464   rs6594030   T   C    .   PASS        NA        NA        NA        NA        NA        NA        NA        NA
      OT.wes.dp LT.wgs.dp LN.wgs.dp ON.wgs.dp OT.wgs.dp LT.rna.freq LN.rna.freq ON.rna.freq OT.rna.freq BT.wes.freq LT.wes.freq
    1        NA        25        24        27        19          NA          NA          NA          NA          NA          NA
    2        NA        21        NA        NA        14          NA          NA          NA          NA          NA          NA
    3        NA        10        15        55        13          NA          NA          NA          NA          NA          NA
    4        NA        10        12        60        14          NA          NA          NA          NA          NA          NA
    5        NA        21        14        26        24          NA          NA          NA          NA          NA          NA
    6        NA        25        16        33        29          NA          NA          NA          NA          NA          NA
      LN.wes.freq ON.wes.freq OT.wes.freq LT.wgs.freq LN.wgs.freq ON.wgs.freq OT.wgs.freq
    1          NA          NA          NA      0.2400      0.1667      0.2222      0.3684
    2          NA          NA          NA      0.0000          NA          NA      0.1429
    3          NA          NA          NA      0.4000      0.5333      0.9091      0.7692
    4          NA          NA          NA      0.5000      0.6667      0.9333      0.7857
    5          NA          NA          NA      0.1429      0.3571      0.6538      0.6250
    6          NA          NA          NA      0.2000      0.3750      0.7273      0.5862

Identify the germline heterozygous loci:

    is.hetero <- function(x, a=0.3, b=0.7) {
     (x - a)  *  (b - x) >= 0
    }

    wgs.snp.ss <- subset(snp.dat, ! CHROM %in% c("chrX", "chrY") &
                             LN.wgs.dp >= 15 &
                             ON.wgs.dp >=15 &
                             is.hetero(LN.wgs.freq, 0.4, 0.6) &
                             is.hetero(ON.wgs.freq, 0.4, 0.6))

Then convert to the GRanges object:

    library(GenomicRanges)
    wgs.hetero.grs <- list()
    wgs.hetero.grs$lung <- with(wgs.snp.ss, GRanges(CHROM, IRanges(POS, POS), mcols=cbind(LT.wgs.dp, LT.wgs.freq)))
    wgs.hetero.grs$ovary <- with(wgs.snp.ss, GRanges(CHROM, IRanges(POS, POS), mcols=cbind(OT.wgs.dp, OT.wgs.freq)))
    names(elementMetadata(wgs.hetero.grs$lung)) <- names(elementMetadata(wgs.hetero.grs$ovary))  <- c("dp", "freq")

The B-allele frequency data is extracted using the Bioconductor package VariantAnnotation and converted from string to numeric format.

### 2.2.2 Preparing CNV Data from DNAcopy

The object seg is the segment call output from DNAcopy and min.num here specifies the minimum segment size to keep

    library(GenomicRanges)
    min.num <- 10
    cnv.gr <- with(subset(seg$output, num.mark >= min.num & ! chrom %in% c("chrX", "chrY")) , GRanges(chrom, IRanges(loc.start, loc.end), mcols=cbind(num.mark, seg.mean)))

Then merge the SNP and CNV GRanges objects.

Example data in the desired format is provided as part of this package as GRanges objects and can be loaded as shown below. To utilize this vignette, you must first load BubbleTree below. You don’t need to use “suppressMessages”.

    suppressMessages(
        library(BubbleTree)
    )

allCall.lst is pre-calculated CNV data. allRBD.lst is simply the RBD data from below.

    load(system.file("data", "allCall.lst.RData", package="BubbleTree"))
    head(allCall.lst[[1]]@rbd)

    ##   seqnames    start       end     width strand seg.id num.mark    lrr
    ## 1    chr10    93890  38769716  38675827      *    806    31699 0.1413
    ## 2    chr10 38877329 135523936  96646608      *    808    74425 0.1415
    ## 3    chr11   133952 134946370 134812419      *    812   102934 0.1412
    ## 4    chr12    60000 133841793 133781794      *    813   103392 0.1413
    ## 5    chr13 19020000 115109861  96089862      *    814    76080 0.1419
    ## 6    chr14 20191636 107288640  87097005      *    823    68709 0.1425
    ##       kurtosis        hds     hds.sd het.cnt seg.size
    ## 1 -0.093044958 0.01851852 0.06051370   10400 1.487216
    ## 2 -0.048701274 0.01851852 0.06046402   25414 3.491784
    ## 3 -0.018840021 0.01851852 0.06052843   36183 4.829335
    ## 4 -0.007149860 0.01851852 0.06096056   36798 4.850823
    ## 5 -0.022536940 0.01851852 0.06057517   27278 3.569431
    ## 6 -0.004935614 0.01851852 0.06166893   25001 3.223607

However, if you wish to create your own RBD object from your input, you would use the code below. There is test data available in this package that is used for demonstration purposes.

    # 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)

    ##   seqnames    start      end    width strand seg.id num.mark     lrr   kurtosis
    ## 1     chr1    65625  2066855  2001231      *      1      548  0.1997  0.1830119
    ## 2     chr1  2075796 38489397 36413602      *      2     5284 -0.4146 -1.9020390
    ## 3     chr1 38511244 39761601  1250358      *      3       72 -0.0511 -2.0000000
    ## 4     chr1 39763396 39982177   218782      *      4      112  0.0401 -0.9912620
    ## 5     chr1 39988109 43905367  3917259      *      5      601  0.0372 -1.4809979
    ## 6     chr1 43905709 44128685   222977      *      6       53  0.2822 -2.0000000
    ##       hds     hds.sd het.cnt   seg.size
    ## 1 0.01220 0.05007095      71 0.24809178
    ## 2 0.35600 0.06296830     501 2.39218420
    ## 3 0.15555 0.03471894       2 0.03259600
    ## 4 0.12060 0.06761821       4 0.05070489
    ## 5 0.14560 0.06186714      47 0.27208605
    ## 6 0.07280 0.05176022       2 0.02399428

    # 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)

    ## Warning in drawBTree(btreeplotter, pred@rbd): More ploidy might be suggested: 1.6, 1.6, 2.2, 1.7, 1.9, 1.5, 1.7, 2.2, 1.9, 2.1, 1.7

    print(btree)

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

    ## Warning in drawBTree(btreeplotter, pred@rbd.adj): More ploidy might be suggested: 1.8, 1.9, 2.1, 2, 2.2, 2.9, 2.3, 2.6, 1.8, 1.9, 2, 2.3, 3, 2.6, 2.9, 2.3

    print(btree)

    # create a combined plot with rbd and rbd.adj that shows the arrows indicating change
    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)

    ## Warning: Ignoring unknown aesthetics: fill

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

    ## Warning in drawBTree(btreeplotter, pred@rbd): More ploidy might be suggested: 1.6, 1.6, 2.2, 1.7, 1.9, 1.5, 1.7, 2.2, 1.9, 2.1, 1.7

    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)

    ## 77 are common, whereas 35 and 302 are unique in each dataset

    sample.name <- "22_cnv_snv"

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

    ## Warning in drawBTree(btreeplotter, pred@rbd.adj): More ploidy might be suggested: 1.8, 1.9, 2.1, 2, 2.2, 2.9, 2.3, 2.6, 1.8, 1.9, 2, 2.3, 3, 2.6, 2.9, 2.3

    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")

    ##
    ## Purity/Ploidy:  Purity: 0.71, 0.26; Ploidy: 3.0; Deviation: 0.02

The remaining datasets used to support the CNV data display on the BubbleTree plots.

    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"))
    load(system.file("data", "cyto.gr.rda", package="BubbleTree"))
    load(system.file("data", "centromere.dat.rda", package="BubbleTree"))
    load(system.file("data", "all.somatic.lst.RData", package="BubbleTree"))
    load(system.file("data", "allHetero.lst.RData", package="BubbleTree"))
    load(system.file("data", "allCNV.lst.RData", package="BubbleTree"))
    load(system.file("data", "hg19.seqinfo.rda", package="BubbleTree"))

    # lists of 379 known cancer genes
    head(cancer.genes.minus2)

    ## [1] "ABL1"   "ACVR1B" "AKR1B1" "AKT1"   "ALK"    "APC"

    # another list of 105 known cancer genes
    head(vol.genes)

    ## [1] "ABL1"  "AKT2"  "ALK"   "APC"   "ATM"   "AXIN2"

    # full gene annotation Grange object
    head(gene.uni.clean.gr)

    ## GRanges object with 6 ranges and 1 metadata column:
    ##           seqnames              ranges strand | gene.symbol
    ##              <Rle>           <IRanges>  <Rle> | <character>
    ##      A1BG    chr19   58858172-58874214      - |        A1BG
    ##      NAT2     chr8   18248755-18258723      + |        NAT2
    ##       ADA    chr20   43248163-43280376      - |         ADA
    ##      CDH2    chr18   25530930-25757445      - |        CDH2
    ##      AKT3     chr1 243651535-244006886      - |        AKT3
    ##   GAGE12F     chrX   49217763-49233491      + |     GAGE12F
    ##   -------
    ##   seqinfo: 24 sequences from hg19 genome

    # cytoband coordinate data
    head(cyto.gr)

    ## GRanges object with 6 ranges and 2 metadata columns:
    ##       seqnames            ranges strand |        name    gieStain
    ##          <Rle>         <IRanges>  <Rle> | <character> <character>
    ##   [1]     chr1         0-2300000      * |      p36.33        gneg
    ##   [2]     chr1   2300000-5400000      * |      p36.32      gpos25
    ##   [3]     chr1   5400000-7200000      * |      p36.31        gneg
    ##   [4]     chr1   7200000-9200000      * |      p36.23      gpos25
    ##   [5]     chr1  9200000-12700000      * |      p36.22        gneg
    ##   [6]     chr1 12700000-16200000      * |      p36.21      gpos50
    ##   -------
    ##   seqinfo: 24 sequences from hg19 genome

    # centromere coordinate data
    head(centromere.dat)

    ##    X.bin chrom chromStart  chromEnd   ix n    size       type bridge
    ## 2     23  chr1  121535434 124535434 1270 N 3000000 centromere     no
    ## 43    20  chr2   92326171  95326171  770 N 3000000 centromere     no
    ## 60     2  chr3   90504854  93504854  784 N 3000000 centromere     no
    ## 67     1  chr4   49660117  52660117  447 N 3000000 centromere     no
    ## 80    14  chr5   46405641  49405641  452 N 3000000 centromere     no
    ## 91    16  chr6   58830166  61830166  628 N 3000000 centromere     no

    # SNV location data
    head(all.somatic.lst, n=1L)

    ## $HCC4.Primary.Tumor
    ## GRanges object with 229 ranges and 1 metadata column:
    ##         seqnames    ranges strand |     score
    ##            <Rle> <IRanges>  <Rle> | <numeric>
    ##     [1]     chr1  20982538      * |    0.0741
    ##     [2]     chr1  23419976      * |    0.5696
    ##     [3]     chr1  40859043      * |    0.3580
    ##     [4]     chr1  41976299      * |    0.3333
    ##     [5]     chr1  47124291      * |    0.2867
    ##     ...      ...       ...    ... .       ...
    ##   [225]    chr20  43964620      * |    0.0508
    ##   [226]    chr20  48503292      * |    0.5333
    ##   [227]    chr21  33043893      * |    0.3987
    ##   [228]    chr21  37618302      * |    0.3883
    ##   [229]    chr22  38087422      * |    0.2037
    ##   -------
    ##   seqinfo: 22 sequences from an unspecified genome; no seqlengths

    # sequence variants
    head(allHetero.lst, n=1L)

    ## $sam2
    ## GRanges object with 592971 ranges and 1 metadata column:
    ##            seqnames    ranges strand |     score
    ##               <Rle> <IRanges>  <Rle> | <numeric>
    ##        [1]     chr2  95633262      * |         0
    ##        [2]     chr2  95818028      * |         0
    ##        [3]     chr2  95937209      * |         1
    ##        [4]     chr2  96742179      * |         0
    ##        [5]     chr2  96922986      * |         1
    ##        ...      ...       ...    ... .       ...
    ##   [592967]    chr22  51171854      * |  0.400000
    ##   [592968]    chr22  51174391      * |  0.517241
    ##   [592969]    chr22  51174533      * |  0.488889
    ##   [592970]    chr22  51178405      * |  0.500000
    ##   [592971]    chr22  51178709      * |  0.360000
    ##   -------
    ##   seqinfo: 21 sequences from an unspecified genome; no seqlengths

    # copy number variation data
    head(allCNV.lst, n=1L)

    ## $sam2
    ## GRanges object with 1305 ranges and 2 metadata columns:
    ##          seqnames              ranges strand |  num.mark     score
    ##             <Rle>           <IRanges>  <Rle> | <numeric> <numeric>
    ##      [1]     chr1         10000-50753      * |        38   -1.2722
    ##      [2]     chr1         51753-79890      * |        22   -2.6144
    ##      [3]     chr1         81231-88087      * |         5   -4.7140
    ##      [4]     chr1        89174-110680      * |        17   -2.4498
    ##      [5]     chr1       111680-139748      * |        24   -1.6389
    ##      ...      ...                 ...    ... .       ...       ...
    ##   [1301]     chr9         10000-37354      * |        29   -0.2345
    ##   [1302]     chr9     40777-138916379      * |     93375    0.1409
    ##   [1303]     chr9 138930478-138946579      * |        11   -0.3820
    ##   [1304]     chr9 138947579-141141038      * |      1775    0.1411
    ##   [1305]     chr9 141142038-141152573      * |        12   -0.1797
    ##   -------
    ##   seqinfo: 25 sequences from an unspecified genome; no seqlengths

    # hg19 sequence data
    hg19.seqinfo

    ## Seqinfo object with 24 sequences from hg19 genome:
    ##   seqnames seqlengths isCircular genome
    ##   chr1      249250621      FALSE   hg19
    ##   chr2      243199373      FALSE   hg19
    ##   chr3      198022430      FALSE   hg19
    ##   chr4      191154276      FALSE   hg19
    ##   chr5      180915260      FALSE   hg19
    ##   ...             ...        ...    ...
    ##   chr20      63025520      FALSE   hg19
    ##   chr21      48129895      FALSE   hg19
    ##   chr22      51304566      FALSE   hg19
    ##   chrX      155270560      FALSE   hg19
    ##   chrY       59373566      FALSE   hg19

# 3 Main Bubbletree Functions

## 3.1 BubbleTree model and diagram

BubbleTree is a model based on three valid assumptions: 1) the paired normal specimen expresses the common diploid state, 2) variant sites are bi-allelic, and 3) genome segments (rather than the whole genome) with homogeneous copy number ratio and BAFs, exist in the profiled tumor genome. The first two assumptions generally hold, whereas the last homogeneity assumption can also be satisfied even in the case of a complex tumor clonal structure.

As the three assumptions are all generally plausible, we therefore developed a model for the BubbleTree diagram. For one homogenous genomic segment (x:y;p), we have,

Expected copy number, (CN)=2×(1-p)+(x+y)×p

Copy Ratio, R=(CN)/2=(1-p)+(x+y)/2×p (1)

B allele frequency, BAF=(y×p+1×(1-p))/((x+y)×p+2×(1-p))

and the homozygous-deviation score (HDS),

HDS= ∣BAF-0.5∣=(p×∣y-x∣)/(2×[(x+y)×p+2×(1-p)]) (2)

Based on equations (1) and (2), we are able to calculate an R score (copy ratio) and HDS for a segment (x:y; p). For example, (0:1; 0.75) will provide 0.625 and 0.3 for the R scores and HDS, respectively.

## 3.2 Description of the BubblePlot Graph

### 3.2.1 The Branches

    btreeplotter <- new("BTreePlotter", max.ploidy=5, max.size=10)
    print(btreeplotter@branches)

The below plot introduces the relationship between HDS and R score (copy number ratio), both used to elucidate the tumor cell prevalence, ploidy state, and clonality for a tumor sample. Generally, the R score indicates the copy number change, ranging from 0 (homozygous deletion) to 3 (hexaploidy) or higher, while the HDS represents LOH, ranging from values of 0 to 0.5 (i.e., LOH with 100% prevalence). Each branch in the diagram starts at the root (1,0), a value of HDS=0 and R score=1. Namely, a diploid heterozygous genotype segment has a copy number ratio, or R score of 1 (tumor DNA copies=2; normal DNA copies=2, so 2/2=1) with no LOH (HDS=0) and is indicated with a genotype of AB, where the A allele is from one parent and the B allele is from the other parent presumably. Then from the root (1,0), the segment prevalence values are provided in increasing increments of 10%, with each branch representing a different ploidy state. As the values increase along the y-axis, the occurrence of LOH increases, such that on the AA/BB branch at HDS=0.5 and R score=1, this indicates a disomy state with LOH and 100% prevalence for the segment.

BubbleTree plots for Primary Liver Tumor

Generally, the branches mark the projected positions of segments at the given integer copy number ploidy states and prevalence. The plot clearly highlights how high prevalence values create distinct separation between branches (i.e., ploidy states), while as prevalence approaches zero, the branches are non-distinguishable. The ploidy states of Φ, AABB, and AAABBB all have HDS scores of 0, which indicate no LOH at increasing or decreasing R scores from a value of 1, and therefore differ most from the copy number neutral heterozygous disomy state (AB) by R score only. These three ploidy states indicate homozygous deletion (Φ) or amplifications (AABB=1 DNA copy number gain each allele, AAABBB=2 DNA copy number gains each allele). Other ploidy states such as ABB (brown), ABBB (blue), ABBBB (green), or ABBBBB (purple) share a piece of the same branch (i.e., the indistinguishable branches), suggesting the existence of multiple likely combinations of prevalence and ploidy states for that region. A tumor clone usually has more than one SCNA, so the abundance of the clone can still be inferred from other distinguishable branches.

### 3.2.2 The Bubbles

    load(system.file("data", "allCall.lst.RData", package="BubbleTree"))
    btreeplotter <- new("BTreePlotter", max.ploidy=5, max.size=10)
    sample <- allCall.lst[["sam10"]]
    rbd1 <- sample@rbd
    rbd2 <- sample@rbd.adj
    arrows <- trackBTree(btreeplotter, rbd1, rbd2, min.srcSize=0.01, min.trtSize=0.01)

    ## Warning: Ignoring unknown aesthetics: fill

    btree <- drawBTree(btreeplotter, rbd1)
    print(btree)

Along with the branches from the prediction of the model, bubbles (i.e., the leaves) are depicted on the basis of the real data, where the size of the bubbles are proportional to the length of the homogenous segments. A bubble (i.e. the homogeneous SCNA segment) represents the HDS and R score as measured from the assay, such as WES or WGS data. The location of the bubble determines the allele copy number(s) and prevalence for the SCNA segment. A close proximity between a bubble and branch indicates an integer copy-number (e.g. 15q11.2-14), whereas any deviation between the bubble and branch (e.g, 7q21.11-21.12) is due to either variation in the measurement or a non-integer copy-number – something that occurs with multiple clones harboring different SCNAs over the same region.)

## 3.3 BubbleTree plot for a WGS sample using adjusted and non-adjusted CNV data.

    load(system.file("data", "allCall.lst.RData", package="BubbleTree"))
    btreeplotter <- new("BTreePlotter", max.ploidy=5, max.size=10)
    sample <- allCall.lst[["sam12"]]
    rbd1 <- sample@rbd
    rbd2 <- sample@rbd.adj
    arrows <- trackBTree(btreeplotter, rbd1, rbd2, min.srcSize=0.01, min.trtSize=0.01)

    ## Warning: Ignoring unknown aesthetics: fill

    btree <- drawBTree(btreeplotter, rbd1) + drawBubbles(btreeplotter, rbd2, "gray80") + arrows
    print(btree)

## 3.4 BubbleTree plot for a WGS sample using only non-adjusted CNV data.

    load(system.file("data", "allCall.lst.RData", package="BubbleTree"))
    btreeplotter <- new("BTreePlotter", max.ploidy=5, max.size=10)
    sample <- allCall.lst[["sam12"]]
    rbd1 <- sample@rbd
    rbd2 <- sample@rbd.adj
    arrows <- trackBTree(btreeplotter, rbd1, rbd2, min.srcSize=0.01, min.trtSize=0.01)

    ## Warning: Ignoring unknown aesthetics: fill

    btree <- drawBTree(btreeplotter, rbd1) + arrows
    print(btree)

## 3.5 View data and generate Excel report

This report only shows samples that have tumors with high ploidy and high purity.

    load(system.file("data", "allRBD.lst.RData", package="BubbleTree"))
    btreepredictor <- new("BTreePredictor")
    btreepredictor@config$cutree.h <- 0.15

    high.ploidy <- rep(TRUE, length(allRBD.lst))
    high.purity <- rep(TRUE, length(allRBD.lst))

    high.ploidy[c("sam6",
                  "ovary.wgs",
                  "ovary.wes",
                  "TCGA-06-0145-01A-01W-0224-08",
                  "TCGA-13-1500-01A-01D-0472-01",
                  "TCGA-AO-A0JJ-01A-11W-A071-09")] <- FALSE

    high.purity[c("sam6", "ovary.wgs", "ovary.wes")] <- FALSE

    nn <- "sam13"
    rbd <- allRBD.lst[[nn]]
    btreepredictor@config$high.ploidy <- high.ploidy[nn]
    btreepredictor@config$high.purity <- high.purity[nn]
    btreepredictor <- loadRBD(btreepredictor, rbd)
    btreepredictor@config$min.segSize <- ifelse(max(btreepredictor@rbd$seg.size, na.rm=TRUE) < 0.4, 0.1, 0.4)
    btreepredictor <- btpredict(btreepredictor)

## 3.6 Tumor Purity

The purity, or prevalence of tumor cells within the tumor, can be determined from the SCNA segments at the highest HDS values, assuming the tumor cells all harbor some proportion of SCNAs or LOH.

    cat(info(btreepredictor), "\n")

    ## Purity: 0.9, 0.45; Ploidy: 1.8; Deviation: 0.01

    names(allCall.lst) <- names(allRBD.lst)
    results <- list()
    for (name in names(allCall.lst)) {
        results[[name]] <- allCall.lst[[name]]@result$dist
    }

Run this code to print out an Excel file of the same report

    xls.filename <- paste("all_calls_report", "xlsx", sep=".")
    saveXLS(results, xls.filename)

## 3.7 BubbleTree plot with an overlay of 77 common cancer genes (black rectangles).

    load(system.file("data", "allCall.lst.RData", 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"))
    load(system.file("data", "cyto.gr.rda", package="BubbleTree"))

    # 77 common cancer genes merged from 2 sets
    comm <- btcompare(vol.genes, cancer.genes.minus2)

    ## 77 are common, whereas 35 and 302 are unique in each dataset

    btreeplotter <- new("BTreePlotter", branch.col="gray50")
    annotator <- new("Annotate")

    nn <- "sam13"
    cc <- allCall.lst[[nn]]
    z <- drawBTree(btreeplotter, cc@rbd.adj) + ggplot2::labs(title=sprintf("%s (%s)", nn, info(cc)))
    out <- cc@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
    v <- z + drawFeatures(btreeplotter, out)
    print(v)

## 3.8 BAF and Heterozygosity Graph

BubbleTree can create a summary visualization that displays the concordance between copy number and max B-allele Frequency for each chromosome as well as compare the BAF and R scores.

    load(system.file("data", "allCall.lst.RData", package="BubbleTree"))
    load(system.file("data", "centromere.dat.rda", package="BubbleTree"))
    load(system.file("data", "all.somatic.lst.RData", package="BubbleTree"))
    load(system.file("data", "allHetero.lst.RData", package="BubbleTree"))
    load(system.file("data", "allCNV.lst.RData", package="BubbleTree"))
    load(system.file("data", "hg19.seqinfo.rda", package="BubbleTree"))

    trackplotter <- new("TrackPlotter")

    nn <- "sam12"
    ymax <- ifelse(nn %in% c("lung.wgs", "lung.wes"), 9, 4.3)

    p1 <- xyTrack(trackplotter,
                  result.dat=allCall.lst[[nn]]@result,
                  gr2=centromere.dat,
                  ymax=ymax) + ggplot2::labs(title=nn)

    p2 <- bafTrack(trackplotter,
                   result.dat=allCall.lst[[nn]]@result,
                   gr2=centromere.dat,
                   somatic.gr=all.somatic.lst[[nn]])

    t1 <- getTracks(p1, p2)

    z1 <- heteroLociTrack(trackplotter,
                          allCall.lst[[nn]]@result,
                          centromere.dat,
                          allHetero.lst[[nn]])

    z2 <- RscoreTrack(trackplotter,
                      allCall.lst[[nn]]@result,
                      centromere.dat,
                      allCNV.lst[[nn]])

    t2 <- getTracks(z1, z2)

    gridExtra::grid.arrange(t1,t2, ncol=1)

## 3.9 Perform a comparison of cancer datasets.

Show the SCNV changes between the recurrent tumor and the primary tumor.

    load(system.file("data", "allCall.lst.RData", package="BubbleTree"))
    btp <- new("BTreePlotter", max.ploidy=5, max.size=10)

    nn1 <- "HCC11.Primary.Tumor"
    nn2 <- "HCC11.Recurrent.Tumor"

    rbd1 <- allCall.lst[[nn1]]@result$dist
    rbd2 <- allCall.lst[[nn2]]@result$dist

    srcSize <- 0.5
    trtSize <- 1
    minOver <- 1e7

    arrows <- trackBTree(btp,
                         rbd1,
                         rbd2,
                         min.srcSize=srcSize,
                         min.trtSize=trtSize,
                         min.overlap=minOver)

    ## Warning: Ignoring unknown aesthetics: fill

    z <- drawBTree(btp, rbd1)

    if(!is.null(arrows)) {
        z <- z + arrows + ggplot2::labs(title=sprintf("%s -> %s", nn1, nn2))
    }
    print(z)

## 3.10 To print out an Excel document of summary of the pre-called CNV data.

    library(BubbleTree)
    load(system.file("data", "allCall.lst.RData", package="BubbleTree"))

    all.summary <- plyr::ldply(allCall.lst, function(.Object) {
        purity <- .Object@result$prev[1]
        adj <- .Object@result$ploidy.adj["adj"]
        ploidy <- (2*adj -2)/purity + 2  # when purity is low the calculation result is not reliable

        with(.Object@result,
             return(c(Purity=round(purity,3),
                      Prevalences=paste(round(prev,3), collapse=", "),
                      "Tumor ploidy"=round(ploidy,1))))
    }) %>% plyr::rename(c(".id"="Sample"))

    xls.filename <- paste("all_summary", "xlsx", sep=".")
    saveXLS(list(Summary=all.summary), xls.filename)

# 4 Citation

Zhu W, Kuziora M, Creasy T, Lai Z, Morehouse C, Guo X, Sebastian Y, Shen D, Huang J, Dry JR, Xue F, Jiang L, Yao Y, Higgs BW (2015). “BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality using next generation sequencing data.” Nucleic Acids Research.