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: fillbtree <- 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: fillbtree <- 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: fillbtree <- 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$seg.size, na.rm=TRUE) < 0.4, 0.1, 0.4)
btreepredictor <- btpredict(btreepredictor)## Warning in min(dist, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in min(dist, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in min(dist, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in min(dist, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in min(dist, na.rm = TRUE): no non-missing arguments to min; returning
## Inf3.6 Tumor Purity
The purity, or prevalence of tumor cells within the tumor, can be determined from the SCNA segments at the highest HDS values, assuming the tumor cells all harbor some proportion of SCNAs or LOH.
cat(info(btreepredictor), "\n")## Purity: 0.9, 0.45; Ploidy: 1.8; Deviation: 0.01names(allCall.lst) <- names(allRBD.lst)
results <- list()
for (name in names(allCall.lst)) {
results[[name]] <- allCall.lst[[name]]@result$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 datasetbtreeplotter <- 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$dist
srcSize <- 0.5
trtSize <- 1
minOver <- 1e7
arrows <- trackBTree(btp,
rbd1,
rbd2,
min.srcSize=srcSize,
min.trtSize=trtSize,
min.overlap=minOver)## Warning: Ignoring unknown aesthetics: fillz <- drawBTree(btp, rbd1)
if(!is.null(arrows)) {
z <- z + arrows + ggplot2::labs(title=sprintf("%s -> %s", nn1, nn2))
}
print(z)
3.10 To print out an Excel document of summary of the pre-called CNV data.
library(BubbleTree)
load(system.file("data", "allCall.lst.RData", package="BubbleTree"))
all.summary <- plyr::ldply(allCall.lst, function(.Object) {
purity <- .Object@result$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)