makeRBD {BubbleTree} | R Documentation |
make the RBD object
makeRBD(.Object, ...) ## S4 method for signature 'RBD' makeRBD(.Object, snp.gr, cnv.gr, unimodal.kurtosis = -0.1)
.Object |
the object |
... |
other input (not needed) |
snp.gr |
SNP GenomicRanges object |
cnv.gr |
CNV GenomicRanges object |
unimodal.kurtosis |
kurtosis |
RBD object
# 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")