plotCompiledCNV: Plot compiled CNV segments for one sequence/chromosome

In exomeCopy: Copy number variant detection from exome sequencing read depth

Description

This function takes a GRanges object as produced by compileCopyCountSegments and plots the CNV segments for one sequence/chromosomes across the samples with CNV segments.

The segments in the normal state should be removed as shown below in the example to produce a cleaned GRanges object. See the vignette for a more complete example.

Usage

  plotCompiledCNV(CNV.segments, seq.name, xlim=NULL, col=NULL,
  copy.counts=0:6, normal.state = 2)

Arguments

CNV.segments	A GRanges object as produced by compileCopyCountSegments and with normal state removed.
seq.name	The name of the sequence to plot
xlim	The genomic coordinates for the x axis. If not included, the plotting window will cover the range of the CNVs in CNV.segments
col	The colors to use for the different copy count states
copy.counts	The corresponding copy counts for the colors
normal.state	The copy count of the normal state

Value

Produces a plot.

Examples

  example(compileCopyCountSegments)
  CNV.clean <- CNV.segments[CNV.segments$copy.count != 2]
  chr.start <- start(range(fit@ranges))
  chr.end <- end(range(fit@ranges))
  plotCompiledCNV(CNV.clean, "chr1", xlim=c(chr.start,chr.end))

Example output

Loading required package: IRanges
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: ‘S4Vectors’

The following object is masked from ‘package:base’:

    expand.grid

Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: Rsamtools
Loading required package: Biostrings
Loading required package: XVector

Attaching package: ‘Biostrings’

The following object is masked from ‘package:base’:

    strsplit


cmpCCS>   example(exomeCopy)

exmCpy> ## The following is an example of running exomeCopy on simulated
exmCpy> ## read counts using the model parameters defined above.  For an example
exmCpy> ## using real exome sequencing read counts (with simulated CNV) please
exmCpy> ## see the vignette.
exmCpy> 
exmCpy> ## create GRanges for storing genomic ranges and covariate data
exmCpy> ## (background, background stdev, GC-content)
exmCpy> 
exmCpy> m <- 5000

exmCpy> gr <- GRanges("chr1", IRanges(start=0:(m-1)*100+1,width=100),
exmCpy+               log.bg=rnorm(m), log.bg.var=rnorm(m), gc=runif(m,30,50))

exmCpy> genome(gr) <- "hg19"

exmCpy> ## create read depth distributional parameters mu and phi
exmCpy> gr$gc.sq <- gr$gc^2

exmCpy> X <- cbind(bg=gr$log.bg,gc=gr$gc,gc.sq=gr$gc.sq)

exmCpy> Y <- cbind(bg.sd=gr$log.bg.var)

exmCpy> beta <- c(5,1,.01,-.01)

exmCpy> gamma <- c(-3,.1)

exmCpy> gr$mu <- exp(beta[1] + scale(X) %*% beta[2:4])

exmCpy> gr$phi <- exp(gamma[1] + scale(Y) %*% gamma[2])

exmCpy> ## create observed counts with simulated heterozygous duplication
exmCpy> cnv.nranges <- 200

exmCpy> bounds <- (round(m/2)+1):(round(m/2)+cnv.nranges)

exmCpy> O <- rnbinom(length(gr),mu=gr$mu,size=1/gr$phi)

exmCpy> O[bounds] <- O[bounds] + rbinom(cnv.nranges,prob=0.5,size=O[bounds])

exmCpy> gr$sample1 <- O

exmCpy> ## run exomeCopy() and list segments
exmCpy> fit <- exomeCopy(gr,"sample1",X.names=c("log.bg","gc","gc.sq"))

exmCpy> # an example call with variance fitting.
exmCpy> # see paper: this does not necessarily improve the fit
exmCpy> fit <- exomeCopy(gr,"sample1",X.names=c("log.bg","gc","gc.sq"),
exmCpy+                  Y.names="log.bg",fit.var=TRUE)

exmCpy> ## see man page for copyCountSegments() for summary of
exmCpy> ## the predicted segments of constant copy count, and
exmCpy> ## for plot.ExomeCopy() for plotting fitted objects
exmCpy> 
exmCpy> 
exmCpy> 
exmCpy> 

cmpCCS>   # this function requires a named list of named lists
cmpCCS>   # as constructed in the vignette
cmpCCS>   fit.list <- list(sample1 = list(chr1 = fit))

cmpCCS>   CNV.segments <- compileCopyCountSegments(fit.list)

exomeCopy documentation built on Nov. 8, 2020, 7:45 p.m.