sequenza (version 2.1.2)

chromosome.view: A graphical representation of multiple chromosomal features in parallel

Description

A graphical representation of multiple chromosomal features in parallel.

Usage

chromosome.view(baf.windows, ratio.windows, mut.tab = NULL, segments = NULL,
                min.N.baf = 1, min.N.ratio = 10000, main = "", vlines = FALSE,
                legend.inset = c(-20 * strwidth("a", units = "figure"), 0),
                BAF.style = "lines", CNn = 2, cellularity = NULL, ploidy = NULL,
                avg.depth.ratio = NULL, model.lwd = 1, model.lty = "24",
                model.col = 1, x.chr.space = 10)
genome.view(seg.cn, info.type = "AB", ...)

Arguments

baf.windows

matrix containing the windowed B-allele frequency values for one chromosome.

ratio.windows

matrix containing the windowed depth ratio values for one chromosome.

mut.tab

mutation table of one chromosome. If specified, the mutations will be drawn in a top panel. mut.tab must be output from the mutation.table function.

segments

segmentation for one chromosome. If specified, the segmented B-allele frequency and depth ratio values will be shown as red lines.

min.N.baf

minimum number of observations required in a BAF window for plotting.

min.N.ratio

minimum number of observations required in a depth ratio window for plotting.

CNn

copy number of the germline genome.

vlines

logical, if TRUE the plot will include dotted vertical lines corresponding to segment breaks.

cellularity

fraction of tumor cells in the sample.

ploidy

value of the estimated ploidy parameter.

avg.depth.ratio

the average value of the normalized depth ratio.

main

main title of the plot.

legend.inset

the inset argument to pass to the legend function. Defines the distance between the mutation legend and the plot border.

BAF.style

the style to visualize the theoretical thresholds for the B allele frequency panel. Possible values are "lines", "blocks", "none". Default is "none".

model.lwd

width of the theoretical lines, if the segments matrix contains the columns A, B and CNt.

model.lty

line type of the theoretical lines, if the segments matrix contains the columns A, B and CNt.

model.col

color of the theoretical lines, if the segments matrix contains the columns A, B and CNt.

x.chr.space

step in megabase on the positions to visualize on the x-axis.

seg.cn

genome wide segments, with the columns A, B and CNt.

info.type

information to plot in genome.view. Available options are "CNt" to draw the copy numbers and "AB" (default) to draw the two alleles.

...

optional arguments passed to plot.

Details

chromosome.view is a convenient plotting function using plotWindows and some handy par parameters, to display the raw sequencing results along with the model fitting.

See Also

windowValues, find.breaks.

Examples

# NOT RUN {
  
# }
# NOT RUN {
data.file <-  system.file("data", "example.seqz.txt.gz", package = "sequenza")
# read all the chromosomes:
seqz.data  <- read.seqz(data.file)
# Gather genome wide GC-stats from raw file:
gc.stats <- gc.sample.stats(data.file)
gc.vect  <- setNames(gc.stats$raw.mean, gc.stats$gc.values)
# Read only one chromosome:
seqz.data  <- read.seqz(data.file, chr.name = 1)

# Correct the coverage of the loaded chromosome:
seqz.data$adjusted.ratio <- seqz.data$depth.ratio /
                           gc.vect[as.character(seqz.data$GC.percent)]
# Select the heterozygous positions
seqz.hom  <- seqz.data$zygosity.normal == 'hom'
seqz.het  <- seqz.data[!seqz.hom, ]
# Detect breakpoints
breaks <- find.breaks(seqz.het, gamma = 80, kmin = 10, baf.thres = c(0, 0.5))
# use heterozygous and homozygous position to measure segment values
seg.s1 <- segment.breaks(seqz.data, breaks = breaks)

# Binning the values of depth ratio and B allele frequency
seqz.r.win <- windowValues(x = seqz.data$adjusted.ratio,
             positions = seqz.data$position,
             chromosomes = seqz.data$chromosome,
             window = 1e6, overlap = 1,
             weight = seqz.data$depth.normal)

seqz.b.win <- windowValues(x = seqz.het$Bf,
             positions = seqz.het$position,
             chromosomes = seqz.het$chromosome,
             window = 1e6, overlap = 1,
             weight = round(x = seqz.het$good.reads, digits = 0))
# create mutation table:
mut.tab   <- mutation.table(seqz.data, mufreq.treshold = 0.15,
                            min.reads = 40, max.mut.types = 1,
                            min.type.freq = 0.9, segments = seg.s1)
# chromosome view without parametes:
chromosome.view(mut.tab = mut.tab[mut.tab$chromosome == "1",],
                baf.windows = seqz.b.win[[1]],
                ratio.windows = seqz.r.win[[1]], min.N.ratio = 1,
                segments = seg.s1[seg.s1$chromosome == "1",],
                main = "Chromosome 1")

# filter out small ambiguous segments, and conveniently weight the segments by size:
seg.filtered <- seg.s1[(seg.s1$end.pos - seg.s1$start.pos) > 10e6, ]
weights.seg  <- 150 + round((seg.filtered$end.pos -
                             seg.filtered$start.pos) / 1e6, 0)
# get the genome wide mean of the normalized depth ratio:
avg.depth.ratio <- mean(gc.stats$adj[,2])
# run the BAF model fit

CP <- baf.model.fit(Bf = seg.filtered$Bf, depth.ratio = seg.filtered$depth.ratio,
                    weight.ratio = weights.seg,
                    weight.Bf = weights.seg,
                    avg.depth.ratio = avg.depth.ratio,
                    cellularity = seq(0.1,1,0.01),
                    ploidy = seq(0.5,3,0.05))

confint <- get.ci(CP)
ploidy   <- confint$max.ploidy
cellularity <- confint$max.cellularity
#detect copy number alteration on the segments:

cn.alleles <- baf.bayes(Bf = seg.s1$Bf, depth.ratio = seg.s1$depth.ratio,
                        cellularity = cellularity, ploidy = ploidy,
                        avg.depth.ratio = 1)

seg.s1 <- cbind(seg.s1, cn.alleles)

# Chromosome view with estimated paramenters:
chromosome.view(mut.tab = mut.tab[mut.tab$chromosome == "1",],
                baf.windows = seqz.b.win[[1]],
                ratio.windows = seqz.r.win[[1]], min.N.ratio = 1,
                segments = seg.s1[seg.s1$chromosome == "1",],
                main = "Chromosome 1", cellularity = cellularity,
                ploidy = ploidy, avg.depth.ratio = 1,
                BAF.style = "lines")
   
# }