squaremodelsummary: Create a graphical summary of the result of squaremodel...

In ACE: Absolute Copy Number Estimation from Low-coverage Whole Genome Sequencing

Description

squaremodelsummary creates a graphical summary of a squaremodel by plotting the matrixplot and absolute copy number profiles corresponding with the 7 best fits. The list of plots can be saved to a variable and/or a file.

Usage

squaremodelsummary(template, QDNAseqobjectsample = FALSE, 
  squaremodel, samplename, printplots = TRUE, outputdir, 
  imagetype = 'pdf', trncname = FALSE)

Arguments

template	Object. Either a data frame as created by objectsampletotemplate, or a QDNAseq-object
QDNAseqobjectsample	Integer. Specifies which sample to analyze from the QDNAseqobject. Required when using a QDNAseq-object as template. Default = FALSE
squaremodel	List of objects returned by squaremodel
samplename	Character string. Use this sample name in the title of the matrixplot. If the sample comes from a QDNAseq-object and this argument is omitted, the sample name is taken from the QDNAseq-object
printplots	Logical. Print the plots to file. Default = TRUE
outputdir	Character string. Print the plots to this directory
imagetype	Character string. Plots are printed to file using this graphics device. "pdf" will result in 8 pages with individual plots, while the other devices print a 2x4 mosaic. Default = "pdf"
trncname	Logical. If set to TRUE, trncname truncates the sample name from the first instance of "_" in the name. You can also specify the regular expression here, e.g. trncname = "-.*" truncates the name from the first dash. Default = FALSE

Value

Returns a list with eight plots (ggplot2-objects): the matrixplot and the copy number plots corresponding to the seven best fits. If printplots is TRUE, the plots will be printed to file.

Author(s)

Jos B. Poell

See Also

squaremodel

Examples

## simulated data assuming each chromosome comprises 100 bins
s <- jitter(c(1, 1, 0.8, 1.2, rep(1, 5), 1.4, rep(1, 13)), amount = 0)
n <- c(100, 100, 40, 60, rep(100, 5), 100, rep(100, 13))
bin <- 1:2200
chr <- rep(1:22, each = 100)
start <- rep(0:99*1000000+1, 22)
end <- rep(1:100*1000000, 22)
copynumbers <- jitter(rep(s,n), amount = 0.05)
segments <- rep(s, n)
template <- data.frame(bin = bin, chr = chr, start = start, end = end, 
  copynumbers = copynumbers, segments = segments)
sm <- squaremodel(template, method = 'MAE', penalty = 0.5, 
  penploidy = 0.5)
sms <- squaremodelsummary(template, squaremodel = sm, 
  samplename = "sim", printplots = FALSE)
sms[[1]]
sms[[2]]
## using segmented data from a QDNAseq-object
data("copyNumbersSegmented")
sqm <- squaremodel(copyNumbersSegmented, QDNAseqobjectsample = 2, 
  penalty = 0.5, penploidy = 0.5, 
  ptop = 4.3, pbottom = 1.8, prows = 250)
sqms <- squaremodelsummary(copyNumbersSegmented, 2, 
  squaremodel = sqm, printplots = FALSE)
sqms[[1]]
sqms[[2]] + ggplot2::ggtitle("Top fit for sample2")

ACE documentation built on Nov. 8, 2020, 5:30 p.m.