squaremodelsummary {ACE} | R Documentation

## Create a graphical summary of the result of squaremodel fitting

### 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")

[Package ACE version 1.12.0 Index]