# CopyNumberPlots: create copy-number specific plots using karyoploteR

# Introduction

Data visualisation is a powerful tool used for data analysis and exploration in many fields. Genomics data analysis is one of these fields where good visualisation tools can be of great help. The aim of CopyNumberPlots is to offer the user an easy way to create copy-number related plots using the infrastructure provided by the R package karyoploteR.

In addition to a set of specialized plotting functions for copy-number analysis data and results (`plotBAF`, `plotCopyNumberCalls`, …), CopyNumberPlots contains a number of data loading functions to help parsing and loading the results of widely used copy-number calling software such as DNAcopy, DECoN or CNVkit.

Finally, since CopyNumberPlots extends the functionality of karyoploteR, it is possible to combine the plotting functions of both packages to get the perfect figure for your data.

# Installation

CopyNumberPlots is a Bioconductor package and to install it we have to use BiocManager.

      if (!requireNamespace("BiocManager", quietly = TRUE))
          install.packages("BiocManager")
      BiocManager::install("CopyNumberPlots")

We can also install the package from github to get the latest devel version, but beware that it might be incompatible with the release version of Bioconductor!

      BiocManager::install("bernatgel/CopyNumberPlots")

# Quick Start

To start working with CopyNumberPlots we will need to use the `plotKaryoptype` function from karyoploteR. If you want more information on how to customize it, use for other organisms or genome version, etc… you can take a look at the karyoploteR tutorial and specifically at the section on how to plot ideograms.

For this quick start example we’ll plot SNP-array data simulating a cancer genome. The data is in a file included with the package. You can use almost any table-like file format, including the Final Report file you would get from Illumina’s Genome Studio. In this case, to keep the example small, we have data only for chomosome 1.

To load the data we’ll use `loadSNPData` which will detect the right columns, read the data and build a GRanges object for us.

If data uses Ensembl-style chromosome names (1,2,3,…,X,Y) instead of default karyoploteR UCSC chromosome names (chr1,chr2,chr3,…,chrX,chrY) we could change the chromosome style to UCSC with the function `UCSCStyle`.

      library(CopyNumberPlots)

    ## Loading required package: karyoploteR

    ## Loading required package: regioneR

    ## Loading required package: GenomicRanges

    ## Loading required package: stats4

    ## Loading required package: BiocGenerics

    ## Loading required package: generics

    ##
    ## Attaching package: 'generics'

    ## The following objects are masked from 'package:base':
    ##
    ##     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    ##     setequal, union

    ##
    ## Attaching package: 'BiocGenerics'

    ## The following objects are masked from 'package:stats':
    ##
    ##     IQR, mad, sd, var, xtabs

    ## The following objects are masked from 'package:base':
    ##
    ##     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
    ##     as.data.frame, basename, cbind, colnames, dirname, do.call,
    ##     duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
    ##     mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    ##     rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
    ##     unsplit, which.max, which.min

    ## Loading required package: S4Vectors

    ##
    ## Attaching package: 'S4Vectors'

    ## The following object is masked from 'package:utils':
    ##
    ##     findMatches

    ## The following objects are masked from 'package:base':
    ##
    ##     I, expand.grid, unname

    ## Loading required package: IRanges

    ## Loading required package: Seqinfo

      s1.file <- system.file("extdata", "S1.rawdata.txt", package = "CopyNumberPlots", mustWork = TRUE)
      s1 <- loadSNPData(s1.file)

    ## Reading data from /tmp/RtmpuliMp4/Rinst19127597d473/CopyNumberPlots/extdata/S1.rawdata.txt

    ## The column identified as Chromosome is: chr

    ## The column identified as Start is: start

    ## The column identified as End is: end

    ## The column identified as B-Allele Frequency is: baf

    ## The column identified as Log Ratio is: lrr

      s1

    ## GRanges object with 965 ranges and 2 metadata columns:
    ##       seqnames    ranges strand |       lrr       baf
    ##          <Rle> <IRanges>  <Rle> | <numeric> <numeric>
    ##   253     chr1    480818      * | -0.949246         1
    ##   678     chr1    595283      * | -0.882367         0
    ##   643     chr1    632319      * | -0.769292         1
    ##    41     chr1   1036550      * | -1.128100         1
    ##    88     chr1   1115414      * | -0.842099         0
    ##   ...      ...       ...    ... .       ...       ...
    ##   575     chr1 248120086      * |  0.714653  0.751899
    ##   510     chr1 248245181      * |  0.446138  0.312570
    ##   654     chr1 248488745      * |  0.794984  0.000000
    ##   171     chr1 248630472      * |  0.758302  1.000000
    ##   938     chr1 248704671      * |  0.994605  0.227549
    ##   -------
    ##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Once we have our data loaded we can start plotting. We’ll start by creating a karyoplot using `plotKaryotype`. If we were plotting more than one chromosome, we could use `plot.type=4` to get all chromosomes in a single line one next to the other. You can get more information on the available plot types at the karyoploteR tutorial.

      kp <- plotKaryotype(chromosomes="chr1")

And once we have a karyoplot we can start adding out data. We can plot the B-allele frequency using `plotBAF`

      kp <- plotKaryotype(chromosomes="chr1")
      plotBAF(kp, s1)

We can plot LRR using `plotLRR`

      kp <- plotKaryotype(chromosomes="chr1")
      plotLRR(kp, s1)

And we can see in this plot that points with a LRR below -4 (and above 2) are plotted in red at -4 (and at 2) so we don’t lose them.

We can also use the data positioning parameters `r0` and `r1` to add more than one data type on the same plot.

      kp <- plotKaryotype(chromosomes="chr1")
      plotBAF(kp, s1, r0=0.55, r1=1)
      plotLRR(kp, s1, r0=0, r1=0.45)

Finally, we can load a copy number calling made on this data and plot it. To load the copy number calls in this file we can use the function `loadCopyNumberCalls` that will read the data, identify the correct columns and create a GRanges object for us.

      s1.calls.file <- system.file("extdata", "S1.segments.txt", package = "CopyNumberPlots", mustWork = TRUE)
      s1.calls <- loadCopyNumberCalls(s1.calls.file)

    ## Reading data from /tmp/RtmpuliMp4/Rinst19127597d473/CopyNumberPlots/extdata/S1.segments.txt

    ## The column identified as Copy Number is: cn

    ## The column identified as LOH is: loh

      s1.calls

    ## GRanges object with 13 ranges and 2 metadata columns:
    ##      seqnames              ranges strand |        cn       loh
    ##         <Rle>           <IRanges>  <Rle> | <integer> <integer>
    ##    1     chr1          1-60000000      * |         1         1
    ##    2     chr1   60000001-60000999      * |         2         0
    ##    3     chr1   60001000-62990000      * |         0         1
    ##    4     chr1   62990001-62999999      * |         2         0
    ##    5     chr1  63000000-121500000      * |         1         1
    ##   ..      ...                 ...    ... .       ...       ...
    ##    9     chr1 189600352-220352872      * |         3         0
    ##   10     chr1 220352873-220352971      * |         2         0
    ##   11     chr1 220352972-234920000      * |         5         0
    ##   12     chr1 234920001-234999999      * |         2         0
    ##   13     chr1 235000000-249250621      * |         3         0
    ##   -------
    ##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

And then use `plotCopyNumberCalls` to add them to the previous plot.

      kp <- plotKaryotype(chromosomes="chr1")
      plotBAF(kp, s1, r0=0.6, r1=1)
      plotLRR(kp, s1, r0=0.15, r1=0.55)

      #plotCopyNumberCalls(kp, s1.calls, r0=0, r1=0.10)

With that the main functionality of CopyNumberPlots is covered. It is important to take into account that since we are extending the functionality of karyoploteR, we can use all karyoploteR functions to add more data and other data types into these plots.

In the following pages you will find more information on how to load data to use with CopyNumberPlots, how to create other plot types and how to customize them.

# Loading Copy-Number Data

The plotting functions in CopyNumberPlots expect data to be in a `GRanges` with a few columns with specific names:

- baf for B-allele frequency data (or similar) (real value between 0 and 1)
- lrr for log-ratio intensity data (or similar) (real value)
- cn for integer copy-number data (integer value)
- loh for loss of heterozygosity (logical value)
- segment.value for values representing the copy-number called segment but not necessarily the integer copy-number calls (real value)

You can create these structures yourself, but CopyNumberPlots has functions to help in loading both raw data (mainly SNP-array and aCGH data) and copy-number calls.

## Load Raw Data

The main function to load raw data is `loadSNPData`. It will take either a file or an R object (`data.frame` or similar) and will load it, detect the columns with the needed information (chromosome, position, log-ratio, B-allele frequency) based on the column names and build a `GRanges` object ready to use by the plotting functions.

      raw.data.file <- system.file("extdata", "snp.data_test.csv", package = "CopyNumberPlots", mustWork=TRUE)
      snps <- loadSNPData(raw.data.file)

    ## Reading data from /tmp/RtmpuliMp4/Rinst19127597d473/CopyNumberPlots/extdata/snp.data_test.csv

    ## The column identified as Chromosome is: Chr

    ## The column identified as Position is: Position

    ## The column identified as B-Allele Frequency is: B.Allele.Freq

    ## The column identified as Log Ratio is: Log.R.Ratio

    ## The column identified as Identifier is: SNP.Name

      snps

    ## GRanges object with 6 ranges and 11 metadata columns:
    ##     seqnames    ranges strand |   Sample.ID          id SNP.Index         SNP
    ##        <Rle> <IRanges>  <Rle> | <character> <character> <integer> <character>
    ##   1        X  68757767      * |        S001   rs7060463         1       [A/G]
    ##   2        9  86682315      * |        S001   rs1898321         2       [T/C]
    ##   3       11  92711948      * |        S001 kgp12808645         3       [A/G]
    ##   4       12  55233823      * |        S001   rs7299872         4       [A/G]
    ##   5        2 147722211      * |        S001   rs2176056         5       [A/G]
    ##   6       19  32605173      * |        S001  rs17597441         6       [T/C]
    ##     Plus.Minus.Strand Allele1...Plus Allele2...Plus  GC.Score  GT.Score
    ##           <character>    <character>    <character> <numeric> <numeric>
    ##   1                 -              C              C    0.9244    0.8872
    ##   2                 +              T              C    0.9643    0.9367
    ##   3                 -              T              T    0.8770    0.8885
    ##   4                 +              A              G    0.8852    0.8508
    ##   5                 +              G              G    0.9499    0.9167
    ##   6                 -              G              G    0.8025    0.8332
    ##           baf       lrr
    ##     <numeric> <numeric>
    ##   1    1.0000   -0.3530
    ##   2    0.5004    0.0740
    ##   3    0.0054   -0.0537
    ##   4    0.5088   -0.2337
    ##   5    1.0000    0.0886
    ##   6    0.9986    0.0779
    ##   -------
    ##   seqinfo: 6 sequences from an unspecified genome; no seqlengths

When run, the function will tell us the columns it identified and will proceed load the data. To identify the columns it will internally use a set of regular expressions that work in most cases including on the ‘Final Report’ files created by Illumina’s Genome Studio. If for any reason the automatic identification of the columns failed, it is possible to specify the exact column names using the appropiate parameters (`chr.col`, `start.col`, `end.col`…).

## Load Copy-Number Calls

Another set of functions included in the package are functions to load the results of copy-number calling algorithms, the copy number calls per se. In this case we also have a generic function, `loadCopyNumberCalls`, and a few functions specialized in specific copy-number calling packages.

Again, the generic function can work with a file or an R object with a table-like structure and will try to discover the right columns itself. It will return a GRanges with the copy-number called segments and the optional columns `cn` for integer copy-number values, `loh` for loss-of-heterozigosity regions and `segment.value` for values computed for the segments (for example, mean value of the probes in the segment).

As an example we will generate a “random” calling

      cn.data <- toGRanges(c("chr14:66459785-86459774", "chr17:68663111-88866308",
                             "chr10:43426998-83426994", "chr3:88892741-120892733",
                             "chr2:12464318-52464316", "chrX:7665575-27665562"))

      cn.data$CopyNumberInteger <- sample(c(0,1,3,4), size = 6, replace = TRUE)
      cn.data$LossHetero <- cn.data$CopyNumberInteger<2

      cn.data

    ## GRanges object with 6 ranges and 2 metadata columns:
    ##     seqnames             ranges strand | CopyNumberInteger LossHetero
    ##        <Rle>          <IRanges>  <Rle> |         <numeric>  <logical>
    ##   1    chr14  66459785-86459774      * |                 3      FALSE
    ##   2    chr17  68663111-88866308      * |                 1       TRUE
    ##   3    chr10  43426998-83426994      * |                 4      FALSE
    ##   4     chr3 88892741-120892733      * |                 1       TRUE
    ##   5     chr2  12464318-52464316      * |                 1       TRUE
    ##   6     chrX   7665575-27665562      * |                 4      FALSE
    ##   -------
    ##   seqinfo: 6 sequences from an unspecified genome; no seqlengths

and load it

      cn.calls <- loadCopyNumberCalls(cn.data)

    ## The column identified as Copy Number is: CopyNumberInteger

    ## The column identified as LOH is: LossHetero

      cn.calls

    ## GRanges object with 6 ranges and 2 metadata columns:
    ##     seqnames             ranges strand |        cn       loh
    ##        <Rle>          <IRanges>  <Rle> | <numeric> <logical>
    ##   1    chr14  66459785-86459774      * |         3     FALSE
    ##   2    chr17  68663111-88866308      * |         1      TRUE
    ##   3    chr10  43426998-83426994      * |         4     FALSE
    ##   4     chr3 88892741-120892733      * |         1      TRUE
    ##   5     chr2  12464318-52464316      * |         1      TRUE
    ##   6     chrX   7665575-27665562      * |         4     FALSE
    ##   -------
    ##   seqinfo: 6 sequences from an unspecified genome; no seqlengths

we can see how the columns for cn and loh were correctly identified.

To plot this objet we can call, for example `plotCopyNumberCalls`.

      kp <- plotKaryotype(plot.type = 1)

      #plotCopyNumberCalls(kp, cn.calls = cn.calls)

There are other specialized functions that will load either the R object produced by copy-number calling R packages or the files produced by either R or external copy-number calling software.

Currently there are specilized functions to load the data produced by:

- ASCAT
- DECoN
- DNAcopy
- pennCNV
- cnmops
- panel.cnmops
- CNVkit

# Plotting Copy-Number Data

Once we have data loaded (or directly created by us) we can plot it.

There are two functions to plot raw data (`plotBAF` and `plotLRR`) and three functions to plot the copy-number calls (`plotCopyNumberCalls`, `plotCopyNumberCallsAsLines` and `plotCopyNumberSummary`).

## Plotting Raw Data

To demonstrate the raw-data plotting functions we’ll use two example files included with the package

      s1.file <- system.file("extdata", "S1.rawdata.txt", package = "CopyNumberPlots", mustWork = TRUE)
      s1 <- loadSNPData(s1.file)

    ## Reading data from /tmp/RtmpuliMp4/Rinst19127597d473/CopyNumberPlots/extdata/S1.rawdata.txt

    ## The column identified as Chromosome is: chr

    ## The column identified as Start is: start

    ## The column identified as End is: end

    ## The column identified as B-Allele Frequency is: baf

    ## The column identified as Log Ratio is: lrr

      head(s1)

    ## GRanges object with 6 ranges and 2 metadata columns:
    ##       seqnames    ranges strand |       lrr       baf
    ##          <Rle> <IRanges>  <Rle> | <numeric> <numeric>
    ##   253     chr1    480818      * | -0.949246 1.0000000
    ##   678     chr1    595283      * | -0.882367 0.0000000
    ##   643     chr1    632319      * | -0.769292 1.0000000
    ##    41     chr1   1036550      * | -1.128100 1.0000000
    ##    88     chr1   1115414      * | -0.842099 0.0000000
    ##   116     chr1   1559575      * | -1.346852 0.0141703
    ##   -------
    ##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

      s2.file <- system.file("extdata", "S2.rawdata.txt", package = "CopyNumberPlots", mustWork = TRUE)
      s2 <- loadSNPData(s2.file)

    ## Reading data from /tmp/RtmpuliMp4/Rinst19127597d473/CopyNumberPlots/extdata/S2.rawdata.txt

    ## The column identified as Chromosome is: chr

    ## The column identified as Start is: start

    ## The column identified as End is: end

    ## The column identified as B-Allele Frequency is: baf

    ## The column identified as Log Ratio is: lrr

      head(s2)

    ## GRanges object with 6 ranges and 2 metadata columns:
    ##       seqnames    ranges strand |        lrr       baf
    ##          <Rle> <IRanges>  <Rle> |  <numeric> <numeric>
    ##   458     chr1    326751      * |  0.1076864 0.0000000
    ##   382     chr1    466084      * |  0.0898970 0.0562677
    ##   177     chr1    523654      * | -0.1805354 0.4927062
    ##   282     chr1    785305      * |  0.0373102 0.4035268
    ##   799     chr1    787101      * |  0.1487428 1.0000000
    ##   315     chr1    899495      * | -0.1578661 1.0000000
    ##   -------
    ##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

## plotBAF

To plot the B-Allele frequency (BAF) we’ll use `plotBAF`. We’ll start creating a karyoplot using karyoploteR’s plotKaryotype and then add the BAF values into it.

      kp <- plotKaryotype(chromosomes="chr1")
      plotBAF(kp, snps=s1)

We can change a number of parameters to alter the appearance of the plot. We can activate and deactivate the axis and label, we can change the color, size and glyph (shape) of the points, we can use r0 and r1 alter the vertical position of the data and in general we can use any of the standard base R plotting parameters.

      kp <- plotKaryotype(chromosomes="chr1")
      plotBAF(kp, snps=s1, r0=0, r1=0.2, labels = "BAF1", points.col = "orange",
              points.cex = 2, points.pch = 4, axis.cex = 0.3)
      plotBAF(kp, snps=s1, r0=0.3, r1=0.5, labels = "BAF2", points.col = "red",
              points.cex = 0.5, points.pch = 8, axis.cex = 0.7)
      plotBAF(kp, snps=s1, r0=0.6, r1=1, labels = "BAF3",
              points.col = "#FF552222", points.cex = 1.8, points.pch = 16,
              axis.cex = 0.7)

If we want to plot more than one sample, if we have the data in a list of GRanges or in a GRanges list, `plotBAF` will take care of it and plot the different samples one below the other. It will also use the names of the list as labels to identify the different samples.

      samples <- list("Sample1"=s1, "Sample2"=s2)
      kp <- plotKaryotype(chromosomes="chr1")
      plotBAF(kp, snps=samples)

## Plot LRR

The function `plotLRR` is equivalent to the `plotBAF` function but will plot the data in the “lrr” column.

      kp <- plotKaryotype(chromosomes="chr1")
      kpAddBaseNumbers(kp)
      plotLRR(kp, snps=s1)

`plotLRR` has a few specific parameters. Since the range of the data points is not limited to [0,1] as in BAF, you can define the `ymin` and `ymax` values and any point falling out of the [ymin, ymax] range will be plotted in red within this range.

This can help us identify out-of-range data, such as the deletion arround 50Mb in the plot above or the gained region at ~220Mb.

Changing the values of `ymin` and `ymax` we can see a bit different picture

      kp <- plotKaryotype(chromosomes="chr1")
      kpAddBaseNumbers(kp)
      plotLRR(kp, snps=s1,  ymin=-1.5, ymax=1.5)

In this case we see many more points out-of-range. We can change the appearance of this points, changing their color, for example, of we can change how they are represented, using a density plot instead of raw points.

      kp <- plotKaryotype(chromosomes="chr1")
      kpAddBaseNumbers(kp)
      plotLRR(kp, snps=s1,  ymin=-1.5, ymax=1.5, out.of.range = "density")

In this case, due to the very few points in the example, the default parameters for the density plot are not optimal. We can increase the window size to compute the density using larger windows. For example, we can set the window to 1 megabase.

      kp <- plotKaryotype(chromosomes="chr1")
      plotLRR(kp, snps=s1,  ymin=-1.5, ymax=1.5, out.of.range = "density", density.window = 1e6)

And we can see the peaks corresponding to the accumulation of out-of-range points.

Finally, we can control the presence and color of the horizontal line marking the 0 with the “line.at.0.*” parameters.

We can also use the standard customization options with `plotLRR`.

      kp <- plotKaryotype(chromosomes="chr1")
      plotLRR(kp, snps=s1, r0=0, r1=0.2, labels = "LRR1", points.col = "orange",
              points.cex = 2, points.pch = 4, axis.cex = 0.3)
      plotLRR(kp, snps=s1, r0=0.3, r1=0.5, labels = "LRR2", points.col = "red",
              points.cex = 0.5, points.pch = 8, axis.cex = 0.7, ymin=-1.5, ymax=1.5,
              out.of.range.col = "gold", out.of.range = "density",
              density.window = 10e6, density.height = 0.3)
      plotLRR(kp, snps=s1, r0=0.6, r1=1, labels = "LRR3",
              points.col = "#FF552222", points.cex = 1.8, points.pch = 16,
              axis.cex = 0.7)

## Plot Copy-Number Calls

The final data type we can plot with CopyNumberPlots are copy number calls, that is, the results from copy-number calling algorithms. To plot that we need a GRanges object with a at least one column of: * “cn” for integer copy number calls * “segment.value” for non-integer segment regional values * “loh” a logical for loss-of-heterozygosity

As an example we’ll use the data generated by ASCAT in a cancer cell line.

      s1.calls.file <- system.file("extdata", "S1.segments.txt", package = "CopyNumberPlots", mustWork = TRUE)
      s1.calls <- loadCopyNumberCalls(s1.calls.file)

    ## Reading data from /tmp/RtmpuliMp4/Rinst19127597d473/CopyNumberPlots/extdata/S1.segments.txt

    ## The column identified as Copy Number is: cn

    ## The column identified as LOH is: loh

      s2.calls <- loadCopyNumberCalls(system.file("extdata", "S2.segments.txt", package = "CopyNumberPlots", mustWork = TRUE))

    ## Reading data from /tmp/RtmpuliMp4/Rinst19127597d473/CopyNumberPlots/extdata/S2.segments.txt

    ## The column identified as Copy Number is: cn

    ## The column identified as LOH is: loh

      s3.calls <- loadCopyNumberCalls(system.file("extdata", "S3.segments.txt", package = "CopyNumberPlots", mustWork = TRUE))

    ## Reading data from /tmp/RtmpuliMp4/Rinst19127597d473/CopyNumberPlots/extdata/S3.segments.txt

    ## The column identified as Copy Number is: cn

    ## The column identified as LOH is: loh

      s1.calls

    ## GRanges object with 13 ranges and 2 metadata columns:
    ##      seqnames              ranges strand |        cn       loh
    ##         <Rle>           <IRanges>  <Rle> | <integer> <integer>
    ##    1     chr1          1-60000000      * |         1         1
    ##    2     chr1   60000001-60000999      * |         2         0
    ##    3     chr1   60001000-62990000      * |         0         1
    ##    4     chr1   62990001-62999999      * |         2         0
    ##    5     chr1  63000000-121500000      * |         1         1
    ##   ..      ...                 ...    ... .       ...       ...
    ##    9     chr1 189600352-220352872      * |         3         0
    ##   10     chr1 220352873-220352971      * |         2         0
    ##   11     chr1 220352972-234920000      * |         5         0
    ##   12     chr1 234920001-234999999      * |         2         0
    ##   13     chr1 235000000-249250621      * |         3         0
    ##   -------
    ##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

### plotCopyNumberCalls

The first function to plot the copy-number calls is `plotCopyNumberCalls`, which will plot them as colored rectangles over the genome. It will create 2 lines of rectangles: the top one with copy-number values and the bottom one with loss-of-heterozygosity in blue.

      kp <- plotKaryotype(chromosomes = "chr1")

      #plotCopyNumberCalls(kp, s1.calls)

By default we’ll see losses in green, 2n regions in gray and gains in yellow-orange-red. And the LOH regions as a blue line below the CN data. We can change the colors used with `cn.colors`. This parameter will take any value accepted by `getCopyNumberColors`, including the predefined palletes. You can find them all in the documentation of `getCopyNumberColors`. This fuction can also help us creating a legend.

      kp <- plotKaryotype(chromosomes="chr1")
      #plotCopyNumberCalls(kp, s1.calls, cn.colors = "red_blue", loh.color = "orange", r1=0.8)
      cn.cols <- getCopyNumberColors(colors = "red_blue")
      legend("top", legend=names(cn.cols), fill = cn.cols, ncol=length(cn.cols))

As with the other plotting functions, giving it a list of GRanges will plot them all.

      cn.calls <- list("Sample1"=s1.calls, "Sample2"=s2.calls, "Sample3"=s3.calls)
      kp <- plotKaryotype(chromosomes="chr1")

      #plotCopyNumberCalls(kp, cn.calls, r1=0.3)

### plotCopyNumberCallsAsLines

Another option is to plot the copy-number calls as lines using the function `plotCopyNumberCallsAsLines`. We’ll show a single chromosome in this case.

      kp <- plotKaryotype(chr="chr1")

      #plotCopyNumberCallsAsLines(kp, s1.calls)

In this case we can change the standard customization options and make it use segments instead of lines using the additional parameter `style`.

      kp <- plotKaryotype(chr="chr1")

      #plotCopyNumberCallsAsLines(kp, s1.calls, style = "segments")

### plotCopyNumberSummary

Finally, to plot a view of the accumulation of copy number alterations we can use `plotCopyNumberSummary`. It will create a coverage plot of gains and losses over all samples in our dataset.

      cn.cols <- getCopyNumberColors(colors = "green_orange_red")
      kp <- plotKaryotype(chromosomes="chr1")
      kpDataBackground(kp, color = cn.cols["2"], r0=0.3)

      #plotCopyNumberCalls(kp, cn.calls, loh.height = 0, r0=0.3)
      #plotCopyNumberSummary(kp, cn.calls, r1=0.25)

And we can change the appearance of the summary using the `direction` parameter.

      cn.cols <- getCopyNumberColors(colors = "green_orange_red")
      kp <- plotKaryotype(chromosomes="chr1")
      kpDataBackground(kp, color = cn.cols["2"], r0=0.3)

      #plotCopyNumberCalls(kp, cn.calls, loh.height = 0, r0=0.3)
      #plotCopyNumberSummary(kp, cn.calls, r1=0.25, direction = "out")

# Session Info

      sessionInfo()

    ## R version 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] CopyNumberPlots_1.26.0 karyoploteR_1.36.0     regioneR_1.42.0
    ##  [4] GenomicRanges_1.62.1   Seqinfo_1.0.0          IRanges_2.44.0
    ##  [7] S4Vectors_0.48.0       BiocGenerics_0.56.0    generics_0.1.4
    ## [10] knitr_1.51             BiocStyle_2.38.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] DBI_1.2.3                   bitops_1.0-9
    ##   [3] gridExtra_2.3               rlang_1.1.7
    ##   [5] magrittr_2.0.4              biovizBase_1.58.0
    ##   [7] matrixStats_1.5.0           compiler_4.5.2
    ##   [9] RSQLite_2.4.5               GenomicFeatures_1.62.0
    ##  [11] png_0.1-8                   vctrs_0.7.1
    ##  [13] ProtGenerics_1.42.0         stringr_1.6.0
    ##  [15] crayon_1.5.3                fastmap_1.2.0
    ##  [17] backports_1.5.0             XVector_0.50.0
    ##  [19] Rsamtools_2.26.0            rmarkdown_2.30
    ##  [21] UCSC.utils_1.6.1            bit_4.6.0
    ##  [23] xfun_0.56                   cn.mops_1.56.0
    ##  [25] cachem_1.1.0                cigarillo_1.0.0
    ##  [27] GenomeInfoDb_1.46.2         jsonlite_2.0.0
    ##  [29] blob_1.3.0                  rhdf5filters_1.22.0
    ##  [31] DelayedArray_0.36.0         Rhdf5lib_1.32.0
    ##  [33] BiocParallel_1.44.0         parallel_4.5.2
    ##  [35] cluster_2.1.8.1             R6_2.6.1
    ##  [37] VariantAnnotation_1.56.0    bslib_0.10.0
    ##  [39] stringi_1.8.7               RColorBrewer_1.1-3
    ##  [41] bezier_1.1.2                rtracklayer_1.70.1
    ##  [43] rpart_4.1.24                jquerylib_0.1.4
    ##  [45] Rcpp_1.1.1                  SummarizedExperiment_1.40.0
    ##  [47] base64enc_0.1-6             Matrix_1.7-4
    ##  [49] nnet_7.3-20                 rstudioapi_0.18.0
    ##  [51] dichromat_2.0-0.1           abind_1.4-8
    ##  [53] yaml_2.3.12                 codetools_0.2-20
    ##  [55] curl_7.0.0                  lattice_0.22-7
    ##  [57] Biobase_2.70.0              KEGGREST_1.50.0
    ##  [59] S7_0.2.1                    evaluate_1.0.5
    ##  [61] foreign_0.8-91              Biostrings_2.78.0
    ##  [63] BiocManager_1.30.27         MatrixGenerics_1.22.0
    ##  [65] checkmate_2.3.3             RCurl_1.98-1.17
    ##  [67] ensembldb_2.34.0            ggplot2_4.0.1
    ##  [69] scales_1.4.0                glue_1.8.0
    ##  [71] Hmisc_5.2-5                 lazyeval_0.2.2
    ##  [73] maketools_1.3.2             tools_4.5.2
    ##  [75] BiocIO_1.20.0               sys_3.4.3
    ##  [77] data.table_1.18.2.1         BSgenome_1.78.0
    ##  [79] GenomicAlignments_1.46.0    buildtools_1.0.0
    ##  [81] XML_3.99-0.20               rhdf5_2.54.1
    ##  [83] grid_4.5.2                  AnnotationDbi_1.72.0
    ##  [85] colorspace_2.1-2            htmlTable_2.4.3
    ##  [87] restfulr_0.0.16             Formula_1.2-5
    ##  [89] cli_3.6.5                   S4Arrays_1.10.1
    ##  [91] AnnotationFilter_1.34.0     gtable_0.3.6
    ##  [93] sass_0.4.10                 digest_0.6.39
    ##  [95] SparseArray_1.10.8          rjson_0.2.23
    ##  [97] htmlwidgets_1.6.4           farver_2.1.2
    ##  [99] memoise_2.0.1               htmltools_0.5.9
    ## [101] lifecycle_1.0.5             httr_1.4.7
    ## [103] bit64_4.6.0-1               bamsignals_1.42.0