Plots and graphics


Plot bin-level log2 coverages and segmentation calls together. Without any further arguments, this plots the genome-wide copy number in a form familiar to those who have used array CGH. scatter Sample.cnr -s Sample.cns
# Shell shorthand scatter -s{s,r}

The options --chromosome and --gene (or their single-letter equivalents) focus the plot on the specified region: scatter -s{s,r} -c chr7 scatter -s{s,r} -c chr7:140434347-140624540 scatter -s{s,r} -g BRAF

In the latter two cases, the genes in the specified region or with the specified names will be highlighted and labeled in the plot. The --width (-w) argument determines the size of the chromosomal regions to show flanking the selected region. Note that only targeted genes can be highlighted and labeled; genes that are not included in the list of targets are not labeled in the .cnn or .cnr files and are therefore invisible to CNVkit.

The arguments -c and -g can be combined to e.g. highlight specific genes in a larger context:

# Show a chromosome arm, highlight one gene scatter -s{s,r} -c chr5:100-50000000 -g TERT
# Show the whole chromosome, highlight two genes scatter -s{s,r} -c chr7 -g BRAF,MET
# Highlight two genes in a specified range scatter -s{s,r} -c chr12:50000000-80000000 -g CDK4,MDM2

When a chromosomal region is plotted with CNVkit’s “scatter” command , the size of the plotted datapoints is proportional to the weight of each point used in segmentation – a relatively small point indicates a less reliable bin. Therefore, if you see a cluster of smaller points in a short segment (or where you think there ought to be a segment, but there isn’t one), then you can cast some doubt on the copy number call in that region. The dispersion of points around the segmentation line also visually indicates the level of noise or uncertainty.

To create multiple region-specific plots at once, the regions of interest can be listed in a separate file and passed to the scattter command with the -l/--range-list option. This is equivalent to creating the plots separately with the -c option and then combining the plots into a single multi-page PDF.

The bin-level log2 ratios or coverages can also be plotted without segmentation calls: scatter Sample.cnr

This can be useful for viewing the raw, un-corrected coverage depths when deciding which samples to use to build a profile, or simply to see the coverages without being helped/biased by the called segments.

The --trend option (-t) adds a smoothed trendline to the plot. This is fairly superfluous if a valid segment file is given, but could be helpful if the CBS dependency is not available, or if you’re skeptical of the segmentation in a region.

SNV b-allele frequencies

Loss of heterozygosity (LOH) can be viewed alongside copy number by passing variants as a VCF file with the -v option. Heterozygous SNP allelic frequencies are shown in a subplot below the CNV scatter plot. scatter Sample.cnr -s Sample.cns -v Sample.vcf

If only the VCF file is given by itself, just plot the allelic frequencies: scatter -v Sample.vcf

Given segments, show the mean b-allele frequency values above and below 0.5 of SNVs falling within each segment. Divergence from 0.5 indicates LOH in the tumor sample. scatter -s Sample.cns -v Sample.vcf -i TumorID -n NormalID

Regions with LOH are reflected in heterozygous germline SNPs in the tumor sample with allele frequencies shifted away from the expected 0.5 value. Given a VCF with only the tumor sample called, it is difficult to focus on just the informative SNPs because it’s not known which SNVs are present and heterozygous in normal, germline cells. Better results can be had by giving CNVkit more information:

  • Call somatic mutations using paired tumor and normal samples. In the VCF, the somatic variants should be flagged in the INFO column with the string “SOMATIC”. (MuTect does this automatically.) Then CNVkit will skip these for plotting.
  • Add a “PEDIGREE” tag to the VCF header, listing the tumor sample as “Derived” and the normal as “Original”. (MuTect doesn’t do this, but it does add a nonstandard GATK header that CNVkit can extract the same information from.)
  • In lieu of a PEDIGREE tag, tell CNVkit which sample IDs are the tumor and normal using the -i and -n options, respectively.
  • If no paired normal sample is available, you can still filter for likely informative SNPs by intersecting your tumor VCF with a set of known SNPs such as 1000 Genomes, ESP6500, or ExAC. Drop the private SNVs that don’t appear in these databases to create a VCF more amenable to LOH detection.


Draw copy number (either individual bins (.cnn, .cnr) or segments (.cns)) on chromosomes as an ideogram. If both the bin-level log2 ratios and segmentation calls are given, show them side-by-side on each chromosome (segments on the left side, bins on the right side). diagram Sample.cnr diagram -s Sample.cns diagram -s Sample.cns Sample.cnr

If bin-level log2 ratios are provided (.cnr), genes with log2 ratio values beyond a fixed threshold will be labeled on the plot. This plot style works best with target panels of a few hundred genes at most; with whole-exome sequencing there are often so many genes affected by CNAs that the individual gene labels become difficult to read.


If only segments are provided (-s), gene labels are not shown. This plot is then equivalent to the heatmap command, which effectively summarizes the segmented values from many samples.

By default, the sex chromosomes X and Y are colorized relative to the expected ploidy, i.e. for female samples analyzed with a male reference, while the X chromosome has a copy ratio near +1.0 in the input .cnr and .cns files, in the output diagram it will be shown as neutral copy number (white or faint colors) rather than a gain (red), because the diploid X is expected. The sample sex can be specified with the -x/--sample-sex option, or will otherwise be guessed automatically (see Chromosomal sex). This correction is done by default, but can be disabled with the option --no-shift-xy.


Draw copy number (either bins (.cnn, .cnr) or segments (.cns)) for multiple samples as a heatmap.

To get an overview of the larger-scale CNVs in a cohort, use the “heatmap” command on all .cns files: heatmap *.cns

The color range can be subtly rescaled with the -d option to de-emphasize low-amplitude segments, which are likely spurious CNAs: heatmap *.cns -d

A heatmap can also be drawn from bin-level log2 coverages or copy ratios (.cnn, .cnr), but this will be extremely slow at the genome-wide level. Consider doing this with a smaller number of samples and only for one chromosome or chromosomal region at a time, using the -c option: heatmap TR_9*T.cnr -c chr12  # Slow! heatmap TR_9*T.cnr -c chr7:125000000-145000000

If an output file name is not specified with the -o option, an interactive matplotlib window will open, allowing you to select smaller regions, zoom in, and save the image as a PDF or PNG file.

The samples are shown in the order there’s given on the command line. If you use “*.cns” then the filenames might always be fetched alphabetically (depending on your operating system), but if you type them out in the order you like, it should keep that order. You can use the Unix shell to pull the names out of a file on the fly, e.g.: heatmap `cat filenames.txt`

As with diagram, the sex chromosomes X and Y are colorized relative to the expected ploidy, based on the sample and reference sex (see Chromosomal sex). This correction can be disabled with the option --no-shift-xy.

Customizing plots

The plots generated with the scatter and heatmap commands use the Python plotting library matplotlib. You can customize font sizes and other aspects of the plots by configuring matplotlib.

If you’re running CNVkit on the command line and not using it as a Python library, then you can just create a file in your home directory (or the same directory as called .matplotlibrc. For example, to shrink the font size of the x- and y-axis labels, put this line in the configuration file:

axes.labelsize      : small

Alternatively, use the cnvlib module in Python (see Python API (cnvlib package)) to create the plots and then modify the plot elements using matplotlib.pyplot.