# Text and tabular reports¶

## breaks¶

List the targeted genes in which a segmentation breakpoint occurs.

cnvkit.py breaks Sample.cnr Sample.cns


This helps to identify genes in which (a) an unbalanced fusion or other structural rearrangement breakpoint occured, or (b) CNV calling is simply difficult due to an inconsistent copy number signal.

The output is a text table of tab-separated values, which is amenable to further processing by scripts and standard Unix tools such as grep, sort, cut and awk.

Columns:

• gene, chromosome – as in .cns (see Target and antitarget bin-level coverages (.cnn)), the gene where the breakpoint occurs and the chromosome it lies on.
• location – the end of the segment to the left of the breakpoint, and start of the segment to the right.
• change – the difference in log2 values between the adjacent segments.
• probes_left, probes_right – the number of probes on each side of the breakpoint within the gene. (Not the same as the number of probes supporting each segment; just the portion within the gene.)

For example, to get a list of the names of genes that contain a possible copy number breakpoint (e.g. unbalanced translocation):

cnvkit.py breaks Sample.cnr Sample.cns | cut -f1 | sort -u > gene-breaks.txt


## genemetrics¶

Identify targeted genes with copy number gain or loss above or below a threshold. (Formerly called gainloss.)

cnvkit.py genemetrics Sample.cnr
cnvkit.py genemetrics Sample.cnr -s Sample.cns -t 0.4 -m 5 -y


The first four columns of output table show each targeted gene’s name and its genomic coordinates (based on the first and last bins with that label in the original target BED file, and thus the .cnr file).

The remaining output columns have slightly different meaning depending on whether or not segments were provided. Without segments (.cnr alone):

• log2: Weighted mean of log2 ratios of all the gene’s bins, including any off-target intronic bins.
• depth: Weighted mean of un-normalized read depths across all this gene’s bins.
• weight: Sum of this gene’s bins’ weights.
• nbins: The number of bins assigned to this gene.

With segments (-s):

• log2: The log2 ratio value of the segment covering the gene, i.e. weighted mean of all bins covered by the whole segment, not just this gene.
• depth, weight, probes: As above.
• seg_weight: The sum of the weights of the bins supporting the segment.
• seg_probes: The number of probes supporting the segment.

The -t/--threshold and -m/--min-probes options are used to control which genes are reported:

• A threshold of .2 (the default) will report single-copy gains and losses in a completely pure tumor sample (or germline CNVs), but a lower threshold would be necessary to call somatic CNAs if significant normal-cell contamination is present.
• Some likely false positives can be eliminated by dropping CNVs that cover a small number of bins, at the risk of missing some true positives. With -m 3, the default, genes where only 1 or 2 bins show copy number change will not be reported. This applies to the segment’s bin count (seg_probes) if segments are provided with -s, otherwise it’s the gene’s bin count (nbins).

Specify the reference X-chromosome ploidy (-y if the same option was used when constructing the reference) to ensure CNVs on the X chromosome are reported correctly; otherwise, a large number of spurious gains or losses may be reported.

Note

Where more than one segment overlaps the gene, i.e. if the gene contains a breakpoint, each segment’s value will be reported as a separate row for the same gene. If a large-scale CNA covers multiple genes, each of those genes will be listed individually.

The output is a text table of tab-separated values, like that of breaks. Continuing the Unix example, we can try genemetrics both with and without the segment files, take the intersection of those as a list of “trusted” genes, and visualize each of them with scatter:

cnvkit.py genemetrics -y Sample.cnr -s Sample.cns | tail -n+2 | cut -f1 | sort > segment-genes.txt
cnvkit.py genemetrics -y Sample.cnr | tail -n+2 | cut -f1 | sort > ratio-genes.txt
comm -12 ratio-genes.txt segment-genes.txt > trusted-genes.txt
for gene in cat trusted-genes.txt
do
cnvkit.py scatter -s Sample.cn{s,r} -g $gene -o Sample-$gene-scatter.pdf
done


(The point is that it’s possible.)

## sex¶

Guess samples’ chromosomal sex from the relative coverage of chromosomes X and Y. A table of the sample name (derived from the filename), inferred sex (string “Female” or “Male”), and log2 ratio value of chromosomes X and Y is printed.

cnvkit.py sex *.cnn *.cnr *.cns
cnvkit.py sex -y *.cnn *.cnr *.cns


If there is any confusion in specifying either the sex of the sample or the construction of the reference copy number profile, you can check what happened using the sex command. If the reference and intermediate .cnn files are available (.targetcoverage.cnn and .antitargetcoverage.cnn, which are created before most of CNVkit’s corrections), CNVkit can report the reference sex and the sample’s relative coverage of the X and Y chromosomes:

cnvkit.py sex reference.cnn Sample.targetcoverage.cnn Sample.antitargetcoverage.cnn


The output looks like this, where columns are filename, inferred sex, and ratio of chromosome X and Y log2 coverages relative to autosomes:

cnv_reference.cnn   Female  -0.176  -1.061
Sample.targetcoverage.cnn   Female  -0.0818 -12.471
Sample.antitargetcoverage.cnn       Female  -0.265  -15.139


If the -y option was not specified when constructing the reference (e.g. cnvkit.py batch ...), then you have a female reference, and in the final plots you will see chrX with neutral copy number in female samples and around -1 log2 ratio in male samples.

## metrics¶

Calculate the spread of bin-level copy ratios from the corresponding final segments using several statistics. These statistics help quantify how “noisy” a sample is and help to decide which samples to exclude from an analysis, or to select normal samples for a reference copy number profile.

For a single sample:

cnvkit.py metrics Sample.cnr -s Sample.cns


(Note that the order of arguments and options matters here, unlike the other commands: Everything after the -s flag is treated as a segment dataset.)

Multiple samples can be processed together to produce a table:

cnvkit.py metrics S1.cnr S2.cnr -s S1.cns S2.cns
cnvkit.py metrics *.cnr -s *.cns


Several bin-level log2 ratio estimates for a single sample, such as the uncorrected on- and off-target coverages and the final bin-level log2 ratios, can be compared to the same final segmentation (reusing the given segments for each coverage dataset):

cnvkit.py metrics Sample.targetcoverage.cnn Sample.antitargetcoverage.cnn Sample.cnr -s Sample.cns


In each case, given the bin-level copy ratios (.cnr) and segments (.cns) for a sample, the log2 ratio value of each segment is subtracted from each of the bins it covers, and several estimators of spread are calculated from the residual values. The output table shows for each sample:

• Total number of segments (in the .cns file) – a large number of segments can indicate that the sample has either many real CNAs, or noisy coverage and therefore many spurious segments.
• Uncorrected sample standard deviation – this measure is prone to being inflated by a few outliers, such as may occur in regions of poor coverage or if the targets used with CNVkit analysis did not exactly match the capture. (Also note that the log2 ratio data are not quite normally distributed.) However, if a sample’s standard deviation is drastically higher than the other estimates shown by the metrics command, that helpfully indicates the sample has some outlier bins.
• Median absolute deviation (MAD) – very robust against outliers, but less statistically efficient.
• Interquartile range (IQR) – another robust measure that is easy to understand.
• Tukey’s biweight midvariance – a robust and efficient measure of spread.

Note that many small segments will fit noisy data better, shrinking the residuals used to calculate the other estimates of spread, even if many of the segments are spurious. One possible heuristic for judging the overall noisiness of each sample in a table is to multiply the number of segments by the biweight midvariance – the value will tend to be higher for unreliable samples. Check questionable samples for poor coverage (using e.g. bedtools, chanjo, IGV or Picard CalculateHsMetrics).

Finally, visualizing a sample with CNVkit’s scatter command will often make it apparent whether a sample or the copy ratios within a genomic region can be trusted.

## segmetrics¶

Calculate summary statistics of the residual bin-level log2 ratio estimates from the segment means, similar to the existing metrics command, but for each segment individually.

Results are output in the same format as the CNVkit segmentation file (.cns), with the stat names and calculated values printed in additional columns.

cnvkit.py segmetrics Sample.cnr -s Sample.cns --iqr
cnvkit.py segmetrics -s Sample.cn{s,r} --ci --pi


Supported stats:

• Alternative estimators of segment mean, which ignore bin weights: --mean, -median, --mode.
• As in metrics: standard deviation (--std), median absolute deviation (--mad), inter-quartile range (--iqr), Tukey’s biweight midvariance (--bivar)
• Additionally: mean squared error (--mse), standard error of the mean (-sem).
• Confidence interval of the segment mean (--ci), estimated by bootstrap (100 resamplings) of the bin-level log2 ratio values within the segment. The upper and lower bounds are output as separate columns ci_lo and ci_hi.
• Prediction interval (--pi), estimated by the range between the 2.5-97.5 percentiles of the segment’s bin-level log2 ratios. The upper and lower bounds are output as columns pi_lo and pi_hi.

The --ci and --sem values obtained here can also be used in the call command for filtering segments.