---
name: "cnvkit-segmetrics"
section: "1"
platform: "linux"
package: "cnvkit"
version: "0.9.12-1"
release: "Debian 13 (Trixie)"
platforms: ["linux"]
url: "https://new.carta.tech/cnvkit-segmetrics"
---

# cnvkit-segmetrics(1)

Compute segment-level metrics from bin-level log2 ratios.

## NAME

cnvkit\_segmetrics - Compute segment-level metrics from bin-level log2 ratios.

## DESCRIPTION

usage: cnvkit.py segmetrics \[-h\] **-s** SEGMENTS \[--drop-low-coverage\]

- \[-o FILENAME\] \[--mean\] \[--median\] \[--mode\]

  \[--t-test\] \[--stdev\] \[--sem\] \[--mad\] \[--mse\] \[--iqr\] \[--bivar\] \[--ci\] \[--pi\] \[-a ALPHA\] \[-b BOOTSTRAP\] \[--smooth-bootstrap\] cnarray

### positional arguments:

- cnarray

  Bin-level copy ratio data file (\*.cnn, \*.cnr).

### options:

- **-h**, **--help**

  show this help message and exit

- **-s** SEGMENTS, **--segments** SEGMENTS

  Segmentation data file (\*.cns, output of the 'segment' command).

- **--drop-low-coverage**

  Drop very-low-coverage bins before calculations to avoid negative bias in poor-quality tumor samples.

- **-o** FILENAME, **--output** FILENAME

  Output table file name.

### Statistics available:

- **--mean**

  Mean log2 ratio (unweighted).

- **--median**

  Median.

- **--mode**

  Mode (i.e. peak density of bin log2 ratios).

- **--t-test**

  One-sample t-test of bin log2 ratios versus 0.0.

- **--stdev**

  Standard deviation.

- **--sem**

  Standard error of the mean.

- **--mad**

  Median absolute deviation (standardized).

- **--mse**

  Mean squared error.

- **--iqr**

  Inter-quartile range.

- **--bivar**

  Tukey's biweight midvariance.

- **--ci**

  Confidence interval (by bootstrap).

- **--pi**

  Prediction interval.

- **-a** ALPHA, **--alpha** ALPHA

  Level to estimate confidence and prediction intervals; use with **--ci** and **--pi**. \[Default: 0.05\]

- **-b** BOOTSTRAP, **--bootstrap** BOOTSTRAP

  Number of bootstrap iterations to estimate confidence interval; use with **--ci**. \[Default: 100\]

- **--smooth-bootstrap**

  Apply Gaussian noise to bootstrap samples, a.k.a. smoothed bootstrap, to estimate confidence interval; use with **--ci**.