Man page - cnvkit-segmetrics(1)

Packages contains this manual

Package:  cnvkit
apt-get install cnvkit

Manuals in package:
• cnvkit-import-theta(1)
• cnvkit-coverage(1)
• cnvkit-metrics(1)
• cnvkit-import-seg(1)
• cnvkit-export(1)
• cnvkit-breaks(1)
• cnvkit-fix(1)
• cnvkit-segmetrics(1)
• cnvkit-batch(1)
• cnvkit-import-rna(1)
• cnvkit-bintest(1)
• cnvkit-diagram(1)
• cnvkit-call(1)
• cnvkit-autobin(1)
• cnvkit-access(1)
• cnvkit-antitarget(1)
• cnvkit-sex(1)
• cnvkit-segment(1)
• cnvkit-genemetrics(1)
• cnvkit-heatmap(1)
• cnvkit-target(1)
• cnvkit-import-picard(1)
• cnvkit-reference(1)
• cnvkit-scatter(1)
Documentations in package:
• cnvkit

Manual

CNVKIT_SEGMETRICS

NAME
DESCRIPTION
positional arguments:
options:
Statistics available:

---

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 .

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