Provided by: cnvkit_0.9.9-2_amd64
NAME
cnvkit_segmetrics - Compute segment-level metrics from bin-level log2 ratios.
DESCRIPTION
usage: cnvkit 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). optional arguments: -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.