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.