binaryA

Amean

the number of standard deviations above zero
that an element of Amean must be to get a value of 1

threshold


<code>binaryA</code> creates a binarized heatmap of the A matrix
in which the value is 1 if the value in Amean is greater than
threshold * Asd and 0 otherwise


Coordinated Gene Activity in Pattern Sets (CoGAPS)
implements a Bayesian MCMC matrix factorization algorithm,
GAPS, and links it to gene set statistic methods to infer biological
process activity.  It can be used to perform sparse matrix factorization on
any data, and when this data represents biomolecules, to do gene set
analysis.

Elana J Fertig

CoGAPS

Coordinated Gene Activity in Pattern Sets

binaryA function

<code>binaryA</code> creates a binarized heatmap of the A matrix
in which the value is 1 if the value in Amean is greater than
threshold * Asd and 0 otherwise

binaryA: `binaryA` creates a binarized heatmap of the A matrix in which the value is 1 if the value in Amean is greater than threshold * Asd and 0 otherwise

Description

Usage

Arguments