Implements the "Log-Sum-Exponential trick" for calculating the log of the sums of
exponents without arithmetic underflows. This allows very small numbers to be
summed in log space.

Create networks of gene sets, infer clusters of functionally-related gene sets based
on similarity statistics, and visualize the results. This package simplifies and accelerates
interpretation of pathways analysis data sets. It is designed to work in tandem with standard
pathways analysis methods, such as the 'GSEA' program (Gene Set Enrichment Analysis), CERNO
(Coincident Extreme Ranks in Numerical Observations, implemented in the 'tmod' package) and others.
Inputs to 'GSNA' are the outputs of pathways analysis methods: a list of gene sets (or "modules"),
pathways or GO-terms with associated p-values. Since pathways analysis methods may be used to
analyze many different types of data including transcriptomic, epigenetic, and high-throughput
screen data sets, the 'GSNA' pipeline is applicable to these data as well. The use of 'GSNA' has
been described in the following papers:
Collins DR, Urbach JM, Racenet ZJ, Arshad U, Power KA, Newman RM, et al. (2021) <doi:10.1016/j.immuni.2021.08.007>,
Collins DR, Hitschfel J, Urbach JM, Mylvaganam GH, Ly NL, Arshad U, et al. (2023) <doi:10.1126/sciimmunol.ade5872>.

Jonathan M Urbach

GSNA

Gene Set Networking Analysis Package

lse function

<dl><dt>a</dt>
<dd>A numeric log value.</dd>
<dt>b</dt>
<dd>Another numeric log value.</dd></dl>

Arguments

lse — lse

<dl>

<dt>a</dt>
<dd>A numeric log value.</dd>


<dt>b</dt>
<dd>Another numeric log value.</dd>

</dl>

lse: lse

Description

Usage

Value

Arguments

Examples