reach

Calculate largest reach centrality

It aims to find significant pathways through
network topology information. It has several advantages compared
with current pathway enrichment tools. First, pathway node instead
of single gene is taken as the basic unit when analysing networks
to meet the fact that genes must be constructed into complexes to
hold normal functions. Second, multiple network centrality measures are
applied simultaneously to measure importance of nodes from different
aspects to make a full view on the biological system. CePa extends
standard pathway enrichment methods, which include both over-representation
analysis procedure and gene-set analysis procedure.
<doi:10.1093/bioinformatics/btt008>.

Zuguang Gu

CePa

Centrality-Based Pathway Enrichment

reach function

<dl>
 <dt>graph</dt>
<dd>an <code>igraph</code> object</dd> <dt>mode</dt>
<dd>mode of the centrality</dd> <dt>weights</dt>
<dd>If the edges in the graph have weight, then by default, the weight is used to calculate the length of the shortest path. Set it to NULL to supress the weight.</dd>
</dl>

Arguments

Calculate largest reach centrality — reach

<dl>


 <dt>graph</dt>
<dd>an <code>igraph</code> object</dd>

 <dt>mode</dt>
<dd>mode of the centrality</dd>

 <dt>weights</dt>
<dd>If the edges in the graph have weight, then by default, the weight is used to calculate the length of the shortest path. Set it to NULL to supress the weight.</dd>


</dl>

reach: Calculate largest reach centrality

Description

Usage

Arguments

Details

Examples