WeightdRandomWalk

Interactions between different biological entities are crucial for the function of biological systems.
In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted.
The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments.
However, such variations often occur locally and do not concern the whole network.
To capture local variations of such networks, we propose multiplex network differential analysis (MNDA).
MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation.
Yousefi et al. (2023) <doi:10.1101/2023.01.22.525058>.

Behnam Yousefi

mnda

Multiplex Network Differential Analysis (MNDA)

Farzaneh Firoozbakht

WeightdRandomWalk function

<dl><dt>graph</dt>
<dd>an igraph object</dd>
<dt>startNode</dt>
<dd>the starting node (i.e. a node name or a node index)</dd>
<dt>maxStep</dt>
<dd>maximum number steps (default:5)</dd>
<dt>node_names</dt>
<dd>a list of names for nodes</dd></dl>

Arguments

Weighted Random Walk algorithm — WeightdRandomWalk

<dl>

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


<dt>startNode</dt>
<dd>the starting node (i.e. a node name or a node index)</dd>


<dt>maxStep</dt>
<dd>maximum number steps (default:5)</dd>


<dt>node_names</dt>
<dd>a list of names for nodes</dd>

</dl>

WeightdRandomWalk: Weighted Random Walk algorithm

Description

Usage

Value

Arguments

Examples