network_gen

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

network_gen function

<dl><dt>N_nodes</dt>
<dd>number of nodes in the graph</dd>
<dt>N_var_nodes</dt>
<dd>number of nodes whose neighborhood should change from layer 1 to 2</dd>
<dt>N_var_nei</dt>
<dd>number of neighbors that should be changing from layer 1 to 2</dd>
<dt>noise_sd</dt>
<dd>the standard deviation of the noise added to the edge weights</dd></dl>

Arguments

Multiplex Network Generation — network_gen

<dl>

<dt>N_nodes</dt>
<dd>number of nodes in the graph</dd>


<dt>N_var_nodes</dt>
<dd>number of nodes whose neighborhood should change from layer 1 to 2</dd>


<dt>N_var_nei</dt>
<dd>number of neighbors that should be changing from layer 1 to 2</dd>


<dt>noise_sd</dt>
<dd>the standard deviation of the noise added to the edge weights</dd>

</dl>

network_gen: Multiplex Network Generation

Description

Usage

Value

Arguments

Details

Examples