ednn_io_prepare

Preparing the input and output of the EDNN for a multiplex graph

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

PLEXI

Multiplex Network Analysis

Farzaneh Firoozbakht

ednn_io_prepare function

<dl><dt>edge.list</dt>
<dd>edge list as a dataframe with two columns.</dd>
<dt>edge.weight</dt>
<dd>edge weights as a dataframe. Each column corresponds to a graph. By default, the <code>colnames</code> are considered as outcomes unless indicated in <code>outcome</code> argument.</dd>
<dt>outcome</dt>
<dd>clinical outcomes for each graph. If not mentioned, the <code>colnames(edge.weight)</code> are considered by default.</dd>
<dt>indv.index</dt>
<dd>the index of individual networks.</dd>
<dt>edge.threshold</dt>
<dd>numeric value to set edge weights below the threshold to zero (default: 0). the greater edge weights do not change.</dd>
<dt>walk.rep</dt>
<dd>number of repeats for the random walk (default: 100).</dd>
<dt>n.steps</dt>
<dd>number of the random walk steps (default: 5).</dd>
<dt>random.walk</dt>
<dd>boolean value to enable the random walk algorithm (default: TRUE).</dd>
<dt>verbose</dt>
<dd>if TRUE a progress bar is shown.</dd></dl>

Arguments

Preparing the input and output of the EDNN for a multiplex graph — ednn_io_prepare

<dl>

<dt>edge.list</dt>
<dd>edge list as a dataframe with two columns.</dd>


<dt>edge.weight</dt>
<dd>edge weights as a dataframe. Each column corresponds to a graph. By default, the <code>colnames</code> are considered as outcomes unless indicated in <code>outcome</code> argument.</dd>


<dt>outcome</dt>
<dd>clinical outcomes for each graph. If not mentioned, the <code>colnames(edge.weight)</code> are considered by default.</dd>


<dt>indv.index</dt>
<dd>the index of individual networks.</dd>


<dt>edge.threshold</dt>
<dd>numeric value to set edge weights below the threshold to zero (default: 0). the greater edge weights do not change.</dd>


<dt>walk.rep</dt>
<dd>number of repeats for the random walk (default: 100).</dd>


<dt>n.steps</dt>
<dd>number of the random walk steps (default: 5).</dd>


<dt>random.walk</dt>
<dd>boolean value to enable the random walk algorithm (default: TRUE).</dd>


<dt>verbose</dt>
<dd>if TRUE a progress bar is shown.</dd>

</dl>

ednn_io_prepare: Preparing the input and output of the EDNN for a multiplex graph

Description

Usage

Value

Arguments

Examples