p_val_rank

Calculate p.value for x given a set of null values using ranks

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

p_val_rank function

<dl><dt>x</dt>
<dd>numeric value</dd>
<dt>null.values</dt>
<dd>a numeric vector of null distribution samples</dd>
<dt>alternative</dt>
<dd>alterative test including: <code>"two.sided"</code> [default],
<code>"greater"</code>, and <code>"less"</code></dd></dl>

Arguments

Calculate p.value for x given a set of null values using ranks — p_val_rank

<dl>

<dt>x</dt>
<dd>numeric value</dd>


<dt>null.values</dt>
<dd>a numeric vector of null distribution samples</dd>


<dt>alternative</dt>
<dd>alterative test including: <code>"two.sided"</code> [default],
<code>"greater"</code>, and <code>"less"</code></dd>

</dl>

p_val_rank: Calculate p.value for x given a set of null values using ranks

Description

Usage

Value

Arguments

Examples