get_topn

Helper function for compute_null_dnp - returns the top n genes by dnp for each sample

Provides a general toolkit for drug target identification. We include functionality to reduce large graphs to subgraphs and prioritize nodes. In addition to being optimized for use with generic graphs, we also provides support to analyze protein-protein interactions networks from online repositories. For more details on core method, refer to Weaver et al. (2021) <https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008755>.

Davis Weaver

crosstalkr

Analysis of Graph-Structured Data with a Focus on
Protein-Protein Interaction Networks

get_topn function

<dl><dt>df</dt>
<dd>output of <code>compute_dnp()</code></dd>
<dt>n_genes</dt>
<dd>integer describing number of genes per sample that we will compute the null distribution for</dd></dl>

Arguments

Helper function for compute_null_dnp - returns the top n genes by dnp for each sample — get_topn

<dl>

<dt>df</dt>
<dd>output of <code>compute_dnp()</code></dd>


<dt>n_genes</dt>
<dd>integer describing number of genes per sample that we will compute the null distribution for</dd>

</dl>

State of Data and AI Literacy Report 2025

get_topn: Helper function for compute_null_dnp - returns the top n genes by dnp for each sample

Description

Usage

Arguments