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<h5>Description:</h5>
<p>Dyngen is a novel, multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. dyngen is more flexible than current single-cell simulation engines, and allows better method development and benchmarking, thereby stimulating development and testing of novel computational methods.</p>

<h5>More Information:</h5>
<ul>
<li><strong>Grand Challenge/Workpackage(s)</strong>: <a href="/nl/onderzoek/onderzoeksuitdagingen-2019-2023/ai-driven-data-science"[entry:58:/onderzoek/onderzoeksutidagingen-2019-2023/ai-driven-data-science]data-entry-id="58" target="_blank" rel="noopener">GC1</a></li>
<li><strong>Responsible Research Lead</strong>: <a href="mailto:yvan.saeys@ugent.be" target="_blank" rel="noopener">Yvan Saeys</a></li>
<li><strong>Authors</strong>: <a href="mailto:brechtus@gmail.com" target="_blank" rel="noopener">Robrecht Cannoodt</a>, <a href="mailto:Louise.Deconinck@ugent.be" target="_blank" rel="noopener">Louise Deconinck</a>, <a href="mailto:Wouter.Saelens@gmail.com" target="_blank" rel="noopener">Wouter Saelens</a>, <a href="mailto:yvan.saeys@ugent.be" target="_blank" rel="noopener">Yvan Saeys&nbsp;</a></li>
<li><strong>Programming Language</strong>: R</li>
<li><a href="https://github.com/dynverse/dyngen" target="_blank" rel="noopener">Link to the repository</a></li>
<li><strong>Categories</strong>: Single cell and DNA sequence analysis, networks and graphs, un/semi-supervised learning</li>
</ul>

<h5>Publications:</h5>
<ul>
<li>R. Cannoodt,&nbsp; W. Saelens, L. Deconinck and Y. Saeys, "<a href="https://doi.org/10.1038/s41467-021-24152-2" target="_blank" rel="noopener">Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells</a>", Nat Commun 12, 3942 (2021).&nbsp;</li>
</ul>

<p>A simulation engine for dynamic models of single-cell transcriptomics data.</p>