text: user16220: A collegue of mine recently suggested to try the louvain algorithm for clustering multiplex cytometry data. However, implementations of louvain are kind of rare in R. To my knowledge the only stand-alone implementation is included in the igraph package.
  So far i tried to use the adjacency matrix that the umap function returns (uwot package) to create a graph using igraphs graph_from_adjacency_matrix function. I can run the louvain algorithm on the graph, but the result is always a few thousand clusters with a hand-full if cells. changing the resolution parameter does not change anything.
  If i remember correctly, Seurats findClusters function uses louvain, however i don't want to use PCA reduction before clustering, which is requiered in Seurat to find clusters.
  Does anybody know of a different implementation of louvain or leiden algorithm that or an easier way to use igraphsfunctions? I obviously don't have to use the matrix umap returned, but what would be a better approach to make my data "louvain-able"?
  
  user3848: Both conda and PyPI have leiden clustering in Python which operates via iGraph
  conda install -c conda-forge leidenalg
  
  pip install leiden-clustering
  
  Used via
  import leidenalg as la
  import igraph as ig
  
  Example output
  
  The docs are here. You will not need much Python to use it.
  There is an entire Leiden package in R-cran here
  Python implements Rs Leiden package here,
  conda install -c conda-forge r-leiden
  
  
original_text: 16220: <p>A collegue of mine recently suggested to try the louvain algorithm for clustering multiplex cytometry data. However, implementations of louvain are kind of rare in R. To my knowledge the only stand-alone implementation is included in the <code>igraph</code> package.
  So far i tried to use the adjacency matrix that the <code>umap</code> function returns (<code>uwot</code> package) to create a graph using <code>igraphs</code> <code>graph_from_adjacency_matrix</code> function. I can run the louvain algorithm on the graph, but the result is always a few thousand clusters with a hand-full if cells. changing the resolution parameter does not change anything.</p>
  <p>If i remember correctly, <code>Seurats</code> <code>findClusters</code> function uses <code>louvain</code>, however i don't want to use PCA reduction before clustering, which is requiered in <code>Seurat</code> to find clusters.</p>
  <p>Does anybody know of a different implementation of louvain or leiden algorithm that or an easier way to use <code>igraphs</code>functions? I obviously don't have to use the matrix umap returned, but what would be a better approach to make my data &quot;louvain-able&quot;?</p>
   3848: <p>Both conda and PyPI have leiden clustering in Python which operates via <code>iGraph</code></p>
  <pre><code>conda install -c conda-forge leidenalg
  
  pip install leiden-clustering
  </code></pre>
  <p>Used via</p>
  <pre><code>import leidenalg as la
  import igraph as ig
  </code></pre>
  <p>Example output</p>
  <p><a href="https://i.stack.imgur.com/F8xMh.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/F8xMh.png" alt="enter image description here" /></a></p>
  <p>The docs are <a href="https://leidenalg.readthedocs.io/en/stable/intro.html" rel="nofollow noreferrer">here</a>. You will not need much Python to use it.</p>
  <p>There is an entire Leiden package in R-cran <a href="https://cran.r-project.org/web/packages/leiden/index.html" rel="nofollow noreferrer">here</a></p>
  <p>Python implements Rs Leiden package here,</p>
  <pre><code>conda install -c conda-forge r-leiden
  </code></pre>
  