text: user1855: I’ve run the cellranger analysis pipeline on single cell RNASeq datasets. I can import the matrix and graph-based clusters into R. Doing this I can optimise the dimension reduction and plot cells with coloured by clusters generated by cellranger. I wish to optimise the graph-based clustering as well.
  Is it possible to either:
  
  obtain the graph generated by cellranger from the files generated. (I can only find the final clusters in the analysis output)
  reproduce generating a similar graph (used for clustering) from the output matrix (preferably with R or Python.
  
  With either approach, I am looking for a list of edges for nearest neighbors in the gene-barcode UMI matrix to use as input for a clustering algorithm.
  
  user181: Cell Ranger
  You can't download the tSNE coordinates for cells directly from the Analysis tab of the fancy, polished .html document that Cell Ranger produces. If you have access to the machine on which the pipeline was run, you can grab the intermediate files. Everything is well documented by 10X. They explain defaults etc. on that page. The tSNE coordinates are available at:
  analysis/tsne/2_components/projection.csv
  
  and the contents look like:
  $ head -5 analysis/tsne/2_components/projection.csv
  Barcode,TSNE-1,TSNE-2
  AAACATACAACGAA-1,-13.5494,1.4674
  AAACATACTACGCA-1,-2.7325,-10.6347
  AAACCGTGTCTCGC-1,12.9590,-1.6369
  AAACGCACAACCAC-1,-9.3585,-6.7300
  
  Seurat
  And the second point: Cell Ranger is replicating the Seurat workflow. Seurat uses the Louvain method for community detection by default. Check out the intro tutorial and see if you can follow along. This tutorial works with 10X data, in fact the first function call in the tutorial reads in the 10X matrices. If you are already plotting in R you'll speed through this tutorial. Seurat is great to work with, you'll have tSNEs and clusters in no time. And there's so much more you can do from there!
  
original_text: 1855: <p>I’ve run the cellranger analysis pipeline on single cell RNASeq datasets. I can import the matrix and graph-based clusters into R. Doing this I can optimise the dimension reduction and plot cells with coloured by clusters generated by cellranger. I wish to optimise the graph-based clustering as well.</p>
  
  <p>Is it possible to either:</p>
  
  <ul>
  <li><p>obtain the graph generated by cellranger from the files generated. (I can only find the final clusters in the analysis output)</p></li>
  <li><p>reproduce generating a similar graph (used for clustering) from the output matrix (preferably with R or Python.</p></li>
  </ul>
  
  <p>With either approach, I am looking for a list of edges for nearest neighbors in the gene-barcode UMI matrix to use as input for a clustering algorithm.</p>
   181: <p><strong>Cell Ranger</strong></p>
  
  <p>You can't download the tSNE coordinates for cells directly from the <em>Analysis</em> tab of the fancy, polished .html document that Cell Ranger produces. If you have access to the machine on which the pipeline was run, you can grab the intermediate files. Everything is <a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/output/analysis" rel="nofollow noreferrer">well documented</a> by 10X. They explain defaults etc. on that page. The tSNE coordinates are available at:</p>
  
  <pre><code>analysis/tsne/2_components/projection.csv
  </code></pre>
  
  <p>and the contents look like:</p>
  
  <pre><code>$ head -5 analysis/tsne/2_components/projection.csv
  Barcode,TSNE-1,TSNE-2
  AAACATACAACGAA-1,-13.5494,1.4674
  AAACATACTACGCA-1,-2.7325,-10.6347
  AAACCGTGTCTCGC-1,12.9590,-1.6369
  AAACGCACAACCAC-1,-9.3585,-6.7300
  </code></pre>
  
  <p><strong>Seurat</strong></p>
  
  <p>And the second point: Cell Ranger is replicating the Seurat workflow. Seurat uses the Louvain method for community detection by default. Check out the <a href="https://satijalab.org/seurat/pbmc3k_tutorial.html" rel="nofollow noreferrer">intro tutorial</a> and see if you can follow along. This tutorial works with 10X data, in fact the first function call in the tutorial reads in the 10X matrices. If you are already plotting in R you'll speed through this tutorial. Seurat is great to work with, you'll have tSNEs and clusters in no time. And there's so much more you can do from there!</p>
  