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<title>Convert Conos Object to ScanPy</title>

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<h1 class="title toc-ignore">Convert Conos Object to ScanPy</h1>



<div id="install-auxilliary-data-packages" class="section level2">
<h2>Install Auxilliary Data Packages</h2>
<p>First install the auxilliary packages for conos, <a href="https://github.com/kharchenkolab/conosPanel">conosPanel</a>:</p>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">&#39;conosPanel&#39;</span>, <span class="at">repos=</span><span class="st">&#39;https://kharchenkolab.github.io/drat/&#39;</span>, <span class="at">type=</span><span class="st">&#39;source&#39;</span>)</span></code></pre></div>
<p>Now load the conos library, and the R package <code>conosPanel</code> for the example data <code>panel</code>:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(conos)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>panel <span class="ot">&lt;-</span> conosPanel<span class="sc">::</span>panel</span></code></pre></div>
<p>Next, use pagoda2 for pre-processing:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(pagoda2)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>panel.preprocessed <span class="ot">&lt;-</span> <span class="fu">lapply</span>(panel, basicP2proc, <span class="at">n.cores=</span><span class="dv">1</span>, <span class="at">min.cells.per.gene=</span><span class="dv">0</span>, <span class="at">n.odgenes=</span><span class="fl">2e3</span>, </span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>                             <span class="at">get.largevis=</span><span class="cn">FALSE</span>, <span class="at">make.geneknn=</span><span class="cn">FALSE</span>)</span></code></pre></div>
<pre><code>## creating space of type angular done
## adding data ... done
## building index ... done
## querying ... done
## creating space of type angular done
## adding data ... done
## building index ... done
## querying ... done
## creating space of type angular done
## adding data ... done
## building index ... done
## querying ... done
## creating space of type angular done
## adding data ... done
## building index ... done
## querying ... done</code></pre>
<p>Now align the datasets:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>con <span class="ot">&lt;-</span> Conos<span class="sc">$</span><span class="fu">new</span>(panel.preprocessed, <span class="at">n.cores=</span><span class="dv">1</span>)</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>con<span class="sc">$</span><span class="fu">buildGraph</span>(<span class="at">k=</span><span class="dv">15</span>, <span class="at">k.self=</span><span class="dv">5</span>, <span class="at">space=</span><span class="st">&#39;PCA&#39;</span>, <span class="at">ncomps=</span><span class="dv">30</span>)</span></code></pre></div>
<pre><code>## .............</code></pre>
<p>Next find the clusters, and create an embedding:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>con<span class="sc">$</span><span class="fu">findCommunities</span>()</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>con<span class="sc">$</span><span class="fu">embedGraph</span>(<span class="at">method=</span><span class="st">&quot;UMAP&quot;</span>)</span></code></pre></div>
<pre><code>## Estimating hitting distances: 04:00:47.
## Done.
## Estimating commute distances: 04:01:43.
## Hashing adjacency list: 04:01:43.
## Done.
## Estimating distances: 04:01:45.
## Done
## Done.
## All done!: 04:01:52.</code></pre>
<p>Now prepare the metadata (which can be any type of clustering of all the cells):</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>metadata <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">Cluster=</span>con<span class="sc">$</span>clusters<span class="sc">$</span>leiden<span class="sc">$</span>groups)</span></code></pre></div>
<p>Save data (set <code>exchange_dir</code> to your path):</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="do">## use current directory</span></span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>exchange_dir <span class="ot">&lt;-</span> <span class="st">&quot;.&quot;</span></span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>hdf5file <span class="ot">=</span> <span class="st">&quot;example.h5&quot;</span></span>
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a><span class="fu">saveConosForScanPy</span>(con, <span class="at">output.path=</span>exchange_dir, <span class="at">hdf5_filename=</span>hdf5file, <span class="at">verbose=</span><span class="cn">TRUE</span>)</span></code></pre></div>
<p>Users can then access the data saved to the HDF5 file, e.g.Â to access metadata, run:</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(rhdf5)</span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>metadata <span class="ot">=</span> <span class="fu">h5read</span>(<span class="fu">paste0</span>(exchange_dir, <span class="st">&quot;/example.h5&quot;</span>), <span class="st">&#39;metadata/metadata.df&#39;</span>)</span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(metadata, <span class="dv">4</span>)</span></code></pre></div>
<pre><code>##                                 CellId           Dataset
## 1 MantonBM1_HiSeq_1-TCTATTGGTCTCTCGT-1 MantonBM1_HiSeq_1
## 2 MantonBM1_HiSeq_1-GAATAAGTCACGCATA-1 MantonBM1_HiSeq_1
## 3 MantonBM1_HiSeq_1-ACACCGGTCTAACTTC-1 MantonBM1_HiSeq_1
## 4 MantonBM1_HiSeq_1-TCATTTGGTACGCTGC-1 MantonBM1_HiSeq_1</code></pre>
<p>All possible fields included in the output HDF5 file are:</p>
<ul>
<li><code>raw_count_matrix</code>: the sparse <code>dgCMatrix</code> of raw counts
<ul>
<li><code>raw_count_matrix/data</code>: the matrix entries</li>
<li><code>raw_count_matrix/shape</code>: the matrix dimensions</li>
<li><code>raw_count_matrix/indices</code>: 0-based vector of non-zero matrix entries</li>
<li><code>raw_count_matrix/indptr</code>: vector of pointers, one for each column (or row), to the initial (zero-based) index of elements in the column (or row)</li>
</ul></li>
<li><code>metadata/metadata.df</code>: the <code>data.frame</code> of metadata values</li>
<li><code>genes/genes.df</code>: the <code>data.frame</code> of genes</li>
<li><code>count_matrix</code>: the sparse <code>dgCMatrix</code> of normalized counts, if <code>cm.norm=TRUE</code>
<ul>
<li><code>count_matrix/data</code>: the matrix entries</li>
<li><code>count_matrix/shape</code>: the matrix dimensions</li>
<li><code>count_matrix/indices</code>: 0-based vector of non-zero matrix entries</li>
<li><code>count_matrix/indptr</code>: vector of pointers, one for each column (or row), to the initial (zero-based) index of elements in the column (or row)</li>
</ul></li>
<li><code>embedding/embedding.df</code>: the <code>data.frame</code> of the conos embedding, if <code>embedding=TRUE</code></li>
<li><code>pseudopca/pseudopca.df</code>: the <code>data.frame</code> of an emulated PCA by embedding the graph to a space with <code>n.dims</code> dimensions and save it as a pseudoPCA, if <code>pseudopca=TRUE</code></li>
<li><code>pca/pca.df</code>: the <code>data.frame</code> of the PCA of all the samples (not batch corrected), if <code>pca=TRUE</code></li>
<li><code>graph_connectivities</code>: the sparse <code>dgCMatrix</code> of graph connectivites, if <code>alignment.graph=TRUE</code>
<ul>
<li><code>graph_connectivities/data</code>: the matrix entries</li>
<li><code>graph_connectivities/shape</code>: the matrix dimensions</li>
<li><code>graph_connectivities/indices</code>: 0-based vector of non-zero matrix entries</li>
<li><code>graph_connectivities/indptr</code>: vector of pointers, one for each column (or row), to the initial (zero-based) index of elements in the column (or row)</li>
</ul></li>
<li><code>graph_distances</code>: the sparse <code>dgCMatrix</code> of graph distances, if <code>alignment.graph=TRUE</code>
<ul>
<li><code>graph_distances/data</code>: the matrix entries</li>
<li><code>graph_distances/shape</code>: the matrix dimensions</li>
<li><code>graph_distances/indices</code>: 0-based vector of non-zero matrix entries</li>
<li><code>graph_distances/indptr</code>: vector of pointers, one for each column (or row), to the initial (zero-based) index of elements in the column (or row)</li>
</ul></li>
</ul>
<p>In order to read in the <code>dcGMatrix</code> again, simply use the <code>Matrix</code> package as follows:</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(rhdf5)</span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(Matrix)</span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>rawcountMat <span class="ot">=</span> <span class="fu">h5read</span>(<span class="fu">paste0</span>(exchange_dir, <span class="st">&quot;/example.h5&quot;</span>), <span class="st">&#39;raw_count_matrix&#39;</span>)</span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>raw_count_matrix <span class="ot">=</span> <span class="fu">sparseMatrix</span>(<span class="at">x =</span> <span class="fu">as.numeric</span>(rawcountMat<span class="sc">$</span>data),  </span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">dims =</span> <span class="fu">as.numeric</span>(<span class="fu">c</span>(rawcountMat<span class="sc">$</span>shape[<span class="dv">1</span>], rawcountMat<span class="sc">$</span>shape[<span class="dv">2</span>])), </span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">p =</span> <span class="fu">as.numeric</span>(rawcountMat<span class="sc">$</span>indptr), </span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a>    <span class="at">i =</span> rawcountMat<span class="sc">$</span>indices, <span class="at">index1=</span><span class="cn">FALSE</span>)</span></code></pre></div>
<p><strong>Note:</strong> Please set <code>index1=FALSE</code> as the index vectors are 0-based. For more details, see the documentation for <a href="https://www.rdocumentation.org/packages/Matrix/versions/1.2-18/topics/sparseMatrix">Matrix::sparseMatrix()</a></p>
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