Commit 55bb9e74 authored by Jun Zhao's avatar Jun Zhao
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documentation update

parent 8ed543ee

docs/reference/STGmarkerFinder.html

+5 −9
Original line number Diff line number Diff line
@@ -130,9 +130,9 @@ Feature selection using stochastic gates. arXiv preprint arXiv:1810.04247, 2018. 
    

    </div>



    <pre class="usage"><span class='fu'>STGmarkerFinder</span>(<span class='no'>X</span>, <span class='no'>cell.idx</span>, <span class='no'>da.region.label</span>, <span class='kw'>da.regions.to.run</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,

  <span class='kw'>lambda</span> <span class='kw'>=</span> <span class='fl'>1.2</span>, <span class='kw'>n.runs</span> <span class='kw'>=</span> <span class='fl'>5</span>, <span class='kw'>return.model</span> <span class='kw'>=</span> <span class='no'>F</span>,

  <span class='kw'>python.use</span> <span class='kw'>=</span> <span class='st'>"/usr/bin/python"</span>, <span class='kw'>GPU</span> <span class='kw'>=</span> <span class='st'>""</span>)</pre>

    <pre class="usage"><span class='fu'>STGmarkerFinder</span>(<span class='no'>X</span>, <span class='no'>da.regions</span>, <span class='kw'>da.regions.to.run</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>lambda</span> <span class='kw'>=</span> <span class='fl'>1.2</span>,

  <span class='kw'>n.runs</span> <span class='kw'>=</span> <span class='fl'>5</span>, <span class='kw'>return.model</span> <span class='kw'>=</span> <span class='no'>F</span>, <span class='kw'>python.use</span> <span class='kw'>=</span> <span class='st'>"/usr/bin/python"</span>,

  <span class='kw'>GPU</span> <span class='kw'>=</span> <span class='st'>""</span>)</pre>

    

    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>

    <table class="ref-arguments">
@@ -143,12 +143,8 @@ Feature selection using stochastic gates. arXiv preprint arXiv:1810.04247, 2018. 
rownames must be gene names</p></td>

    </tr>

    <tr>

      <th>cell.idx</th>

      <td><p>result "da.cell.idx" from the output of function getDAcells</p></td>

    </tr>

    <tr>

      <th>da.region.label</th>

      <td><p>result "cluster.res" from the output of function getDAregion</p></td>

      <th>da.regions</th>

      <td><p>output from the function getDAregion()</p></td>

    </tr>

    <tr>

      <th>da.regions.to.run</th>

docs/reference/getDAcells.html

+44 −16
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@@ -38,6 +38,11 @@ 


<meta property="og:title" content="DA-seq Step 1 &amp; Step 2: select DA cells — getDAcells" />



<meta property="og:description" content="Step 1: compute a multiscale score measure for each cell of its k-nearest-neighborhood for

multiple values of k.

Step 2: train a logistic regression classifier based on the multiscale score measure and retain cells

that may reside in DA regions." />
@@ -120,12 +125,17 @@ 


    <div class="ref-description">

    

    <p>Step 1: compute a multiscale score measure for each cell of its k-nearest-neighborhood for

multiple values of k.

Step 2: train a logistic regression classifier based on the multiscale score measure and retain cells

that may reside in DA regions.</p>

    

    </div>



    <pre class="usage"><span class='fu'>getDAcells</span>(<span class='no'>X</span>, <span class='no'>cell.labels</span>, <span class='no'>labels.1</span>, <span class='no'>labels.2</span>, <span class='kw'>k.vector</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,

  <span class='kw'>k.folds</span> <span class='kw'>=</span> <span class='fl'>10</span>, <span class='kw'>n.runs</span> <span class='kw'>=</span> <span class='fl'>10</span>, <span class='kw'>es.patience</span> <span class='kw'>=</span> <span class='fl'>10</span>, <span class='kw'>k.smooth</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,

  <span class='kw'>save.knn</span> <span class='kw'>=</span> <span class='no'>F</span>, <span class='kw'>alpha</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>k.folds</span> <span class='kw'>=</span> <span class='fl'>10</span>, <span class='kw'>n.runs</span> <span class='kw'>=</span> <span class='fl'>10</span>,

  <span class='kw'>pred.thres</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.05</span>, <span class='fl'>0.95</span>), <span class='kw'>do.plot</span> <span class='kw'>=</span> <span class='no'>T</span>, <span class='kw'>plot.embedding</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,

  <span class='kw'>size</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>python.use</span> <span class='kw'>=</span> <span class='st'>"/usr/bin/python"</span>, <span class='kw'>GPU</span> <span class='kw'>=</span> <span class='st'>""</span>)</pre>

  <span class='kw'>size</span> <span class='kw'>=</span> <span class='fl'>0.5</span>)</pre>

    

    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>

    <table class="ref-arguments">
@@ -150,6 +160,10 @@ 
      <th>k.vector</th>

      <td><p>vector, k values to create the score vector</p></td>

    </tr>

    <tr>

      <th>alpha</th>

      <td><p>numeric, elasticnet mixing parameter passed to glmnet(), default 0 (Ridge)</p></td>

    </tr>

    <tr>

      <th>k.folds</th>

      <td><p>integer, number of data splits used in the neural network, default 10</p></td>
@@ -159,24 +173,38 @@ 
      <td><p>integer, number of times to run the neural network to get the predictions, default 10</p></td>

    </tr>

    <tr>

      <th>es.patience</th>

      <td><p>integer, patience parameter used in the EarlyStopping procedure, default 10</p></td>

      <th>pred.thres</th>

      <td><p>length-2 vector, top and bottom threshold on the predictions from the

logistic classification, default c(0.05,0.95)</p></td>

    </tr>

    <tr>

      <th>do.plot</th>

      <td><p>a logical value to indicate whether to return ggplot objects showing the results,

default True</p></td>

    </tr>

    <tr>

      <th>k.smooth</th>

      <td><p>integer, number of nearest neighbors used to smooth the prediction scores,

default 1<!-- % of cells} --></p>

<p>pred.threslength-2 vector, top and bottom threshold on the predictions from the

logistic classification, default c(0.05,0.95)</p>

<p>do.plota logical value to indicate whether to return ggplot objects showing the results,

default True</p>

<p>plot.embeddingsize N-by-2 matrix, 2D embedding for the cells</p>

<p>sizecell size to use in the plot, default 0.5</p>

<p>python.usecharacter string, the Python to use, default "/usr/bin/python"</p>

<p>GPUwhich GPU to use, default '', using CPU</p></td>

      <th>plot.embedding</th>

      <td><p>size N-by-2 matrix, 2D embedding for the cells</p></td>

    </tr>

    <tr>

      <th>size</th>

      <td><p>cell size to use in the plot, default 0.5</p></td>

    </tr>

    </table>

    

    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>



    <p>a list of results</p><dl'>

  <dt>da.ratio</dt><dd><p>score vector for each cell</p></dd>

  <dt>da.pred</dt><dd><p>(mean) prediction from the neural network</p></dd>

  <dt>da.up</dt><dd><p>index for DA cells more abundant in condition of labels.2</p></dd>

  <dt>da.down</dt><dd><p>index for DA cells more abundant in condition of labels.1</p></dd>

  <dt>pred.plot</dt><dd><p>ggplot object showing the predictions of logistic regression on plot.embedding</p></dd>

  <dt>da.cells.plot</dt><dd><p>ggplot object highlighting cells of da.cell.idx on plot.embedding</p></dd>



</dl>



    



  </div>

  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">

docs/reference/getDAregion.html

+20 −6
Original line number Diff line number Diff line
@@ -128,8 +128,10 @@ coherent DA regions.</p> 
    

    </div>



    <pre class="usage"><span class='fu'>getDAregion</span>(<span class='no'>X</span>, <span class='no'>da.cells</span>, <span class='kw'>resolution</span> <span class='kw'>=</span> <span class='fl'>0.05</span>, <span class='no'>cell.labels</span>, <span class='no'>labels.1</span>,

  <span class='no'>labels.2</span>, <span class='kw'>do.plot</span> <span class='kw'>=</span> <span class='no'>T</span>, <span class='kw'>plot.embedding</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>size</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='no'>...</span>)</pre>

    <pre class="usage"><span class='fu'>getDAregion</span>(<span class='no'>X</span>, <span class='no'>da.cells</span>, <span class='no'>cell.labels</span>, <span class='no'>labels.1</span>, <span class='no'>labels.2</span>,

  <span class='kw'>prune.SNN</span> <span class='kw'>=</span> <span class='fl'>1</span>/<span class='fl'>15</span>, <span class='kw'>resolution</span> <span class='kw'>=</span> <span class='fl'>0.05</span>, <span class='kw'>group.singletons</span> <span class='kw'>=</span> <span class='no'>F</span>,

  <span class='kw'>min.cell</span> <span class='kw'>=</span> <span class='fl'>10</span>, <span class='kw'>do.plot</span> <span class='kw'>=</span> <span class='no'>T</span>, <span class='kw'>plot.embedding</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>size</span> <span class='kw'>=</span> <span class='fl'>0.5</span>,

  <span class='no'>...</span>)</pre>

    

    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>

    <table class="ref-arguments">
@@ -142,10 +144,6 @@ coherent DA regions.</p> 
      <th>da.cells</th>

      <td><p>output from getDAcells() or updateDAcells()</p></td>

    </tr>

    <tr>

      <th>resolution</th>

      <td><p>parameter for Seurat function FindClusters(), default 0.05</p></td>

    </tr>

    <tr>

      <th>cell.labels</th>

      <td><p>size N vector, labels for each input cell</p></td>
@@ -158,6 +156,22 @@ coherent DA regions.</p> 
      <th>labels.2</th>

      <td><p>vector, label name(s) that represent condition 2</p></td>

    </tr>

    <tr>

      <th>prune.SNN</th>

      <td><p>parameter for Seurat function FindNeighbors(), default 1/15</p></td>

    </tr>

    <tr>

      <th>resolution</th>

      <td><p>parameter for Seurat function FindClusters(), default 0.05</p></td>

    </tr>

    <tr>

      <th>group.singletons</th>

      <td><p>parameter for Seurat function FindClusters(), default True</p></td>

    </tr>

    <tr>

      <th>min.cell</th>

      <td><p>integer, number of cells below which a DA region will be removed as outliers, default 10</p></td>

    </tr>

    <tr>

      <th>do.plot</th>

      <td><p>a logical value to indicate whether to return ggplot objects showing the results, default True</p></td>

docs/reference/index.html

+8 −2
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@@ -185,9 +185,9 @@ 
      </tr><tr>

        

        <td>

          <p><code><a href="getDAregion.old.html">getDAregion.old()</a></code> </p>

          <p><code><a href="plotCellLabel.html">plotCellLabel()</a></code> </p>

        </td>

        <td><p>DA-seq Step 3: get DA regions</p></td>

        <td><p>Plot cell labels</p></td>

      </tr><tr>

        

        <td>
@@ -196,6 +196,12 @@ 
        <td><p>Plot a score for each cell</p></td>

      </tr><tr>

        

        <td>

          <p><code><a href="plotDAsite.html">plotDAsite()</a></code> </p>

        </td>

        <td><p>Plot da site</p></td>

      </tr><tr>

        

        <td>

          <p><code><a href="runSTG.html">runSTG()</a></code> </p>

        </td>

docs/reference/plotCellLabel.html

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    <h1>Plot cell labels</h1>

    

    <div class="hidden name"><code>plotCellLabel.Rd</code></div>

    </div>



    <div class="ref-description">

    

    <p>Produce a ggplot object with cells on 2D embedding, colored by given labels of each cell.</p>

    

    </div>



    <pre class="usage"><span class='fu'>plotCellLabel</span>(<span class='no'>X</span>, <span class='no'>label</span>, <span class='kw'>cell.col</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>size</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>do.label</span> <span class='kw'>=</span> <span class='no'>T</span>)</pre>

    

    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>

    <table class="ref-arguments">

    <colgroup><col class="name" /><col class="desc" /></colgroup>

    <tr>

      <th>X</th>

      <td><p>matrix, 2D embedding of each cell for the plot</p></td>

    </tr>

    <tr>

      <th>label</th>

      <td><p>vector, label for each cell</p></td>

    </tr>

    <tr>

      <th>cell.col</th>

      <td><p>string vector, color bar to use for cell labels, default ggplot default</p></td>

    </tr>

    <tr>

      <th>size</th>

      <td><p>numeric, dot size for each cell, default 0.5</p></td>

    </tr>

    <tr>

      <th>do.label</th>

      <td><p>a logical value indicating whether to add text to mark each cell label</p></td>

    </tr>

    </table>

    

    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>



    <p>a ggplot object</p>

    



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