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<!DOCTYPE html>
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<meta name="author" content="Wei Vivian Li" />
<meta name="date" content="2018-08-15" />
<title>scImpute Updates</title>
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<body>
<h1 class="title toc-ignore">scImpute Updates</h1>
<h4 class="author"><em>Wei Vivian Li</em></h4>
<h4 class="date"><em>2018-08-15</em></h4>
<div id="updates" class="section level2">
<h2>Updates</h2>
<blockquote>
<p>2018/08/15:</p>
</blockquote>
<ul>
<li>Version 0.0.9 is released!</li>
<li>More robust implementation of dimension reduction.</li>
<li>Faster calculation of cell similarity.</li>
</ul>
<blockquote>
<p>2018/06/27:</p>
</blockquote>
<ul>
<li>Version 0.0.8 is released!</li>
<li>Faster implementation of dimension reduction.</li>
</ul>
<blockquote>
<p>2018/06/08:</p>
</blockquote>
<ul>
<li>Version 0.0.7 is released!</li>
<li>New option for application on TPM values.</li>
</ul>
<blockquote>
<p>2018/03/16:</p>
</blockquote>
<ul>
<li>Version 0.0.6 is released!</li>
<li>The scImpute method is published at <a href="https://www.nature.com/articles/s41467-018-03405-7"><em>Nature Communications</em></a>.</li>
<li>scImpute now supports input and output in the format of R objects (.rds).</li>
</ul>
<blockquote>
<p>2018/01/12:</p>
</blockquote>
<ul>
<li>Version 0.0.5 is released!</li>
<li>It is now possible to apply scImpute on just one cell population by setting <code>Kcluster = 1</code>.</li>
</ul>
<blockquote>
<p>2017/10/27:</p>
</blockquote>
<ul>
<li>Version 0.0.4 is released!</li>
<li>scImpute now supports multi-code parallelism.</li>
</ul>
<blockquote>
<p>2017/10/22:</p>
</blockquote>
<ul>
<li>Version 0.0.3 is released!</li>
<li>Estimation of dropout probabilities is more accurate.</li>
<li>Imputation step is more robust.</li>
<li><code>scimpute()</code> incorporates a new parameter <code>Kcluster</code> to specify the number of cell subpopulations.</li>
<li><code>scImpute</code> is now able to detect outlier cells.</li>
</ul>
<blockquote>
<p>2017/07/01:</p>
</blockquote>
<ul>
<li>Version 0.0.2 is released!</li>
<li>This version speeds up the first step in <code>scImpute</code> and program now completes in a few seconds when applied to a dataset with 10,000 genes and 100 cells (using single core).</li>
</ul>
</div>
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