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<title>BioInfoSummer workshop: Single Cell RNA-seq Analysis</title>
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<h1 class="title toc-ignore">BioInfoSummer workshop: Single Cell RNA-seq Analysis</h1>
<h4 class="author">Sydney Precision Bioinformatics Group</h4>
<h4 class="date">04 December 2019</h4>
</div>
<div id="single-cell-plus" class="section level1">
<h1>Single Cell Plus</h1>
<p>This workshop is funded by “Single Cell Plus”, a research initiative between Hong Kong University and the University of Sydney.</p>
</div>
<div id="slides" class="section level1">
<h1>Slides</h1>
<p>You can find the workshop slides <a href="slides.pdf">here</a>.</p>
</div>
<div id="aim-of-this-workshop" class="section level1">
<h1>Aim of this workshop</h1>
<p>In this workshop we will focus on two mouse liver datasets to illustrate three critical topics in single-cell RNA-Seq analysis.</p>
<ol style="list-style-type: decimal">
<li>Quality control of sc-RNASeq data,</li>
<li>The <code>scMerge</code> package for integrating multiple sc-RNASeq data,</li>
<li>Some downstream analyses after <code>scMerge</code>.</li>
</ol>
<p>At the end of this workshop you should have some basic understanding of sc-RNASeq data and some key computational and statistical challenges of this type of data.</p>
</div>
<div id="prerequisites" class="section level1">
<h1>Prerequisites</h1>
<p>Ideally, you should be familiar with <code>R</code> and the Bioconductor. However, since this is not a programming workshop, you can still pick up important practical skills by running the codes we provided.</p>
<div id="running-workshop-on-google-cloud" class="section level2">
<h2>Running workshop on Google Cloud</h2>
<p>Your instructor will provide you with the links to a Google Cloud server, username and the password. Once you log in, you will have access to RStudio via a webpage.</p>
<p>Type the following code into your console and you are good to go!</p>
<pre><code>source("/home/user_setup.R")</code></pre>
</div>
<div id="local" class="section level2">
<h2>Running workshop locally</h2>
<ol style="list-style-type: decimal">
<li>Please download and install:</li>
</ol>
<ul>
<li>Latest <code>R</code> from <a href="https://cloud.r-project.org/" class="uri">https://cloud.r-project.org/</a></li>
<li>Latest <code>RStudio</code> from <a href="https://www.rstudio.com/products/rstudio/download/" class="uri">https://www.rstudio.com/products/rstudio/download/</a></li>
</ul>
<ol style="list-style-type: decimal">
<li><p>In your RStudio console pane, please install all the necessary packages by running <a href="https://raw.githubusercontent.com/SydneyBioX/BIS2019_SC/master/install.R">this script</a>. This will take a while.</p></li>
<li><p>Download all the the workshop materials from <a href="https://github.com/SydneyBioX/BIS2019_SC/archive/master.zip">this zip file</a> and unzip it on your Desktop. You should have a folder with the name <code>/desktop/BIS2019_SC-master</code> created.</p></li>
<li><p>Download the data from <a href="https://storage.googleapis.com/scp_data/data.zip">this zip file</a> and unzip this file under <code>/desktop/BIS2019_SC-master</code>, so that you should have a folder created as <code>/desktop/BIS2019_SC-master/data</code>.</p></li>
<li><p>Open up <code>/desktop/SC_CPC_workshop-master/BIS2019_SC.Rproj</code> and you should be able to run every <code>.Rmd</code> file in the workshop now.</p></li>
</ol>
</div>
</div>
<div id="clipboard" class="section level1">
<h1>Clipboard</h1>
<p>In some circumstances, we might need to share codes beyond the existing materials. Please click <a href="https://docs.google.com/document/d/136YffzxjkUM8RIGsnjD_yOkCTWw4Ijdwjy4_58nC-zc/edit?usp=sharing">here</a> to access these codes.</p>
</div>
<div id="references" class="section level1">
<h1>References</h1>
<p><strong>Methodologies</strong></p>
<ol style="list-style-type: decimal">
<li><p>Cao Y., Lin Y., Ormerod, J.T., Yang, P., Yang, J., and Lo, K.K. (2019). scDC: Single cell differential composition analysis. Accepted by InCoB 2019.</p></li>
<li><p>Geddes, T.A., Kim, T., Nan, L., Burchfield, J.G., Yang, J.Y.H., Tao, D., and Yang, P. (2019). Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis. BioRxiv Accepted by InCoB 2019.</p></li>
<li><p>Kim, T., Lo, K.K., Geddes, T.A., Kim H., Yang, J., and Yang, P. (2019) Accepted by InCoB 2019. scReClassify: post hoc cell type classification of single-cell RNA-seq data.</p></li>
<li><p>Kim, T., Chen, I.R., Lin, Y., Wang, A.Y.-Y., Yang, J.Y.H., and Yang, P. (2019). <a href="https://doi.org/10.1093/bib/bby076">Impact of similarity metrics on single-cell RNA-seq data clustering</a>. Brief. Bioinform.</p></li>
<li><p>Lin, Y., Ghazanfar, S., Wang, K.Y.X., Gagnon-Bartsch, J.A., Lo, K.K., Su, X., Han, Z.G., Ormerod, J.T., Speed, T.P., Yang, P., and Yang, J. (2019). <a href="http://www.pnas.org/lookup/doi/10.1073/pnas.1820006116">scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets</a>. Proc. Natl. Acad. Sci. U. S. A.</p></li>
<li><p>Lin, Y., Ghazanfar, S., Strbenac, D., Wang, A., Patrick, E., Lin, D., Speed, T., Yang, J., and Yang, P. (2019). Evaluating stably expressed genes in single cells. GigaScience.</p></li>
<li><p>Lin, Y., Cao Y., Kim H., Salim A., Speed T., Lin D., Yang P., Yang, J. (2019) scClassify: hierarchical classification of cells. BioRxiv</p></li>
</ol>
<p><strong>Data</strong></p>
<ol style="list-style-type: decimal">
<li><p>Xianbin Su, Yi Shi, Xin Zou, Zhao-Ning Lu, Gangcai Xie, Jean Y. H. Yang, Chong-Chao Wu, Xiao-Fang Cui, Kun-Yan He, Qing Luo, Yu-Lan Qu, Na Wang, Lan Wang, Ze-Guang Han. (2017) <a href="https://doi.org/10.1186/s12864-017-4342-x">Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development</a>, BMC Genomics.</p></li>
<li><p>Li Yang, Wei‐Hua Wang, Wei‐Lin Qi, Zhen Guo, Erfei Bi, Cheng‐Ran Xu. (2017) <a href="https://doi.org/10.1002/hep.29353">A single‐cell transcriptomic analysis reveals precise pathways and regulatory mechanisms underlying hepatoblast differentiation</a>, Hepatology.</p></li>
</ol>
</div>
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