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committedJun 1, 2020
v1.0.0
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‎DESCRIPTION

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Package: scLink
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Title: Inferring functional gene co-expression networks from single cell gene expression data
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Version: 0.0.1
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Version: 1.0.0
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Authors@R:
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person(given = "Wei Vivian",
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family = "Li",
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role = c("aut", "cre"),
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email = "vivian.li@rutgers.edu",
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comment = c(ORCID = "0000-0002-2087-2709"))
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Description: The scLink method uses statistical network modeling to understand the co-expression relationships among genes and to construct functional gene networks from single-cell gene expression data.
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Description: The scLink method uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data.
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License: GPL-2
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Depends: parallel
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Imports: QUIC

‎README.md

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================
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Wei Vivian Li
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Rutgers Department of Biostatistics and Epidemiology
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<!-- README.md is generated from README.Rmd. Please edit that file -->
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Latest News
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-----------
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> 2020/06/01: Version 1.0.0 released!
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> 2020/03/05: Version 0.0.9 released!
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- `sclink_cor` for calculating the co-expression matrix by scLink
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- `sclink_net` for constructing the gene co-expression network by scLink
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For detailed usage, please refer to the package [manual](https://github.com/Vivianstats/scLink/blob/master/inst/docs/).
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For detailed usage, please refer to the package [manual](https://github.com/Vivianstats/scLink/blob/master/inst/docs/) or [vignette](https://github.com/Vivianstats/scLink/blob/master/inst/vignette/).
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<meta name="author" content="Wei Vivian Li, Rutgers Department of Biostatistics and Epidemiology" />
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<meta name="date" content="2020-06-01" />
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<h1 class="title toc-ignore">Introduction to scLink</h1>
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<h4 class="author">Wei Vivian Li, Rutgers Department of Biostatistics and Epidemiology</h4>
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<h4 class="date">2020-06-01</h4>
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<p>A system-level understanding of the regulation and coordination mechanisms of gene expression is essential to understanding the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell-type-specific manner. Here we introduce the <code>scLink</code> package, which uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data.</p>
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<p>Here we demonstrate the functionality of <code>scLink</code> using the example data stored in the package.</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(scLink)</code></pre></div>
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<pre><code>## Loading required package: parallel</code></pre>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">count =<span class="st"> </span><span class="kw">readRDS</span>(<span class="kw">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;example.rds&quot;</span>, <span class="dt">package =</span> <span class="st">&quot;scLink&quot;</span>))
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genes =<span class="st"> </span><span class="kw">readRDS</span>(<span class="kw">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;genes.rds&quot;</span>, <span class="dt">package =</span> <span class="st">&quot;scLink&quot;</span>))</code></pre></div>
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<p>The example raw count matrix <code>count</code> has 793 <strong>rows</strong> representing different cells and 23,341 <strong>columns</strong> representing different genes. <code>genes</code> is a character vector of 500 genes of interest.</p>
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<div id="sclink_norm" class="section level2">
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<h2><code>sclink_norm</code></h2>
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<p>We use the function <code>sclink_norm</code> to process single cell read count for application of the sclink method. The code below will normalize the read count matrix with a library size of <span class="math inline">\(10^6\)</span> and only keep the 500 genes in <code>genes</code> for downstream analysis. Note that the normalized count matrix <code>count.norm</code> is on the <span class="math inline">\(\log_{10}\)</span> scale.</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">count.norm =<span class="st"> </span><span class="kw">sclink_norm</span>(count, <span class="dt">scale.factor =</span> <span class="fl">1e6</span>, <span class="dt">filter.genes =</span> <span class="ot">FALSE</span>, <span class="dt">gene.names =</span> genes)</code></pre></div>
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<p>If users do not have a particular gene list for network inference, they can set <code>filter.genes=TRUE</code> to filter for the top <span class="math inline">\(n\)</span> genes with largest average expression values. For example:</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">count.norm =<span class="st"> </span><span class="kw">sclink_norm</span>(count, <span class="dt">scale.factor =</span> <span class="fl">1e6</span>, <span class="dt">filter.genes =</span> <span class="ot">TRUE</span>, <span class="dt">n =</span> <span class="dv">500</span>)</code></pre></div>
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</div>
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<div id="sclink_net" class="section level2">
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<h2><code>sclink_net</code></h2>
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<p>After the pre-processing step, we use the function <code>sclink_net</code> to calculate the robust correlation matrix and identifed sparse co-expression network of scLink. <code>expr</code> is the normalized count matrix output by <code>sclink_norm</code> or supplied by the users. <code>lda</code> is the candidate regularization parameters used in scLink’s graphical model. The users can set <code>ncores</code> to take advantage of parallel computation.</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">networks =<span class="st"> </span><span class="kw">sclink_net</span>(<span class="dt">expr =</span> count.norm, <span class="dt">ncores =</span> <span class="dv">1</span>, <span class="dt">lda =</span> <span class="kw">seq</span>(<span class="fl">0.5</span>, <span class="fl">0.1</span>, <span class="op">-</span><span class="fl">0.05</span>))</code></pre></div>
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<p><code>sclink_net</code> returns a list of results. The scLink’s robust correlation matrix can be retrieved from the <code>cor</code> element:</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">networks<span class="op">$</span>cor[<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>]</code></pre></div>
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<pre><code>## Rn45s Eef1a1 Malat1
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## Rn45s 1.00000000 -0.27604002 -0.08561265
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## Eef1a1 -0.27604002 1.00000000 -0.05138179
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## Malat1 -0.08561265 -0.05138179 1.00000000</code></pre>
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<p>The gene co-expression networks and summary statistics can be retrieved from the <code>summary</code> element, which is a list with the same length as <code>lda</code>: each element corresponds to one regularization parameter.</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">net1 =<span class="st"> </span>networks<span class="op">$</span>summary[[<span class="dv">1</span>]]
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<span class="kw">names</span>(net1)</code></pre></div>
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<pre><code>## [1] &quot;adj&quot; &quot;Sigma&quot; &quot;nedge&quot; &quot;bic&quot; &quot;lambda&quot;</code></pre>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">### adjacency matrix
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net1<span class="op">$</span>adj[<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>]</code></pre></div>
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<pre><code>## Rn45s Eef1a1 Malat1
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## Rn45s 1 0 0
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## Eef1a1 0 1 0
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## Malat1 0 0 1</code></pre>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">### concentration matrix
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net1<span class="op">$</span>Sigma[<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>]</code></pre></div>
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<pre><code>## Rn45s Eef1a1 Malat1
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## Rn45s 1.696584 0.000000 0.000000
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## Eef1a1 0.000000 1.665809 0.000000
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## Malat1 0.000000 0.000000 1.578271</code></pre>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">### BIC
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net1<span class="op">$</span>bic</code></pre></div>
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<pre><code>## [1] 1255450</code></pre>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">### number of edges
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net1<span class="op">$</span>nedge</code></pre></div>
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<pre><code>## [1] 127</code></pre>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">### regularization parameter lambda
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net1<span class="op">$</span>lambda</code></pre></div>
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<pre><code>## [1] 0.5</code></pre>
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</div>
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<div id="sclink_cor" class="section level2">
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<h2><code>sclink_cor</code></h2>
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<p>Since it is very difficult to infer co-expression relationships for lowly expressed genes in single-cell data, we suggest the filtering step as used in <code>sclink_norm</code> to select genes. This also reduces the computational burden. However, if the users would like to infer gene networks for a large gene list (e.g., <span class="math inline">\(&gt; 5000\)</span> genes), we suggest that the users first use <code>sclink_cor</code> to investigate the correlation structures among these genes.</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">corr =<span class="st"> </span><span class="kw">sclink_cor</span>(<span class="dt">expr =</span> count.norm, <span class="dt">ncores =</span> <span class="dv">1</span>)</code></pre></div>
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<p>If the correlation matrix suggests obvious gene modules, then the users can apply <code>sclink_net</code> separately on these modules to reduce computation time and increase accuracy.</p>
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</div>
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