/
README.html
846 lines (833 loc) · 225 KB
/
README.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
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<body>
<h1 id="glmsparsenet">glmSparseNet</h1>
<ul>
<li><a href="#overview">Overview</a></li>
<li><a href="#citation">Citation</a></li>
<li><a href="#instalation">Instalation</a></li>
<li><a href="#details">Details</a>
<ul>
<li><a href="#function-definition">Function definition</a></li>
</ul></li>
<li><a href="#example-for-survival-analysis-using-rna-seq-data">Example
for survival analysis using RNA-seq data</a></li>
<li><a href="#visualization-and-analytical-tools">Visualization and
Analytical tools</a>
<ul>
<li><a href="#survival-curves-with-separate2groupscox">Survival curves
with <code>separate2groupsCox</code></a></li>
</ul></li>
</ul>
<!-- README.md and README.html are generated from README.Rmd. Please edit that file -->
<blockquote>
<p>Elastic-Net models with additional regularization based on network
centrality metrics</p>
</blockquote>
<p><a href="https://travis-ci.org/sysbiomed/glmSparseNet"><img src="data:image/svg+xml;base64,ZmlsZSBub3QgZm91bmQ=" alt="Travis-CI Build Status" /></a> <a href="https://codecov.io/github/sysbiomed/glmSparseNet?branch=master"><img src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxMTIiIGhlaWdodD0iMjAiPgogICAgPGxpbmVhckdyYWRpZW50IGlkPSJiIiB4Mj0iMCIgeTI9IjEwMCUiPgogICAgICAgIDxzdG9wIG9mZnNldD0iMCIgc3RvcC1jb2xvcj0iI2JiYiIgc3RvcC1vcGFjaXR5PSIuMSIgLz4KICAgICAgICA8c3RvcCBvZmZzZXQ9IjEiIHN0b3Atb3BhY2l0eT0iLjEiIC8+CiAgICA8L2xpbmVhckdyYWRpZW50PgogICAgPG1hc2sgaWQ9ImEiPgogICAgICAgIDxyZWN0IHdpZHRoPSIxMTIiIGhlaWdodD0iMjAiIHJ4PSIzIiBmaWxsPSIjZmZmIiAvPgogICAgPC9tYXNrPgogICAgPGcgbWFzaz0idXJsKCNhKSI+CiAgICAgICAgPHBhdGggZmlsbD0iIzU1NSIgZD0iTTAgMGg3M3YyMEgweiIgLz4KICAgICAgICA8cGF0aCBmaWxsPSIjZTA1ZDQ0IiBkPSJNNzMgMGgzOXYyMEg3M3oiIC8+CiAgICAgICAgPHBhdGggZmlsbD0idXJsKCNiKSIgZD0iTTAgMGgxMTJ2MjBIMHoiIC8+CiAgICA8L2c+CiAgICA8ZyBmaWxsPSIjZmZmIiB0ZXh0LWFuY2hvcj0ibWlkZGxlIiBmb250LWZhbWlseT0iRGVqYVZ1IFNhbnMsVmVyZGFuYSxHZW5ldmEsc2Fucy1zZXJpZiIgZm9udC1zaXplPSIxMSI+CiAgICAgICAgPHRleHQgeD0iNDYiIHk9IjE1IiBmaWxsPSIjMDEwMTAxIiBmaWxsLW9wYWNpdHk9Ii4zIj5jb2RlY292PC90ZXh0PgogICAgICAgIDx0ZXh0IHg9IjQ2IiB5PSIxNCI+Y29kZWNvdjwvdGV4dD4KICAgICAgICA8dGV4dCB4PSI5MyIgeT0iMTUiIGZpbGw9IiMwMTAxMDEiIGZpbGwtb3BhY2l0eT0iLjMiPjM5JTwvdGV4dD4KICAgICAgICA8dGV4dCB4PSI5MyIgeT0iMTQiPjM5JTwvdGV4dD4KICAgIDwvZz4KICAgIDxzdmcgdmlld0JveD0iMTIwIC04IDYwIDYwIj4KICAgICAgICA8cGF0aCBkPSJNMjMuMDEzIDBDMTAuMzMzLjAwOS4wMSAxMC4yMiAwIDIyLjc2MnYuMDU4bDMuOTE0IDIuMjc1LjA1My0uMDM2YTExLjI5MSAxMS4yOTEgMCAwIDEgOC4zNTItMS43NjcgMTAuOTExIDEwLjkxMSAwIDAgMSA1LjUgMi43MjZsLjY3My42MjQuMzgtLjgyOGMuMzY4LS44MDIuNzkzLTEuNTU2IDEuMjY0LTIuMjQuMTktLjI3Ni4zOTgtLjU1NC42MzctLjg1MWwuMzkzLS40OS0uNDg0LS40MDRhMTYuMDggMTYuMDggMCAwIDAtNy40NTMtMy40NjYgMTYuNDgyIDE2LjQ4MiAwIDAgMC03LjcwNS40NDlDNy4zODYgMTAuNjgzIDE0LjU2IDUuMDE2IDIzLjAzIDUuMDFjNC43NzkgMCA5LjI3MiAxLjg0IDEyLjY1MSA1LjE4IDIuNDEgMi4zODIgNC4wNjkgNS4zNSA0LjgwNyA4LjU5MWExNi41MyAxNi41MyAwIDAgMC00Ljc5Mi0uNzIzbC0uMjkyLS4wMDJhMTYuNzA3IDE2LjcwNyAwIDAgMC0xLjkwMi4xNGwtLjA4LjAxMmMtLjI4LjAzNy0uNTI0LjA3NC0uNzQ4LjExNS0uMTEuMDE5LS4yMTguMDQxLS4zMjcuMDYzLS4yNTcuMDUyLS41MS4xMDgtLjc1LjE2OWwtLjI2NS4wNjdhMTYuMzkgMTYuMzkgMCAwIDAtLjkyNi4yNzZsLS4wNTYuMDE4Yy0uNjgyLjIzLTEuMzYuNTExLTIuMDE2LjgzOGwtLjA1Mi4wMjZjLS4yOS4xNDUtLjU4NC4zMDUtLjg5OS40OWwtLjA2OS4wNGExNS41OTYgMTUuNTk2IDAgMCAwLTQuMDYxIDMuNDY2bC0uMTQ1LjE3NWMtLjI5LjM2LS41MjEuNjY2LS43MjMuOTYtLjE3LjI0Ny0uMzQuNTEzLS41NTIuODY0bC0uMTE2LjE5OWMtLjE3LjI5Mi0uMzIuNTctLjQ0OS44MjRsLS4wMy4wNTdhMTYuMTE2IDE2LjExNiAwIDAgMC0uODQzIDIuMDI5bC0uMDM0LjEwMmExNS42NSAxNS42NSAwIDAgMC0uNzg2IDUuMTc0bC4wMDMuMjE0YTIxLjUyMyAyMS41MjMgMCAwIDAgLjA0Ljc1NGMuMDA5LjExOS4wMi4yMzcuMDMyLjM1NS4wMTQuMTQ1LjAzMi4yOS4wNDkuNDMybC4wMS4wOGMuMDEuMDY3LjAxNy4xMzMuMDI2LjE5Ny4wMzQuMjQyLjA3NC40OC4xMTkuNzIuNDYzIDIuNDE5IDEuNjIgNC44MzYgMy4zNDUgNi45OWwuMDc4LjA5OC4wOC0uMDk1Yy42ODgtLjgxIDIuMzk1LTMuMzggMi41MzktNC45MjJsLjAwMy0uMDI5LS4wMTQtLjAyNWExMC43MjcgMTAuNzI3IDAgMCAxLTEuMjI2LTQuOTU2YzAtNS43NiA0LjU0NS0xMC41NDQgMTAuMzQzLTEwLjg5bC4zODEtLjAxNGExMS40MDMgMTEuNDAzIDAgMCAxIDYuNjUxIDEuOTU3bC4wNTQuMDM2IDMuODYyLTIuMjM3LjA1LS4wM3YtLjA1NmMuMDA2LTYuMDgtMi4zODQtMTEuNzkzLTYuNzI5LTE2LjA4OUMzNC45MzIgMi4zNjEgMjkuMTYgMCAyMy4wMTMgMCIgZmlsbD0iI0YwMUY3QSIgZmlsbC1ydWxlPSJldmVub2RkIi8+CiAgICA8L3N2Zz4KPC9zdmc+" alt="Coverage status" /></a></p>
<h2 id="overview">Overview</h2>
<p><code>glmSparseNet</code> is a R package that generalizes sparse
regression models when the features <em>(e.g. genes)</em> have a graph
structure <em>(e.g. protein-protein interactions)</em>, by including
network-based regularizers. <code>glmSparseNet</code> uses the
<code>glmnet</code> R-package, by including centrality measures of the
network as penalty weights in the regularization. The current version
implements regularization based on node degree, i.e. the strength and/or
number of its associated edges, either by promoting hubs in the solution
or orphan genes in the solution. All the <code>glmnet</code>
distribution families are supported, namely <em>“gaussian”</em>,
<em>“poisson”</em>, <em>“binomial”</em>, <em>“multinomial”</em>,
<em>“cox”</em>, and <em>“mgaussian”</em>.</p>
<p>It adds two new main functions called <code>glmSparseNet</code> and
<code>cv.glmSparseNet</code> that extend both model inference and model
selection via cross-validation with network-based regularization. These
functions are very flexible and allow to transform the penalty weights
after the centrality metric is calculated, thus allowing to change how
it affects the regularization. To facilitate users, we made available a
function that will penalize low connected nodes in the network -
<code>glmHub</code> or <code>glmDegree</code> - and another that will
penalize hubs - <code>glmOrphan</code>.</p>
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" alt="Overview of the R-Package pipeline" /></span></p>
<p>Below, we provide one example for survival analysis using
transcriptomic data from the TCGA Adrenocortical Carcinoma project. More
information and Rmd files are available in the vignettes folder where
more extensive and complete examples are provided for logistic regresson
and Cox’s regression for different types of cancer data.</p>
<h2 id="citation">Citation</h2>
<p>Veríssimo, A., Carrasquinha E., Lopes, M.B., Oliveira, A.L., Sagot,
M.-F. & Vinga, S. (2018), Sparse network-based regularization for
the analysis of patientomics high-dimensional survival data. bioRxiv
403402; doi: <a href="https://doi.org/10.1101/403402">https://doi.org/10.1101/403402</a></p>
<p>Veríssimo, A., Oliveira, A.L., Sagot, M.-F., & Vinga, S. (2016).
DegreeCox – a network-based regularization method for survival analysis.
BMC Bioinformatics. 17(16): 449. <a href="https://doi.org/10.1186/s12859-016-1310-4">https://doi.org/10.1186/s12859-016-1310-4</a></p>
<p>This package was developed by André Veríssimo, Eunice Carrasquinha,
Marta B. Lopes and Susana Vinga under the project SOUND, funded from the
European Union Horizon 2020 research and innovation program under grant
agreement No. 633974.</p>
<h2 id="instalation">Instalation</h2>
<p>Bioconductor is necessary for the installation of this package.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">require</span>(<span class="st">"BiocManager"</span>)) {</span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a> <span class="fu">install.packages</span>(<span class="st">"BiocManager"</span>)</span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a>}</span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a>BiocManager<span class="sc">::</span><span class="fu">install</span>(<span class="st">"glmSparseNet"</span>)</span></code></pre></div>
<h2 id="details">Details</h2>
<p>This package extends the <code>glmnet</code> r-package with
network-based regularization based on features relations. This network
can be calculated from the data itself or using external networks to
enrich the model.</p>
<p>There are 2 methods available to use data-dependant methods to
generate the network:</p>
<ol style="list-style-type: decimal">
<li>Correlation matrix with cutoff;</li>
<li>Covariance matrix with cutoff;</li>
</ol>
<p>Alternatively, the network can be passed as an adjancency matrix or
an already calculate metric for each node.</p>
<h3 id="function-definition">Function definition</h3>
<p>The main functions from this packages are the
<code>glmSparseNet</code> and <code>cv.glmSparseNet</code> and the
arguments for the functions are defined as:</p>
<ul>
<li><code>xdata</code>: A MultiAssayExperiment object or an input matrix
of dimension <code>Observations x Features</code></li>
<li><code>ydata</code>: Response object that can take different forms
depending on the model family that is used</li>
<li><code>family</code>: Model type that can take: <em>“gaussian”</em>,
<em>“poisson”</em>, <em>“binomial”</em>, <em>“multinomial”</em>,
<em>“cox”</em>, and <em>“mgaussian”</em></li>
<li><code>network</code>: Network to use in penalization, it can take as
input: “correlation”, “covariance”, a matrix object with p.vars x p.vars
representing the network, a weighted vector of penalties</li>
<li><code>experiment.name</code>: Optional parameter used with a
“MultiAssayExperiment” object as input</li>
<li><code>network.options</code>: Optional parameter defining the
options to process the network, such as:
<ul>
<li><code>cutoff</code>: A real number to use to remove edges from the
network</li>
<li><code>minDegree</code>: Minimum value that the weight should have,
this is useful as when the weight is 0, there is no regularization on
that feature, which may lead to convergence problems</li>
<li><code>transFun</code>: Transformation function to the vector of
penalty weights after these are calculated from the network</li>
</ul></li>
</ul>
<p><em>note:</em> These functions can take any additional arguments that
<code>glmnet</code> or <code>cv.glmnet</code> accept (e.g. number of
folds in cross validation)</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="fu">cv.glmSparseNet</span>(</span>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a> xdata,</span>
<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a> ydata,</span>
<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a> <span class="at">family =</span> <span class="st">"cox"</span>,</span>
<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a> <span class="at">network =</span> <span class="st">"correlation"</span>,</span>
<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a> <span class="at">options =</span> <span class="fu">networkOptions</span>(</span>
<span id="cb2-7"><a href="#cb2-7" tabindex="-1"></a> <span class="at">cutoff =</span> .<span class="dv">6</span>,</span>
<span id="cb2-8"><a href="#cb2-8" tabindex="-1"></a> <span class="at">minDegree =</span> <span class="fl">0.2</span></span>
<span id="cb2-9"><a href="#cb2-9" tabindex="-1"></a> )</span>
<span id="cb2-10"><a href="#cb2-10" tabindex="-1"></a>)</span></code></pre></div>
<h2 id="example-for-survival-analysis-using-rna-seq-data">Example for
survival analysis using RNA-seq data</h2>
<p>This example uses an adrenal cancer dataset using the correlation to
calculate the network and cross-validation to find the optimal model.
The network itself if filtered using a cutoff value of 0.6, i.e. all
edges that have a correlation between the two features <em>(genes)</em>
below the cutoff value are discarded.</p>
<p>The data was retrieved from TCGA database and the Adrenocortical
Carcinoma project with 92 patients and a reduced RNASeq data. See
Bioconductor package <code>MultiAssayExperiment</code> for more
information on the <code>miniACC</code> dataset.</p>
<p>To run the following examples, the next libraries are also
needed:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="fu">library</span>(futile.logger)</span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a><span class="fu">library</span>(reshape2)</span>
<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a><span class="fu">library</span>(MultiAssayExperiment)</span>
<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a><span class="fu">library</span>(survival)</span>
<span id="cb3-7"><a href="#cb3-7" tabindex="-1"></a><span class="fu">library</span>(glmnet)</span>
<span id="cb3-8"><a href="#cb3-8" tabindex="-1"></a><span class="fu">library</span>(glmSparseNet)</span></code></pre></div>
<p>There is some pre-processing needed to remove patients with invalid
follow-up date or death date:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="co"># load data</span></span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="fu">data</span>(<span class="st">"miniACC"</span>, <span class="at">package =</span> <span class="st">"MultiAssayExperiment"</span>)</span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a>xdata <span class="ot"><-</span> miniACC</span>
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a></span>
<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a><span class="co"># build valid data with days of last follow up or to event</span></span>
<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a>eventIx <span class="ot"><-</span> <span class="fu">which</span>(<span class="sc">!</span><span class="fu">is.na</span>(xdata<span class="sc">$</span>days_to_death))</span>
<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a>censIx <span class="ot"><-</span> <span class="fu">which</span>(<span class="sc">!</span><span class="fu">is.na</span>(xdata<span class="sc">$</span>days_to_last_followup))</span>
<span id="cb4-8"><a href="#cb4-8" tabindex="-1"></a>survEventTime <span class="ot"><-</span> <span class="fu">array</span>(<span class="cn">NA</span>, <span class="fu">nrow</span>(<span class="fu">colData</span>(xdata)))</span>
<span id="cb4-9"><a href="#cb4-9" tabindex="-1"></a>survEventTime[eventIx] <span class="ot"><-</span> xdata<span class="sc">$</span>days_to_death[eventIx]</span>
<span id="cb4-10"><a href="#cb4-10" tabindex="-1"></a>survEventTime[censIx] <span class="ot"><-</span> xdata<span class="sc">$</span>days_to_last_followup[censIx]</span>
<span id="cb4-11"><a href="#cb4-11" tabindex="-1"></a></span>
<span id="cb4-12"><a href="#cb4-12" tabindex="-1"></a><span class="co"># Keep only valid individuals</span></span>
<span id="cb4-13"><a href="#cb4-13" tabindex="-1"></a><span class="co">#</span></span>
<span id="cb4-14"><a href="#cb4-14" tabindex="-1"></a><span class="co"># they are valid if they have:</span></span>
<span id="cb4-15"><a href="#cb4-15" tabindex="-1"></a><span class="co"># - either a follow_up time or event time</span></span>
<span id="cb4-16"><a href="#cb4-16" tabindex="-1"></a><span class="co"># - a valid vital_status (i.e. not missing)</span></span>
<span id="cb4-17"><a href="#cb4-17" tabindex="-1"></a><span class="co"># - folloup_time or event_time > 0</span></span>
<span id="cb4-18"><a href="#cb4-18" tabindex="-1"></a>validIx <span class="ot"><-</span> <span class="fu">as.vector</span>(<span class="sc">!</span><span class="fu">is.na</span>(survEventTime) <span class="sc">&</span> <span class="sc">!</span><span class="fu">is.na</span>(xdata<span class="sc">$</span>vital_status) <span class="sc">&</span> survEventTime <span class="sc">></span> <span class="dv">0</span>)</span>
<span id="cb4-19"><a href="#cb4-19" tabindex="-1"></a>ydata <span class="ot"><-</span> <span class="fu">data.frame</span>(</span>
<span id="cb4-20"><a href="#cb4-20" tabindex="-1"></a> <span class="at">time =</span> survEventTime[validIx],</span>
<span id="cb4-21"><a href="#cb4-21" tabindex="-1"></a> <span class="at">status =</span> xdata<span class="sc">$</span>vital_status[validIx],</span>
<span id="cb4-22"><a href="#cb4-22" tabindex="-1"></a> <span class="at">row.names =</span> xdata<span class="sc">$</span>patientID[validIx]</span>
<span id="cb4-23"><a href="#cb4-23" tabindex="-1"></a>)</span></code></pre></div>
<p>The function <code>cv.glmSparseNet</code> fits the survival data
using 10-fold cross validation and using a cutoff value of 0.6 to reduce
the size of the network.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="co"># build response object for glmnet</span></span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a>fit3 <span class="ot"><-</span> <span class="fu">cv.glmSparseNet</span>(</span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a> xdata, </span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a> ydata,</span>
<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a> <span class="at">family =</span> <span class="st">"cox"</span>,</span>
<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a> <span class="at">network =</span> <span class="st">"correlation"</span>,</span>
<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a> <span class="at">experiment =</span> <span class="st">"RNASeq2GeneNorm"</span>,</span>
<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a> <span class="at">alpha =</span> .<span class="dv">7</span>,</span>
<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a> <span class="at">nlambda =</span> <span class="dv">1000</span>,</span>
<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a> <span class="at">options =</span> <span class="fu">networkOptions</span>(</span>
<span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a> <span class="at">cutoff =</span> .<span class="dv">6</span>,</span>
<span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a> <span class="at">minDegree =</span> <span class="fl">0.2</span>,</span>
<span id="cb5-13"><a href="#cb5-13" tabindex="-1"></a> <span class="at">transFun =</span> hubHeuristic</span>
<span id="cb5-14"><a href="#cb5-14" tabindex="-1"></a> )</span>
<span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a>)</span></code></pre></div>
<pre><code>## Warning: 'experiments' dropped; see 'drops()'
## harmonizing input:
## removing 306 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'</code></pre>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="fu">plot</span>(fit3)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p><em>Cross validation plot, showing all 1000 lambdas tested and the
error for each, vertical lines show best model and another with fewer
variables selected within one standard error of the best.</em></p>
<h2 id="visualization-and-analytical-tools">Visualization and Analytical
tools</h2>
<h3 id="survival-curves-with-separate2groupscox">Survival curves with
<code>separate2groupsCox</code></h3>
<p>This function generates Kaplan-Meier survival model based on the
estimated coefficients of the Cox model. It creates two groups based on
the relative risk and displays both survival curves <em>(high
vs. low-risk patients, as defined by the median)</em> and the
corresponding results of log-rank tests.</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="co"># Data to use in draw.kaplan function</span></span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="co"># * it takes the input data, response and coefficients</span></span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a><span class="co"># * calculates the relative risk</span></span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a><span class="co"># * separates individuals based on relative risk into High/Low risk groups</span></span>
<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a>xdataReduced <span class="ot"><-</span> <span class="fu">as</span>(xdata[, , <span class="st">"RNASeq2GeneNorm"</span>], <span class="st">"MatchedAssayExperiment"</span>)</span></code></pre></div>
<pre><code>## Warning: 'experiments' dropped; see 'drops()'
## harmonizing input:
## removing 306 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'</code></pre>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>ydataKM <span class="ot"><-</span> ydata[<span class="fu">rownames</span>(<span class="fu">colData</span>(xdataReduced)), ]</span>
<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a>bestModelCoef <span class="ot"><-</span> <span class="fu">coef</span>(fit3, <span class="at">s =</span> <span class="st">"lambda.min"</span>)[, <span class="dv">1</span>]</span></code></pre></div>
<p>Kaplan-Meier plot</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="fu">separate2GroupsCox</span>(</span>
<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a> bestModelCoef, <span class="fu">t</span>(<span class="fu">assay</span>(xdata[[<span class="st">"RNASeq2GeneNorm"</span>]])), ydataKM, <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)</span>
<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a>)</span></code></pre></div>
<pre><code>## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
## $pvalue
## [1] 2.728306e-07
##
## $plot</code></pre>
<p><img 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" /><!-- --></p>
<pre><code>##
## $km
## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognosticIndexDf)
##
## n events median 0.95LCL 0.95UCL
## Low risk - 1 40 5 NA NA NA
## High risk - 1 39 23 1105 579 NA</code></pre>
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