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<h1 class="title toc-ignore">Using IntegratedLearner for multi-omics
prediction and classification</h1>
<h4 class="author">Himel Mallick and Anupreet Porwal</h4>
<h4 class="date">2024-08-02</h4>
<p>This vignette highlights some example workflows for performing
multi-omics prediction and classification using the
<code>IntegratedLearner</code> R package.</p>
<p><code>IntegratedLearner</code> provides an integrated machine
learning framework to 1) consolidate predictions by borrowing
information across several longitudinal and cross-sectional omics data
layers, 2) decipher the mechanistic role of individual omics features
that can potentially lead to new sets of testable hypotheses, and 3)
quantify uncertainty of the integration process. Three types of
integration paradigms are supported: early, late, and intermediate. The
software includes multiple ML models based on the <a href="https://cran.r-project.org/web/packages/SuperLearner/index.html">SuperLearner
R package</a> as well as several data exploration capabilities and
visualization modules in a unified estimation framework.</p>
<div id="load-packages" class="section level2">
<h2>Load Packages</h2>
<p>Once installed, <strong><code>IntegratedLearner</code></strong> can
be simply loaded (along with the required packages) as follows:</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="co"># Load the IntegratedLearner package</span></span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="fu">library</span>(IntegratedLearner)</span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a><span class="fu">options</span>(<span class="at">java.parameters =</span> <span class="st">"-Xmx5g"</span>) <span class="co"># This is needed for running BART</span></span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a></span>
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a><span class="co"># Load other essential packages for this tutorial</span></span>
<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a><span class="fu">library</span>(tidyverse) </span>
<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a><span class="fu">library</span>(SuperLearner)</span>
<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a><span class="fu">library</span>(caret)</span>
<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a><span class="fu">library</span>(cowplot)</span>
<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a><span class="fu">library</span>(mcmcplots)</span>
<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a><span class="fu">library</span>(bartMachine)</span></code></pre></div>
</div>
<div id="the-input" class="section level2">
<h2>The Input</h2>
<p><strong><code>IntegratedLearner</code></strong> requires three
tab-delimited input files: (i) concatenated multi-omics profiles
(<code>feature_table</code>), (ii) sample-specific metadata
(<code>sample_metadata</code>), and (iii) feature-specific metadata
(<code>feature_metadata</code>). The rows of the
<code>feature_table</code> should correspond to the concatenated
features (e.g., microbiome, gene expression, metabolites, etc.) and the
columns should correspond to samples.</p>
<p>The columns of the <code>sample_metadata</code> must correspond to
sample-specific covariates (e.g., disease status or the clinical outcome
of interest) with the rows corresponding to samples. Row names of
<code>sample_metadata</code> must match the column names of
<code>feature_table</code>. Furthermore, <code>sample_metadata</code>
should have a column named <strong><code>subjectID</code></strong>
describing per-subject unique identifiers. For longitudinal designs,
this variable is expected to have non-unique values. Additionally, a
column named <strong><code>Y</code></strong> must be present which is
the outcome of interest (can be binary or continuous).</p>
<p><code>feature_metadata</code> is expected to be a data frame
containing feature-specific metadata with a column named
<strong><code>featureID</code></strong> describing per-feature unique
identifiers and <strong><code>featureType</code></strong> describing the
corresponding omics source layers (e.g., metagenomics, metabolomics,
etc.). Row names of <code>feature_metadata</code> must match that of
<code>feature_table</code>.</p>
<p>For the purpose of this vignette, it is assumed that these three
input data have already been quality-controlled with necessary
preprocessing steps. For the classification example, we will be using a
cleaned version of the PRISM multi-omics dataset (<a href="https://www.nature.com/articles/s41564-018-0306-4">Franzosa et
al., 2019</a>) which can be downloaded from <a href="https://github.com/himelmallick/IntegratedLearner/blob/master/data/PRISM.RData">here</a>.</p>
<div id="example-1---prediction-of-binary-ibd-disease-status-from-multi-omics-profiles" class="section level3">
<h3>Example 1 - Prediction of Binary IBD Disease Status from Multi-omics
Profiles</h3>
<p>We first use the PRISM dataset (<a href="https://www.nature.com/articles/s41564-018-0306-4">Franzosa et
al., 2019</a>) which is a gut microbiome multi-omics dataset consisting
of <span class="math inline">\(9171\)</span> quality-controlled features
from 2 layers (i.e., microbiome taxonomic profiles and metabolites). In
this study, stool samples were collected from a cross-sectional cohort
of individuals enrolled in the Prospective Registry in IBD Study at MGH
(PRISM) in order to characterize the gut metabolic profile and
microbiome composition in Inflammatory Bowel Diseases (IBD).</p>
<p>This cohort included 155 subjects: 68 with Crohn’s disease (CD), 53
with ulcerative colitis (UC), jointly grouped as IBD, and 34 non-IBD
controls. Each stool sample was subjected to metagenomic sequencing
followed by profiling of microbial community taxonomic composition and
functional potential. In addition, each sample was analyzed by four
liquid chromatography tandem mass spectrometry (LC-MS) methods measuring
polar metabolites, lipids, free fatty acids, and bile acids,
respectively.</p>
<p>In addition to carrying out a holistic investigation of the
microbiome–metabolome interface, one of the primary objectives of this
study was to assess the power of the metabolomic and microbial layers in
classifying IBD status.</p>
<p>Let us first examine the characteristics of the PRISM data by loading
the <a href="https://github.com/himelmallick/IntegratedLearner/blob/master/data/PRISM.RData"><strong><code>PRISM.RData</code></strong></a>
object which contains a list of three data frames:
<code>feature_table</code>, <code>sample_metadata</code> and
<code>feature_metadata</code>. Note that, the <code>feature_table</code>
contains negative values. This is because both species and metabolite
data have been residualized to remove the effect of potential
confounders.</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>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="co"># Load dataset </span></span>
<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a><span class="fu">load</span>(<span class="fu">url</span>(<span class="st">"https://github.com/himelmallick/IntegratedLearner/blob/master/data/PRISM.RData?raw=true"</span>))</span>
<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a> </span>
<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a><span class="co"># Extract individual components </span></span>
<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a>feature_table<span class="ot"><-</span>pcl<span class="sc">$</span>feature_table</span>
<span id="cb2-7"><a href="#cb2-7" tabindex="-1"></a>sample_metadata<span class="ot"><-</span>pcl<span class="sc">$</span>sample_metadata</span>
<span id="cb2-8"><a href="#cb2-8" tabindex="-1"></a>feature_metadata<span class="ot"><-</span>pcl<span class="sc">$</span>feature_metadata</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 class="fu">rm</span>(pcl)</span>
<span id="cb2-11"><a href="#cb2-11" tabindex="-1"></a> </span>
<span id="cb2-12"><a href="#cb2-12" tabindex="-1"></a><span class="co"># Explore data dimensions</span></span>
<span id="cb2-13"><a href="#cb2-13" tabindex="-1"></a><span class="fu">head</span>(feature_table[<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, <span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>])</span>
<span id="cb2-14"><a href="#cb2-14" tabindex="-1"></a><span class="co">#> G35127 G35128 G35152 G36347</span></span>
<span id="cb2-15"><a href="#cb2-15" tabindex="-1"></a><span class="co">#> Granulicella_unclassified -0.05253649 -0.05127158 -0.06133085 0.004887447</span></span>
<span id="cb2-16"><a href="#cb2-16" tabindex="-1"></a><span class="co">#> Actinomyces_graevenitzii 1.04668500 -1.32629194 -1.51654615 -3.247989324</span></span>
<span id="cb2-17"><a href="#cb2-17" tabindex="-1"></a><span class="co">#> Actinomyces_johnsonii -0.70327678 -0.41575776 -0.29326475 -0.314361595</span></span>
<span id="cb2-18"><a href="#cb2-18" tabindex="-1"></a><span class="co">#> Actinomyces_massiliensis -0.56808952 0.14722099 0.05660884 -1.077235688</span></span>
<span id="cb2-19"><a href="#cb2-19" tabindex="-1"></a><span class="co">#> Actinomyces_naeslundii -0.49546119 -0.15921604 -0.03146485 -0.354377267</span></span>
<span id="cb2-20"><a href="#cb2-20" tabindex="-1"></a><span class="co">#> G36348</span></span>
<span id="cb2-21"><a href="#cb2-21" tabindex="-1"></a><span class="co">#> Granulicella_unclassified -0.006164066</span></span>
<span id="cb2-22"><a href="#cb2-22" tabindex="-1"></a><span class="co">#> Actinomyces_graevenitzii -0.717183019</span></span>
<span id="cb2-23"><a href="#cb2-23" tabindex="-1"></a><span class="co">#> Actinomyces_johnsonii -0.340485318</span></span>
<span id="cb2-24"><a href="#cb2-24" tabindex="-1"></a><span class="co">#> Actinomyces_massiliensis -0.159240362</span></span>
<span id="cb2-25"><a href="#cb2-25" tabindex="-1"></a><span class="co">#> Actinomyces_naeslundii -0.139758576</span></span>
<span id="cb2-26"><a href="#cb2-26" tabindex="-1"></a><span class="fu">head</span>(sample_metadata[<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, ])</span>
<span id="cb2-27"><a href="#cb2-27" tabindex="-1"></a><span class="co">#> Diagnosis Y subjectID</span></span>
<span id="cb2-28"><a href="#cb2-28" tabindex="-1"></a><span class="co">#> G35127 CD 1 G35127</span></span>
<span id="cb2-29"><a href="#cb2-29" tabindex="-1"></a><span class="co">#> G35128 CD 1 G35128</span></span>
<span id="cb2-30"><a href="#cb2-30" tabindex="-1"></a><span class="co">#> G35152 CD 1 G35152</span></span>
<span id="cb2-31"><a href="#cb2-31" tabindex="-1"></a><span class="co">#> G36347 CD 1 G36347</span></span>
<span id="cb2-32"><a href="#cb2-32" tabindex="-1"></a><span class="co">#> G36348 CD 1 G36348</span></span>
<span id="cb2-33"><a href="#cb2-33" tabindex="-1"></a><span class="fu">head</span>(feature_metadata[<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, ])</span>
<span id="cb2-34"><a href="#cb2-34" tabindex="-1"></a><span class="co">#> featureID featureType</span></span>
<span id="cb2-35"><a href="#cb2-35" tabindex="-1"></a><span class="co">#> Granulicella_unclassified Granulicella_unclassified species</span></span>
<span id="cb2-36"><a href="#cb2-36" tabindex="-1"></a><span class="co">#> Actinomyces_graevenitzii Actinomyces_graevenitzii species</span></span>
<span id="cb2-37"><a href="#cb2-37" tabindex="-1"></a><span class="co">#> Actinomyces_johnsonii Actinomyces_johnsonii species</span></span>
<span id="cb2-38"><a href="#cb2-38" tabindex="-1"></a><span class="co">#> Actinomyces_massiliensis Actinomyces_massiliensis species</span></span>
<span id="cb2-39"><a href="#cb2-39" tabindex="-1"></a><span class="co">#> Actinomyces_naeslundii Actinomyces_naeslundii species</span></span>
<span id="cb2-40"><a href="#cb2-40" tabindex="-1"></a> </span>
<span id="cb2-41"><a href="#cb2-41" tabindex="-1"></a><span class="co"># How many layers and how many features per layer?</span></span>
<span id="cb2-42"><a href="#cb2-42" tabindex="-1"></a><span class="fu">table</span>(feature_metadata<span class="sc">$</span>featureType)</span>
<span id="cb2-43"><a href="#cb2-43" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb2-44"><a href="#cb2-44" tabindex="-1"></a><span class="co">#> metabolites species </span></span>
<span id="cb2-45"><a href="#cb2-45" tabindex="-1"></a><span class="co">#> 8831 340</span></span>
<span id="cb2-46"><a href="#cb2-46" tabindex="-1"></a> </span>
<span id="cb2-47"><a href="#cb2-47" tabindex="-1"></a><span class="co"># Distribution of outcome (1: IBD, 0: nonIBD)</span></span>
<span id="cb2-48"><a href="#cb2-48" tabindex="-1"></a><span class="fu">table</span>(sample_metadata<span class="sc">$</span>Y)</span>
<span id="cb2-49"><a href="#cb2-49" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb2-50"><a href="#cb2-50" tabindex="-1"></a><span class="co">#> 0 1 </span></span>
<span id="cb2-51"><a href="#cb2-51" tabindex="-1"></a><span class="co">#> 34 121</span></span>
<span id="cb2-52"><a href="#cb2-52" tabindex="-1"></a> </span>
<span id="cb2-53"><a href="#cb2-53" tabindex="-1"></a><span class="co"># Sanity check</span></span>
<span id="cb2-54"><a href="#cb2-54" tabindex="-1"></a><span class="fu">all</span>(<span class="fu">rownames</span>(feature_table)<span class="sc">==</span><span class="fu">rownames</span>(feature_metadata)) <span class="co"># TRUE</span></span>
<span id="cb2-55"><a href="#cb2-55" tabindex="-1"></a><span class="co">#> [1] TRUE</span></span>
<span id="cb2-56"><a href="#cb2-56" tabindex="-1"></a><span class="fu">all</span>(<span class="fu">colnames</span>(feature_table)<span class="sc">==</span><span class="fu">rownames</span>(sample_metadata)) <span class="co"># TRUE</span></span>
<span id="cb2-57"><a href="#cb2-57" tabindex="-1"></a><span class="co">#> [1] TRUE</span></span>
<span id="cb2-58"><a href="#cb2-58" tabindex="-1"></a></span>
<span id="cb2-59"><a href="#cb2-59" tabindex="-1"></a><span class="co"># Load independent validation dataset</span></span>
<span id="cb2-60"><a href="#cb2-60" tabindex="-1"></a><span class="fu">load</span>(<span class="fu">url</span>(<span class="st">"https://github.com/himelmallick/IntegratedLearner/blob/master/data/NLIBD.RData?raw=true"</span>))</span>
<span id="cb2-61"><a href="#cb2-61" tabindex="-1"></a></span>
<span id="cb2-62"><a href="#cb2-62" tabindex="-1"></a>feature_table_valid<span class="ot"><-</span>pcl<span class="sc">$</span>feature_table</span>
<span id="cb2-63"><a href="#cb2-63" tabindex="-1"></a>sample_metadata_valid<span class="ot"><-</span>pcl<span class="sc">$</span>sample_metadata</span>
<span id="cb2-64"><a href="#cb2-64" tabindex="-1"></a><span class="fu">rm</span>(pcl)</span>
<span id="cb2-65"><a href="#cb2-65" tabindex="-1"></a></span>
<span id="cb2-66"><a href="#cb2-66" tabindex="-1"></a></span>
<span id="cb2-67"><a href="#cb2-67" tabindex="-1"></a><span class="co"># Sanity check to make sure test set has sample structure as training </span></span>
<span id="cb2-68"><a href="#cb2-68" tabindex="-1"></a><span class="fu">all</span>(<span class="fu">rownames</span>(feature_table)<span class="sc">==</span><span class="fu">rownames</span>(feature_table_valid)) <span class="co"># TRUE</span></span>
<span id="cb2-69"><a href="#cb2-69" tabindex="-1"></a><span class="co">#> [1] TRUE</span></span>
<span id="cb2-70"><a href="#cb2-70" tabindex="-1"></a><span class="fu">all</span>(<span class="fu">colnames</span>(feature_table_valid)<span class="sc">==</span><span class="fu">rownames</span>(sample_metadata_valid)) <span class="co"># TRUE</span></span>
<span id="cb2-71"><a href="#cb2-71" tabindex="-1"></a><span class="co">#> [1] TRUE</span></span></code></pre></div>
<p><code>IntegratedLearner</code> late fusion algorithm proceeds by 1)
fitting a machine learning algorithm per-layer to predict outcome
(<code>base_learner</code>) and 2) combining the layer-wise
cross-validated predictions using a meta model
(<code>meta_learner</code>) to generate final predictions based on all
available data points. As a default choice, we recommend
<code>SL.nnls.auc</code> as the meta model algorithm. It fits a
non-negative least squares (in case of continuous outcome) and rank loss
minimization (in case of binary outcome) on layer-wise cross-validated
predictions to generate the final predictions and quantify per-layer
contribution in the final predictions. As an example, we would like to
build a random forest classifier based on these data to classify IBD
patients. By default, <code>IntegratedLearner</code> uses a 5-fold CV to
train the model (for the full dataset, it takes about ~5-6 minutes using
a single core of a system with an Intel Core i5 processor (1.7 GHz) and
16 GB of RAM - adjust your expectations accordingly!).</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>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a>fit<span class="ot"><-</span><span class="fu">IntegratedLearner</span>(<span class="at">feature_table =</span> feature_table,</span>
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a> <span class="at">sample_metadata =</span> sample_metadata, </span>
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a> <span class="at">feature_metadata =</span> feature_metadata,</span>
<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a> <span class="at">feature_table_valid =</span> feature_table_valid,</span>
<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a> <span class="at">sample_metadata_valid =</span> sample_metadata_valid,</span>
<span id="cb3-7"><a href="#cb3-7" tabindex="-1"></a> <span class="at">folds =</span> <span class="dv">5</span>,</span>
<span id="cb3-8"><a href="#cb3-8" tabindex="-1"></a> <span class="at">base_learner =</span> <span class="st">'SL.randomForest'</span>,</span>
<span id="cb3-9"><a href="#cb3-9" tabindex="-1"></a> <span class="at">meta_learner =</span> <span class="st">'SL.nnls.auc'</span>,</span>
<span id="cb3-10"><a href="#cb3-10" tabindex="-1"></a> <span class="at">verbose =</span> <span class="cn">TRUE</span>,</span>
<span id="cb3-11"><a href="#cb3-11" tabindex="-1"></a> <span class="at">family=</span><span class="fu">binomial</span>())</span>
<span id="cb3-12"><a href="#cb3-12" tabindex="-1"></a><span class="co">#> Running base model for layer 1 ...</span></span>
<span id="cb3-13"><a href="#cb3-13" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 8831</span></span>
<span id="cb3-14"><a href="#cb3-14" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-15"><a href="#cb3-15" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 8831</span></span>
<span id="cb3-16"><a href="#cb3-16" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-17"><a href="#cb3-17" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 8831</span></span>
<span id="cb3-18"><a href="#cb3-18" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-19"><a href="#cb3-19" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 8831</span></span>
<span id="cb3-20"><a href="#cb3-20" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-21"><a href="#cb3-21" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 8831</span></span>
<span id="cb3-22"><a href="#cb3-22" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-23"><a href="#cb3-23" tabindex="-1"></a><span class="co">#> Non-Negative least squares convergence: TRUE</span></span>
<span id="cb3-24"><a href="#cb3-24" tabindex="-1"></a><span class="co">#> full SL.randomForest_All</span></span>
<span id="cb3-25"><a href="#cb3-25" tabindex="-1"></a><span class="co">#> Running base model for layer 2 ...</span></span>
<span id="cb3-26"><a href="#cb3-26" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 340</span></span>
<span id="cb3-27"><a href="#cb3-27" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-28"><a href="#cb3-28" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 340</span></span>
<span id="cb3-29"><a href="#cb3-29" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-30"><a href="#cb3-30" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 340</span></span>
<span id="cb3-31"><a href="#cb3-31" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-32"><a href="#cb3-32" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 340</span></span>
<span id="cb3-33"><a href="#cb3-33" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-34"><a href="#cb3-34" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 340</span></span>
<span id="cb3-35"><a href="#cb3-35" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-36"><a href="#cb3-36" tabindex="-1"></a><span class="co">#> Non-Negative least squares convergence: TRUE</span></span>
<span id="cb3-37"><a href="#cb3-37" tabindex="-1"></a><span class="co">#> full SL.randomForest_All</span></span>
<span id="cb3-38"><a href="#cb3-38" tabindex="-1"></a><span class="co">#> Running stacked model...</span></span>
<span id="cb3-39"><a href="#cb3-39" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 2</span></span>
<span id="cb3-40"><a href="#cb3-40" tabindex="-1"></a><span class="co">#> CV SL.nnls.auc_All</span></span>
<span id="cb3-41"><a href="#cb3-41" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 2</span></span>
<span id="cb3-42"><a href="#cb3-42" tabindex="-1"></a><span class="co">#> CV SL.nnls.auc_All</span></span>
<span id="cb3-43"><a href="#cb3-43" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 2</span></span>
<span id="cb3-44"><a href="#cb3-44" tabindex="-1"></a><span class="co">#> CV SL.nnls.auc_All</span></span>
<span id="cb3-45"><a href="#cb3-45" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 2</span></span>
<span id="cb3-46"><a href="#cb3-46" tabindex="-1"></a><span class="co">#> CV SL.nnls.auc_All</span></span>
<span id="cb3-47"><a href="#cb3-47" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 2</span></span>
<span id="cb3-48"><a href="#cb3-48" tabindex="-1"></a><span class="co">#> CV SL.nnls.auc_All</span></span>
<span id="cb3-49"><a href="#cb3-49" tabindex="-1"></a><span class="co">#> Non-Negative least squares convergence: TRUE</span></span>
<span id="cb3-50"><a href="#cb3-50" tabindex="-1"></a><span class="co">#> full SL.nnls.auc_All</span></span>
<span id="cb3-51"><a href="#cb3-51" tabindex="-1"></a><span class="co">#> Running concatenated model...</span></span>
<span id="cb3-52"><a href="#cb3-52" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 9171</span></span>
<span id="cb3-53"><a href="#cb3-53" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-54"><a href="#cb3-54" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 9171</span></span>
<span id="cb3-55"><a href="#cb3-55" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-56"><a href="#cb3-56" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 9171</span></span>
<span id="cb3-57"><a href="#cb3-57" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-58"><a href="#cb3-58" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 9171</span></span>
<span id="cb3-59"><a href="#cb3-59" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-60"><a href="#cb3-60" tabindex="-1"></a><span class="co">#> Number of covariates in All is: 9171</span></span>
<span id="cb3-61"><a href="#cb3-61" tabindex="-1"></a><span class="co">#> CV SL.randomForest_All</span></span>
<span id="cb3-62"><a href="#cb3-62" tabindex="-1"></a><span class="co">#> Non-Negative least squares convergence: TRUE</span></span>
<span id="cb3-63"><a href="#cb3-63" tabindex="-1"></a><span class="co">#> full SL.randomForest_All</span></span>
<span id="cb3-64"><a href="#cb3-64" tabindex="-1"></a><span class="co">#> Time for model fit : 2.75 minutes </span></span>
<span id="cb3-65"><a href="#cb3-65" tabindex="-1"></a><span class="co">#> ========================================</span></span>
<span id="cb3-66"><a href="#cb3-66" tabindex="-1"></a><span class="co">#> Model fit for individual layers: SL.randomForest </span></span>
<span id="cb3-67"><a href="#cb3-67" tabindex="-1"></a><span class="co">#> Model fit for stacked layer: SL.nnls.auc </span></span>
<span id="cb3-68"><a href="#cb3-68" tabindex="-1"></a><span class="co">#> Model fit for concatenated layer: SL.randomForest </span></span>
<span id="cb3-69"><a href="#cb3-69" tabindex="-1"></a><span class="co">#> ========================================</span></span>
<span id="cb3-70"><a href="#cb3-70" tabindex="-1"></a><span class="co">#> AUC metric for training data: </span></span>
<span id="cb3-71"><a href="#cb3-71" tabindex="-1"></a><span class="co">#> Individual layers: </span></span>
<span id="cb3-72"><a href="#cb3-72" tabindex="-1"></a><span class="co">#> metabolites species </span></span>
<span id="cb3-73"><a href="#cb3-73" tabindex="-1"></a><span class="co">#> 0.934 0.955 </span></span>
<span id="cb3-74"><a href="#cb3-74" tabindex="-1"></a><span class="co">#> ======================</span></span>
<span id="cb3-75"><a href="#cb3-75" tabindex="-1"></a><span class="co">#> Stacked model:0.951 </span></span>
<span id="cb3-76"><a href="#cb3-76" tabindex="-1"></a><span class="co">#> ======================</span></span>
<span id="cb3-77"><a href="#cb3-77" tabindex="-1"></a><span class="co">#> Concatenated model:0.957 </span></span>
<span id="cb3-78"><a href="#cb3-78" tabindex="-1"></a><span class="co">#> ======================</span></span>
<span id="cb3-79"><a href="#cb3-79" tabindex="-1"></a><span class="co">#> ========================================</span></span>
<span id="cb3-80"><a href="#cb3-80" tabindex="-1"></a><span class="co">#> AUC metric for test data: </span></span>
<span id="cb3-81"><a href="#cb3-81" tabindex="-1"></a><span class="co">#> Individual layers: </span></span>
<span id="cb3-82"><a href="#cb3-82" tabindex="-1"></a><span class="co">#> metabolites species </span></span>
<span id="cb3-83"><a href="#cb3-83" tabindex="-1"></a><span class="co">#> 0.782 0.879 </span></span>
<span id="cb3-84"><a href="#cb3-84" tabindex="-1"></a><span class="co">#> ======================</span></span>
<span id="cb3-85"><a href="#cb3-85" tabindex="-1"></a><span class="co">#> Stacked model:0.908 </span></span>
<span id="cb3-86"><a href="#cb3-86" tabindex="-1"></a><span class="co">#> ======================</span></span>
<span id="cb3-87"><a href="#cb3-87" tabindex="-1"></a><span class="co">#> Concatenated model:0.895 </span></span>
<span id="cb3-88"><a href="#cb3-88" tabindex="-1"></a><span class="co">#> ======================</span></span>
<span id="cb3-89"><a href="#cb3-89" tabindex="-1"></a><span class="co">#> ========================================</span></span>
<span id="cb3-90"><a href="#cb3-90" tabindex="-1"></a><span class="co">#> Weights for individual layers predictions in IntegratedLearner: </span></span>
<span id="cb3-91"><a href="#cb3-91" tabindex="-1"></a><span class="co">#> metabolites species </span></span>
<span id="cb3-92"><a href="#cb3-92" tabindex="-1"></a><span class="co">#> 0.18 0.82 </span></span>
<span id="cb3-93"><a href="#cb3-93" tabindex="-1"></a><span class="co">#> ========================================</span></span></code></pre></div>
<p><code>IntegratedLearner</code> offers easily accessible and
interpretable summary outputs including 1) computation time, 2)
per-layer AUC/ <span class="math inline">\(R^2\)</span> scores on
training and/or test data (if <code>feature_table_valid</code> and
<code>sample_metadata_valid</code> are provided), 3) AUC/ <span class="math inline">\(R^2\)</span> metrics for stacked and concatenated
model if <code>run_stacked=TRUE</code> and <code>run_concat=TRUE</code>
and 4) estimated per-layer weights from meta learner in stacked
model.</p>
<p>We can visualize the classification performance by constructing
layer-wise ROC curves for both train and test set using
<code>plot.learner()</code> function that takes
<code>IntegratedLearner</code> object as input:</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>plot.obj <span class="ot"><-</span> IntegratedLearner<span class="sc">:::</span><span class="fu">plot.learner</span>(fit)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>For this particular multi-omics dataset, species data is more
predictive than metabolites in classifying IBD patients and the stacked
model achieves superior accuracy to individual layers and concatenation
model in independent validation, indicating that the stacked multi-omics
classifier leads to a competitive or superior cross-validated and
independent validation classification accuracy than its single-omics
counterparts.</p>
</div>
<div id="example-2---prediction-of-continuous-gestational-age-from-multi-omics-profiles" class="section level3">
<h3>Example 2 - Prediction of Continuous Gestational Age from
Multi-omics Profiles</h3>
<p>The second dataset is a longitudinal multi-omics data from pregnant
women in a cohort study at Stanford University (<a href="https://academic.oup.com/bioinformatics/article/35/1/95/5047759">Ghaemi
et al., 2019</a>) that aimed to prospectively examine environmental and
biological factors associated with normal and pathological pregnancies.
Women were eligible if they were at least 18 years of age and in their
first trimester of a singleton pregnancy. Unlike the PRISM dataset, the
outcome variable in this study (gestational age) is a continuous
outcome, which was determined by best obstetrical estimate as
recommended by the American College of Obstetricians and Gynecologists
.</p>
<p>In 17 women, three samples were collected during pregnancy and a
fourth one after delivery. The time points were chosen such that a
peripheral blood sample (CyTOF analysis), a plasma sample (proteomic,
cell-free transcriptomics, metabolomics analyses), a serum sample
(luminex analyses) and a series of culture swabs (microbiome analysis)
were simultaneously collected from each woman during the first (7–14
weeks), second (15–20 weeks) and third (24–32 weeks) trimester of
pregnancy and 6-week postpartum.</p>
<p>In order to assess performance of various machine learning modules
available in <code>IntegratedLearner</code>, we calculate the
coefficient of determination (<span class="math inline">\(R^2\)</span>)
based on the observed and cross-validated out-of-sample predicted values
of the gestational age. As before, cross-validation folds are
synchronized between the individual base models from each dataset to
leave out the same set of data points at all levels of the analysis.</p>
<p>As before, let’s now examine the characteristics of the pregnancy
data by loading the <a href="https://github.com/himelmallick/IntegratedLearner/blob/master/data/pregnancy.RData"><strong><code>pregnancy.RData</code></strong></a>
object which again contains a list of three data frames:
<code>feature_table</code>, <code>sample_metadata</code> and
<code>feature_metadata</code>. Unlike the PRISM study, this dataset
contains repeated measures during pregnancy that allowed assessing
important biological adaptations occurring continuously from the early
phases of fetal development (first trimester) to the late phases of
gestation (third trimester). Considering both the small sample size and
the repeated measures aspects of this study, we employ a
one-subject-leave-out cross-validation to build prediction models,
following the original study.</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>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="co"># Load dataset </span></span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="fu">load</span>(<span class="fu">url</span>(<span class="st">"https://github.com/himelmallick/IntegratedLearner/blob/master/data/pregnancy.RData?raw=true"</span>))</span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a></span>
<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a><span class="co"># Extract individual components </span></span>
<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a>feature_table<span class="ot"><-</span>pcl<span class="sc">$</span>feature_table</span>
<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a>sample_metadata<span class="ot"><-</span>pcl<span class="sc">$</span>sample_metadata</span>
<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a>feature_metadata<span class="ot"><-</span>pcl<span class="sc">$</span>feature_metadata</span>
<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a> </span>
<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a><span class="co"># Explore data dimensions</span></span>
<span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a><span class="fu">head</span>(feature_table[<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, <span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>])</span>
<span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a><span class="co">#> PTLG002_1 PTLG003_1 PTLG004_1 PTLG005_1 PTLG007_1</span></span>
<span id="cb5-13"><a href="#cb5-13" tabindex="-1"></a><span class="co">#> CEP135 28.21785 54.56723 53.776824 15.26909 11.04831</span></span>
<span id="cb5-14"><a href="#cb5-14" tabindex="-1"></a><span class="co">#> MIIP 10.10756 17.11006 4.336841 0.00000 19.88695</span></span>
<span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a><span class="co">#> GNL3 45.25968 58.26670 56.378929 70.23780 64.08018</span></span>
<span id="cb5-16"><a href="#cb5-16" tabindex="-1"></a><span class="co">#> CEP70 79.09550 67.97782 93.675759 128.26033 66.28985</span></span>
<span id="cb5-17"><a href="#cb5-17" tabindex="-1"></a><span class="co">#> TIMP1 172.23675 121.62018 183.014677 247.35921 304.93329</span></span>
<span id="cb5-18"><a href="#cb5-18" tabindex="-1"></a><span class="fu">head</span>(sample_metadata[<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, ])</span>
<span id="cb5-19"><a href="#cb5-19" tabindex="-1"></a><span class="co">#> Y subjectID</span></span>
<span id="cb5-20"><a href="#cb5-20" tabindex="-1"></a><span class="co">#> PTLG002_1 11 PTLG002</span></span>
<span id="cb5-21"><a href="#cb5-21" tabindex="-1"></a><span class="co">#> PTLG003_1 11 PTLG003</span></span>
<span id="cb5-22"><a href="#cb5-22" tabindex="-1"></a><span class="co">#> PTLG004_1 11 PTLG004</span></span>
<span id="cb5-23"><a href="#cb5-23" tabindex="-1"></a><span class="co">#> PTLG005_1 11 PTLG005</span></span>
<span id="cb5-24"><a href="#cb5-24" tabindex="-1"></a><span class="co">#> PTLG007_1 11 PTLG007</span></span>
<span id="cb5-25"><a href="#cb5-25" tabindex="-1"></a><span class="fu">head</span>(feature_metadata[<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, ])</span>
<span id="cb5-26"><a href="#cb5-26" tabindex="-1"></a><span class="co">#> featureID featureType</span></span>
<span id="cb5-27"><a href="#cb5-27" tabindex="-1"></a><span class="co">#> CEP135 CEP135 CellfreeRNA</span></span>
<span id="cb5-28"><a href="#cb5-28" tabindex="-1"></a><span class="co">#> MIIP MIIP CellfreeRNA</span></span>
<span id="cb5-29"><a href="#cb5-29" tabindex="-1"></a><span class="co">#> GNL3 GNL3 CellfreeRNA</span></span>
<span id="cb5-30"><a href="#cb5-30" tabindex="-1"></a><span class="co">#> CEP70 CEP70 CellfreeRNA</span></span>
<span id="cb5-31"><a href="#cb5-31" tabindex="-1"></a><span class="co">#> TIMP1 TIMP1 CellfreeRNA</span></span>
<span id="cb5-32"><a href="#cb5-32" tabindex="-1"></a> </span>
<span id="cb5-33"><a href="#cb5-33" tabindex="-1"></a><span class="co"># How many layers and how many features per layer?</span></span>
<span id="cb5-34"><a href="#cb5-34" tabindex="-1"></a><span class="fu">table</span>(feature_metadata<span class="sc">$</span>featureType)</span>
<span id="cb5-35"><a href="#cb5-35" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb5-36"><a href="#cb5-36" tabindex="-1"></a><span class="co">#> CellfreeRNA ImmuneSystem Metabolomics Microbiome PlasmaLuminex </span></span>
<span id="cb5-37"><a href="#cb5-37" tabindex="-1"></a><span class="co">#> 9084 264 253 259 31 </span></span>
<span id="cb5-38"><a href="#cb5-38" tabindex="-1"></a><span class="co">#> PlasmaSomalogic SerumLuminex </span></span>
<span id="cb5-39"><a href="#cb5-39" tabindex="-1"></a><span class="co">#> 650 31</span></span>
<span id="cb5-40"><a href="#cb5-40" tabindex="-1"></a> </span>
<span id="cb5-41"><a href="#cb5-41" tabindex="-1"></a><span class="co"># Number of subjects</span></span>
<span id="cb5-42"><a href="#cb5-42" tabindex="-1"></a><span class="fu">length</span>(<span class="fu">unique</span>(sample_metadata<span class="sc">$</span>subjectID))</span>
<span id="cb5-43"><a href="#cb5-43" tabindex="-1"></a><span class="co">#> [1] 17</span></span>
<span id="cb5-44"><a href="#cb5-44" tabindex="-1"></a> </span>
<span id="cb5-45"><a href="#cb5-45" tabindex="-1"></a><span class="co"># Sanity check</span></span>
<span id="cb5-46"><a href="#cb5-46" tabindex="-1"></a><span class="fu">all</span>(<span class="fu">rownames</span>(feature_table)<span class="sc">==</span><span class="fu">rownames</span>(feature_metadata)) <span class="co"># TRUE</span></span>
<span id="cb5-47"><a href="#cb5-47" tabindex="-1"></a><span class="co">#> [1] TRUE</span></span>
<span id="cb5-48"><a href="#cb5-48" tabindex="-1"></a><span class="fu">all</span>(<span class="fu">colnames</span>(feature_table)<span class="sc">==</span><span class="fu">rownames</span>(sample_metadata)) <span class="co"># TRUE</span></span>
<span id="cb5-49"><a href="#cb5-49" tabindex="-1"></a><span class="co">#> [1] TRUE</span></span>
<span id="cb5-50"><a href="#cb5-50" tabindex="-1"></a></span>
<span id="cb5-51"><a href="#cb5-51" tabindex="-1"></a><span class="co"># Subset to a few rows to save computing time</span></span>
<span id="cb5-52"><a href="#cb5-52" tabindex="-1"></a><span class="co"># top_n<-50</span></span>
<span id="cb5-53"><a href="#cb5-53" tabindex="-1"></a><span class="co"># subsetIDs<-c(1:top_n, (nrow(feature_table)-top_n+1):nrow(feature_table))</span></span>
<span id="cb5-54"><a href="#cb5-54" tabindex="-1"></a><span class="co"># feature_table<-feature_table[subsetIDs,]</span></span>
<span id="cb5-55"><a href="#cb5-55" tabindex="-1"></a><span class="co"># feature_metadata<-feature_metadata[subsetIDs,]</span></span></code></pre></div>
<p>The default base model recommendation for
<code>IntegratedLearner</code> is Bayesian additive regression trees or
BART (<code>base_learner='SL.BART'</code>). Using BART as base-model
yields uncertainty estimates (i.e. credible intervals) of the prediction
and model parameters in addition to reporting a small set of
interpretable features for follow-up experiments (i.e. feature
importance scores).</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a></span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a>fit<span class="ot"><-</span><span class="fu">IntegratedLearner</span>(<span class="at">feature_table =</span> feature_table,</span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a> <span class="at">sample_metadata =</span> sample_metadata, </span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a> <span class="at">feature_metadata =</span> feature_metadata,</span>
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a> <span class="at">folds =</span> <span class="dv">17</span>,</span>
<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a> <span class="at">base_learner =</span> <span class="st">'SL.BART'</span>,</span>
<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a> <span class="at">meta_learner =</span> <span class="st">'SL.nnls.auc'</span>)</span>
<span id="cb6-8"><a href="#cb6-8" tabindex="-1"></a><span class="co">#> Running base model for layer 1 ... </span></span>
<span id="cb6-9"><a href="#cb6-9" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-10"><a href="#cb6-10" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-11"><a href="#cb6-11" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-12"><a href="#cb6-12" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-13"><a href="#cb6-13" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-14"><a href="#cb6-14" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-15"><a href="#cb6-15" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-16"><a href="#cb6-16" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-17"><a href="#cb6-17" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-18"><a href="#cb6-18" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-19"><a href="#cb6-19" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-20"><a href="#cb6-20" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-21"><a href="#cb6-21" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-22"><a href="#cb6-22" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-23"><a href="#cb6-23" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-24"><a href="#cb6-24" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-25"><a href="#cb6-25" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-26"><a href="#cb6-26" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-27"><a href="#cb6-27" tabindex="-1"></a><span class="co">#> Running base model for layer 2 ... </span></span>
<span id="cb6-28"><a href="#cb6-28" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-29"><a href="#cb6-29" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-30"><a href="#cb6-30" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-31"><a href="#cb6-31" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-32"><a href="#cb6-32" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-33"><a href="#cb6-33" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-34"><a href="#cb6-34" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-35"><a href="#cb6-35" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-36"><a href="#cb6-36" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-37"><a href="#cb6-37" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-38"><a href="#cb6-38" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-39"><a href="#cb6-39" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-40"><a href="#cb6-40" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-41"><a href="#cb6-41" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-42"><a href="#cb6-42" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-43"><a href="#cb6-43" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-44"><a href="#cb6-44" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-45"><a href="#cb6-45" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-46"><a href="#cb6-46" tabindex="-1"></a><span class="co">#> Running base model for layer 3 ... </span></span>
<span id="cb6-47"><a href="#cb6-47" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-48"><a href="#cb6-48" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-49"><a href="#cb6-49" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-50"><a href="#cb6-50" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-51"><a href="#cb6-51" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-52"><a href="#cb6-52" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-53"><a href="#cb6-53" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-54"><a href="#cb6-54" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-55"><a href="#cb6-55" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-56"><a href="#cb6-56" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-57"><a href="#cb6-57" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-58"><a href="#cb6-58" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-59"><a href="#cb6-59" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-60"><a href="#cb6-60" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-61"><a href="#cb6-61" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-62"><a href="#cb6-62" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-63"><a href="#cb6-63" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-64"><a href="#cb6-64" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-65"><a href="#cb6-65" tabindex="-1"></a><span class="co">#> Running base model for layer 4 ... </span></span>
<span id="cb6-66"><a href="#cb6-66" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-67"><a href="#cb6-67" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-68"><a href="#cb6-68" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-69"><a href="#cb6-69" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-70"><a href="#cb6-70" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-71"><a href="#cb6-71" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-72"><a href="#cb6-72" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-73"><a href="#cb6-73" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-74"><a href="#cb6-74" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-75"><a href="#cb6-75" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-76"><a href="#cb6-76" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-77"><a href="#cb6-77" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-78"><a href="#cb6-78" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-79"><a href="#cb6-79" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-80"><a href="#cb6-80" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-81"><a href="#cb6-81" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-82"><a href="#cb6-82" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-83"><a href="#cb6-83" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-84"><a href="#cb6-84" tabindex="-1"></a><span class="co">#> Running base model for layer 5 ... </span></span>
<span id="cb6-85"><a href="#cb6-85" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-86"><a href="#cb6-86" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-87"><a href="#cb6-87" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-88"><a href="#cb6-88" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-89"><a href="#cb6-89" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-90"><a href="#cb6-90" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-91"><a href="#cb6-91" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-92"><a href="#cb6-92" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-93"><a href="#cb6-93" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-94"><a href="#cb6-94" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-95"><a href="#cb6-95" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-96"><a href="#cb6-96" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-97"><a href="#cb6-97" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-98"><a href="#cb6-98" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-99"><a href="#cb6-99" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-100"><a href="#cb6-100" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-101"><a href="#cb6-101" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-102"><a href="#cb6-102" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-103"><a href="#cb6-103" tabindex="-1"></a><span class="co">#> Running base model for layer 6 ... </span></span>
<span id="cb6-104"><a href="#cb6-104" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-105"><a href="#cb6-105" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-106"><a href="#cb6-106" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-107"><a href="#cb6-107" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-108"><a href="#cb6-108" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-109"><a href="#cb6-109" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-110"><a href="#cb6-110" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-111"><a href="#cb6-111" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-112"><a href="#cb6-112" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-113"><a href="#cb6-113" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-114"><a href="#cb6-114" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-115"><a href="#cb6-115" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-116"><a href="#cb6-116" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-117"><a href="#cb6-117" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-118"><a href="#cb6-118" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-119"><a href="#cb6-119" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-120"><a href="#cb6-120" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-121"><a href="#cb6-121" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-122"><a href="#cb6-122" tabindex="-1"></a><span class="co">#> Running base model for layer 7 ... </span></span>
<span id="cb6-123"><a href="#cb6-123" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-124"><a href="#cb6-124" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-125"><a href="#cb6-125" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-126"><a href="#cb6-126" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-127"><a href="#cb6-127" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-128"><a href="#cb6-128" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-129"><a href="#cb6-129" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-130"><a href="#cb6-130" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-131"><a href="#cb6-131" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-132"><a href="#cb6-132" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-133"><a href="#cb6-133" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-134"><a href="#cb6-134" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-135"><a href="#cb6-135" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-136"><a href="#cb6-136" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-137"><a href="#cb6-137" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-138"><a href="#cb6-138" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-139"><a href="#cb6-139" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-140"><a href="#cb6-140" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-141"><a href="#cb6-141" tabindex="-1"></a><span class="co">#> Running stacked model...</span></span>
<span id="cb6-142"><a href="#cb6-142" tabindex="-1"></a><span class="co">#> Running concatenated model...</span></span>
<span id="cb6-143"><a href="#cb6-143" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-144"><a href="#cb6-144" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-145"><a href="#cb6-145" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-146"><a href="#cb6-146" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-147"><a href="#cb6-147" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-148"><a href="#cb6-148" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-149"><a href="#cb6-149" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-150"><a href="#cb6-150" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-151"><a href="#cb6-151" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-152"><a href="#cb6-152" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-153"><a href="#cb6-153" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-154"><a href="#cb6-154" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-155"><a href="#cb6-155" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-156"><a href="#cb6-156" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-157"><a href="#cb6-157" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-158"><a href="#cb6-158" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-159"><a href="#cb6-159" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-160"><a href="#cb6-160" tabindex="-1"></a><span class="co">#> serializing in order to be saved for future R sessions...done</span></span>
<span id="cb6-161"><a href="#cb6-161" tabindex="-1"></a><span class="co">#> Time for model fit : 2.437 minutes </span></span>
<span id="cb6-162"><a href="#cb6-162" tabindex="-1"></a><span class="co">#> ========================================</span></span>
<span id="cb6-163"><a href="#cb6-163" tabindex="-1"></a><span class="co">#> Model fit for individual layers: SL.BART </span></span>
<span id="cb6-164"><a href="#cb6-164" tabindex="-1"></a><span class="co">#> Model fit for stacked layer: SL.nnls.auc </span></span>
<span id="cb6-165"><a href="#cb6-165" tabindex="-1"></a><span class="co">#> Model fit for concatenated layer: SL.BART </span></span>
<span id="cb6-166"><a href="#cb6-166" tabindex="-1"></a><span class="co">#> ========================================</span></span>
<span id="cb6-167"><a href="#cb6-167" tabindex="-1"></a><span class="co">#> R^2 for training data: </span></span>
<span id="cb6-168"><a href="#cb6-168" tabindex="-1"></a><span class="co">#> Individual layers: </span></span>
<span id="cb6-169"><a href="#cb6-169" tabindex="-1"></a><span class="co">#> CellfreeRNA ImmuneSystem Metabolomics Microbiome PlasmaLuminex </span></span>
<span id="cb6-170"><a href="#cb6-170" tabindex="-1"></a><span class="co">#> 0.08548959 0.20491045 0.67114346 0.74893855 0.18314107 </span></span>
<span id="cb6-171"><a href="#cb6-171" tabindex="-1"></a><span class="co">#> PlasmaSomalogic SerumLuminex </span></span>
<span id="cb6-172"><a href="#cb6-172" tabindex="-1"></a><span class="co">#> 0.63949095 0.09200745 </span></span>
<span id="cb6-173"><a href="#cb6-173" tabindex="-1"></a><span class="co">#> ======================</span></span>
<span id="cb6-174"><a href="#cb6-174" tabindex="-1"></a><span class="co">#> Stacked model:0.8120559 </span></span>
<span id="cb6-175"><a href="#cb6-175" tabindex="-1"></a><span class="co">#> ======================</span></span>
<span id="cb6-176"><a href="#cb6-176" tabindex="-1"></a><span class="co">#> Concatenated model:0.6880553 </span></span>
<span id="cb6-177"><a href="#cb6-177" tabindex="-1"></a><span class="co">#> ======================</span></span>
<span id="cb6-178"><a href="#cb6-178" tabindex="-1"></a><span class="co">#> ========================================</span></span>
<span id="cb6-179"><a href="#cb6-179" tabindex="-1"></a><span class="co">#> Weights for individual layers predictions in IntegratedLearner: </span></span>
<span id="cb6-180"><a href="#cb6-180" tabindex="-1"></a><span class="co">#> CellfreeRNA ImmuneSystem Metabolomics Microbiome PlasmaLuminex </span></span>
<span id="cb6-181"><a href="#cb6-181" tabindex="-1"></a><span class="co">#> 0.000 0.000 0.238 0.588 0.000 </span></span>
<span id="cb6-182"><a href="#cb6-182" tabindex="-1"></a><span class="co">#> PlasmaSomalogic SerumLuminex </span></span>
<span id="cb6-183"><a href="#cb6-183" tabindex="-1"></a><span class="co">#> 0.174 0.000 </span></span>
<span id="cb6-184"><a href="#cb6-184" tabindex="-1"></a><span class="co">#> ========================================</span></span></code></pre></div>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a>plot.obj <span class="ot"><-</span> IntegratedLearner<span class="sc">:::</span><span class="fu">plot.learner</span>(fit)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- -->
As before, the multi-omics stacked prediction model leads to a better
cross-validated accuracy than its single-omics counterparts and
concatenation model.</p>
<p>When <code>base_learner='SL.BART'</code>, in addition to point
predictions, we can also generate 1) credible intervals for all
observations, 2) estimated layer weights in the meta model and 3)
feature importance scores.</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>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>weights <span class="ot"><-</span> fit<span class="sc">$</span>weights</span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a></span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a>dataX <span class="ot"><-</span> fit<span class="sc">$</span>X_train_layers</span>
<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a>dataY <span class="ot"><-</span> fit<span class="sc">$</span>Y_train</span>
<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a></span>
<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a></span>
<span id="cb8-8"><a href="#cb8-8" tabindex="-1"></a>post.samples <span class="ot"><-</span> <span class="fu">vector</span>(<span class="st">"list"</span>, <span class="fu">length</span>(weights))</span>
<span id="cb8-9"><a href="#cb8-9" tabindex="-1"></a><span class="fu">names</span>(post.samples) <span class="ot"><-</span> <span class="fu">names</span>(dataX)</span>
<span id="cb8-10"><a href="#cb8-10" tabindex="-1"></a></span>
<span id="cb8-11"><a href="#cb8-11" tabindex="-1"></a><span class="cf">for</span>(i <span class="cf">in</span> <span class="fu">seq_along</span>(post.samples)){</span>
<span id="cb8-12"><a href="#cb8-12" tabindex="-1"></a>post.samples[[i]] <span class="ot"><-</span> <span class="fu">bart_machine_get_posterior</span>(fit<span class="sc">$</span>model_fits<span class="sc">$</span>model_layers[[i]],dataX[[i]])<span class="sc">$</span>y_hat_posterior_samples</span>
<span id="cb8-13"><a href="#cb8-13" tabindex="-1"></a>}</span>
<span id="cb8-14"><a href="#cb8-14" tabindex="-1"></a></span>
<span id="cb8-15"><a href="#cb8-15" tabindex="-1"></a>weighted.post.samples <span class="ot"><-</span><span class="fu">Reduce</span>(<span class="st">'+'</span>, <span class="fu">Map</span>(<span class="st">'*'</span>, post.samples, weights))</span>
<span id="cb8-16"><a href="#cb8-16" tabindex="-1"></a><span class="fu">rownames</span>(weighted.post.samples) <span class="ot"><-</span> <span class="fu">rownames</span>(dataX[[<span class="dv">1</span>]])</span>
<span id="cb8-17"><a href="#cb8-17" tabindex="-1"></a><span class="fu">names</span>(dataY) <span class="ot"><-</span> <span class="fu">rownames</span>(dataX[[<span class="dv">1</span>]])</span></code></pre></div>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="fu">capture.output</span>(temp <span class="ot"><-</span> <span class="fu">caterplot</span>(<span class="fu">t</span>(weighted.post.samples), <span class="at">add =</span> <span class="cn">FALSE</span>))</span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="co">#> character(0)</span></span></code></pre></div>
<p>We show below the 68% and 95% credible intervals obtained from
stacked model for all 51 observations. The filled circle indicates the
posterior median and empty circle indicates the true value of the
observation.</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>temp<span class="ot"><-</span><span class="fu">caterplot</span>(<span class="fu">t</span>(weighted.post.samples),</span>
<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a> <span class="at">horizontal =</span> <span class="cn">FALSE</span>, <span class="at">add =</span> <span class="cn">FALSE</span>)</span>
<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a><span class="fu">points</span>(dataY[temp])</span>
<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a><span class="fu">title</span>(<span class="at">main =</span><span class="st">""</span>, <span class="at">xlab =</span> <span class="st">"Observations"</span>, <span class="at">ylab =</span> <span class="st">"Gestational age (in months)"</span>,</span>
<span id="cb10-5"><a href="#cb10-5" tabindex="-1"></a> <span class="at">line =</span> <span class="cn">NA</span>, <span class="at">outer =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<p><img 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" /><!-- --></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>p3 <span class="ot"><-</span> <span class="fu">recordPlot</span>()</span></code></pre></div>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a></span>
<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a>omicsEye_theme <span class="ot"><-</span> <span class="cf">function</span>() {</span>
<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a><span class="co"># set default text format based on categorical and length</span></span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a> angle <span class="ot">=</span> <span class="dv">45</span></span>
<span id="cb12-5"><a href="#cb12-5" tabindex="-1"></a> hjust <span class="ot">=</span> <span class="dv">1</span></span>
<span id="cb12-6"><a href="#cb12-6" tabindex="-1"></a> size <span class="ot">=</span> <span class="dv">6</span></span>
<span id="cb12-7"><a href="#cb12-7" tabindex="-1"></a> <span class="fu">return</span> (ggplot2<span class="sc">::</span><span class="fu">theme_bw</span>() <span class="sc">+</span> ggplot2<span class="sc">::</span><span class="fu">theme</span>(</span>
<span id="cb12-8"><a href="#cb12-8" tabindex="-1"></a> <span class="at">axis.text.x =</span> ggplot2<span class="sc">::</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">8</span>, <span class="at">vjust =</span> <span class="dv">1</span>, <span class="at">hjust =</span> hjust, <span class="at">angle =</span> angle),</span>
<span id="cb12-9"><a href="#cb12-9" tabindex="-1"></a> <span class="at">axis.text.y =</span> ggplot2<span class="sc">::</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">8</span>, <span class="at">hjust =</span> <span class="dv">1</span>),</span>
<span id="cb12-10"><a href="#cb12-10" tabindex="-1"></a> <span class="at">axis.title =</span> ggplot2<span class="sc">::</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">10</span>),</span>
<span id="cb12-11"><a href="#cb12-11" tabindex="-1"></a> <span class="at">plot.title =</span> ggplot2<span class="sc">::</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">10</span>),</span>
<span id="cb12-12"><a href="#cb12-12" tabindex="-1"></a> <span class="at">plot.subtitle =</span> ggplot2<span class="sc">::</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">8</span>),</span>
<span id="cb12-13"><a href="#cb12-13" tabindex="-1"></a> <span class="at">legend.title =</span> ggplot2<span class="sc">::</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">6</span>, <span class="at">face =</span> <span class="st">'bold'</span>),</span>
<span id="cb12-14"><a href="#cb12-14" tabindex="-1"></a> <span class="at">legend.text =</span> ggplot2<span class="sc">::</span><span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">7</span>),</span>
<span id="cb12-15"><a href="#cb12-15" tabindex="-1"></a> <span class="at">axis.line =</span> ggplot2<span class="sc">::</span><span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">'black'</span>, <span class="at">size =</span> .<span class="dv">25</span>),</span>
<span id="cb12-16"><a href="#cb12-16" tabindex="-1"></a> ggplot2<span class="sc">::</span><span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">'black'</span>, <span class="at">size =</span> .<span class="dv">25</span>),</span>
<span id="cb12-17"><a href="#cb12-17" tabindex="-1"></a> <span class="at">axis.line.x =</span> ggplot2<span class="sc">::</span><span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">'black'</span>, <span class="at">size =</span> .<span class="dv">25</span>),</span>
<span id="cb12-18"><a href="#cb12-18" tabindex="-1"></a> <span class="at">axis.line.y =</span> ggplot2<span class="sc">::</span><span class="fu">element_line</span>(<span class="at">colour =</span> <span class="st">'black'</span>, <span class="at">size =</span> .<span class="dv">25</span>),</span>
<span id="cb12-19"><a href="#cb12-19" tabindex="-1"></a> <span class="at">panel.border =</span> ggplot2<span class="sc">::</span><span class="fu">element_blank</span>(),</span>
<span id="cb12-20"><a href="#cb12-20" tabindex="-1"></a> <span class="at">panel.grid.major =</span> ggplot2<span class="sc">::</span><span class="fu">element_blank</span>(),</span>
<span id="cb12-21"><a href="#cb12-21" tabindex="-1"></a> <span class="at">panel.grid.minor =</span> ggplot2<span class="sc">::</span><span class="fu">element_blank</span>())</span>
<span id="cb12-22"><a href="#cb12-22" tabindex="-1"></a> )</span>
<span id="cb12-23"><a href="#cb12-23" tabindex="-1"></a>}</span>
<span id="cb12-24"><a href="#cb12-24" tabindex="-1"></a></span>
<span id="cb12-25"><a href="#cb12-25" tabindex="-1"></a>myColtmp<span class="ot"><-</span><span class="fu">c</span>(<span class="st">"cornflowerblue"</span>,<span class="st">"darkcyan"</span>,<span class="st">"orchid4"</span>,</span>
<span id="cb12-26"><a href="#cb12-26" tabindex="-1"></a> <span class="st">"brown"</span>,<span class="st">"goldenrod4"</span>,<span class="st">"mistyrose4"</span>,<span class="st">"darkgreen"</span>,<span class="st">"purple"</span>)</span>
<span id="cb12-27"><a href="#cb12-27" tabindex="-1"></a></span>
<span id="cb12-28"><a href="#cb12-28" tabindex="-1"></a></span>
<span id="cb12-29"><a href="#cb12-29" tabindex="-1"></a>VIMP_stack<span class="ot"><-</span> <span class="fu">cbind.data.frame</span>(fit<span class="sc">$</span>weights)</span>
<span id="cb12-30"><a href="#cb12-30" tabindex="-1"></a><span class="fu">colnames</span>(VIMP_stack)<span class="ot"><-</span><span class="fu">c</span>(<span class="st">'mean'</span>)</span>
<span id="cb12-31"><a href="#cb12-31" tabindex="-1"></a>VIMP_stack<span class="sc">$</span>sd <span class="ot"><-</span> <span class="cn">NA</span></span>
<span id="cb12-32"><a href="#cb12-32" tabindex="-1"></a>VIMP_stack<span class="sc">$</span>type<span class="ot"><-</span><span class="st">'stack'</span></span>
<span id="cb12-33"><a href="#cb12-33" tabindex="-1"></a></span>
<span id="cb12-34"><a href="#cb12-34" tabindex="-1"></a><span class="do">###############</span></span>
<span id="cb12-35"><a href="#cb12-35" tabindex="-1"></a><span class="co"># Microbiome #</span></span>
<span id="cb12-36"><a href="#cb12-36" tabindex="-1"></a><span class="do">###############</span></span>
<span id="cb12-37"><a href="#cb12-37" tabindex="-1"></a></span>
<span id="cb12-38"><a href="#cb12-38" tabindex="-1"></a>qq<span class="ot"><-</span>bartMachine<span class="sc">::</span><span class="fu">investigate_var_importance</span>(fit<span class="sc">$</span>model_fits<span class="sc">$</span>model_layers<span class="sc">$</span>Microbiome,<span class="at">plot =</span> <span class="cn">FALSE</span>)</span>
<span id="cb12-39"><a href="#cb12-39" tabindex="-1"></a><span class="co">#> .....</span></span>
<span id="cb12-40"><a href="#cb12-40" tabindex="-1"></a>VIMP_microbiome<span class="ot"><-</span><span class="fu">cbind.data.frame</span>(qq<span class="sc">$</span>avg_var_props, qq<span class="sc">$</span>sd_var_props)</span>
<span id="cb12-41"><a href="#cb12-41" tabindex="-1"></a><span class="fu">colnames</span>(VIMP_microbiome)<span class="ot"><-</span><span class="fu">c</span>(<span class="st">'mean'</span>, <span class="st">'sd'</span>)</span>
<span id="cb12-42"><a href="#cb12-42" tabindex="-1"></a>VIMP_microbiome<span class="sc">$</span>type<span class="ot"><-</span><span class="st">'Microbiome'</span></span>
<span id="cb12-43"><a href="#cb12-43" tabindex="-1"></a></span>
<span id="cb12-44"><a href="#cb12-44" tabindex="-1"></a><span class="do">###############</span></span>
<span id="cb12-45"><a href="#cb12-45" tabindex="-1"></a><span class="co"># Plasma Somalogic #</span></span>
<span id="cb12-46"><a href="#cb12-46" tabindex="-1"></a><span class="do">###############</span></span>
<span id="cb12-47"><a href="#cb12-47" tabindex="-1"></a></span>
<span id="cb12-48"><a href="#cb12-48" tabindex="-1"></a>qq<span class="ot"><-</span>bartMachine<span class="sc">::</span><span class="fu">investigate_var_importance</span>(fit<span class="sc">$</span>model_fits<span class="sc">$</span>model_layers<span class="sc">$</span>PlasmaSomalogic,<span class="at">plot =</span> <span class="cn">FALSE</span>)</span>
<span id="cb12-49"><a href="#cb12-49" tabindex="-1"></a><span class="co">#> .....</span></span>
<span id="cb12-50"><a href="#cb12-50" tabindex="-1"></a>VIMP_PlasmaSomalogic<span class="ot"><-</span><span class="fu">cbind.data.frame</span>(qq<span class="sc">$</span>avg_var_props, qq<span class="sc">$</span>sd_var_props)</span>
<span id="cb12-51"><a href="#cb12-51" tabindex="-1"></a><span class="fu">colnames</span>(VIMP_PlasmaSomalogic)<span class="ot"><-</span><span class="fu">c</span>(<span class="st">'mean'</span>, <span class="st">'sd'</span>)</span>
<span id="cb12-52"><a href="#cb12-52" tabindex="-1"></a>VIMP_PlasmaSomalogic<span class="sc">$</span>type<span class="ot"><-</span><span class="st">'PlasmaSomalogic'</span></span>
<span id="cb12-53"><a href="#cb12-53" tabindex="-1"></a></span>
<span id="cb12-54"><a href="#cb12-54" tabindex="-1"></a></span>
<span id="cb12-55"><a href="#cb12-55" tabindex="-1"></a>VIMP<span class="ot"><-</span><span class="fu">as.data.frame</span>(<span class="fu">rbind.data.frame</span>(VIMP_stack,</span>
<span id="cb12-56"><a href="#cb12-56" tabindex="-1"></a> VIMP_microbiome[<span class="dv">1</span><span class="sc">:</span><span class="dv">20</span>,],</span>
<span id="cb12-57"><a href="#cb12-57" tabindex="-1"></a> VIMP_PlasmaSomalogic[<span class="dv">1</span><span class="sc">:</span><span class="dv">20</span>,]))</span>
<span id="cb12-58"><a href="#cb12-58" tabindex="-1"></a></span>
<span id="cb12-59"><a href="#cb12-59" tabindex="-1"></a></span>
<span id="cb12-60"><a href="#cb12-60" tabindex="-1"></a>VIMP<span class="ot"><-</span><span class="fu">rownames_to_column</span>(VIMP, <span class="st">'ID'</span>)</span>
<span id="cb12-61"><a href="#cb12-61" tabindex="-1"></a></span>
<span id="cb12-62"><a href="#cb12-62" tabindex="-1"></a>p4<span class="ot"><-</span>VIMP <span class="sc">%>%</span></span>
<span id="cb12-63"><a href="#cb12-63" tabindex="-1"></a> <span class="fu">filter</span>(type <span class="sc">==</span> <span class="st">'stack'</span>) <span class="sc">%>%</span></span>
<span id="cb12-64"><a href="#cb12-64" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(mean)) <span class="sc">%>%</span></span>
<span id="cb12-65"><a href="#cb12-65" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">y =</span> mean, <span class="at">x =</span> <span class="fu">reorder</span>(ID,<span class="sc">-</span>mean))) <span class="sc">+</span></span>
<span id="cb12-66"><a href="#cb12-66" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">"identity"</span>, <span class="at">fill =</span> <span class="st">'darkseagreen'</span>) <span class="sc">+</span></span>
<span id="cb12-67"><a href="#cb12-67" tabindex="-1"></a> <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb12-68"><a href="#cb12-68" tabindex="-1"></a> <span class="co">#coord_flip() +</span></span>
<span id="cb12-69"><a href="#cb12-69" tabindex="-1"></a> <span class="fu">omicsEye_theme</span>() <span class="sc">+</span></span>
<span id="cb12-70"><a href="#cb12-70" tabindex="-1"></a> <span class="fu">ylab</span>(<span class="st">'Layer Weights'</span>) <span class="sc">+</span></span>
<span id="cb12-71"><a href="#cb12-71" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">''</span>)</span>
<span id="cb12-72"><a href="#cb12-72" tabindex="-1"></a><span class="co">#> Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.</span></span>
<span id="cb12-73"><a href="#cb12-73" tabindex="-1"></a><span class="co">#> ℹ Please use the `linewidth` argument instead.</span></span>
<span id="cb12-74"><a href="#cb12-74" tabindex="-1"></a><span class="co">#> This warning is displayed once every 8 hours.</span></span>
<span id="cb12-75"><a href="#cb12-75" tabindex="-1"></a><span class="co">#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was</span></span>
<span id="cb12-76"><a href="#cb12-76" tabindex="-1"></a><span class="co">#> generated.</span></span>
<span id="cb12-77"><a href="#cb12-77" tabindex="-1"></a></span>
<span id="cb12-78"><a href="#cb12-78" tabindex="-1"></a></span>
<span id="cb12-79"><a href="#cb12-79" tabindex="-1"></a>p5<span class="ot"><-</span>VIMP <span class="sc">%>%</span></span>
<span id="cb12-80"><a href="#cb12-80" tabindex="-1"></a> <span class="fu">filter</span>(type <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">'Microbiome'</span>, <span class="st">'PlasmaSomalogic'</span>)) <span class="sc">%>%</span></span>
<span id="cb12-81"><a href="#cb12-81" tabindex="-1"></a> <span class="fu">arrange</span>(mean) <span class="sc">%>%</span></span>
<span id="cb12-82"><a href="#cb12-82" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">ID =</span> <span class="fu">str_replace_all</span>(ID, <span class="fu">fixed</span>(<span class="st">"_"</span>), <span class="st">" "</span>)) <span class="sc">%>%</span></span>
<span id="cb12-83"><a href="#cb12-83" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">type =</span> <span class="fu">factor</span>(type,</span>
<span id="cb12-84"><a href="#cb12-84" tabindex="-1"></a> <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">'Microbiome'</span>, <span class="st">'PlasmaSomalogic'</span>),</span>
<span id="cb12-85"><a href="#cb12-85" tabindex="-1"></a> <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">'Microbiome'</span>, <span class="st">'PlasmaSomalogic'</span>))) <span class="sc">%>%</span></span>
<span id="cb12-86"><a href="#cb12-86" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="fu">reorder</span>(ID, <span class="sc">-</span>mean), mean, <span class="at">fill =</span> type)) <span class="sc">+</span></span>
<span id="cb12-87"><a href="#cb12-87" tabindex="-1"></a> <span class="fu">facet_wrap</span>(.<span class="sc">~</span> type, <span class="at">scale =</span> <span class="st">'free'</span>) <span class="sc">+</span></span>
<span id="cb12-88"><a href="#cb12-88" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">"identity"</span>, <span class="at">fill =</span> <span class="st">"lightsalmon"</span>) <span class="sc">+</span></span>
<span id="cb12-89"><a href="#cb12-89" tabindex="-1"></a> <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin=</span><span class="fu">ifelse</span>(mean<span class="sc">-</span>sd<span class="sc">></span><span class="dv">0</span>,mean<span class="sc">-</span>sd,<span class="dv">0</span>), <span class="at">ymax=</span>mean<span class="sc">+</span>sd), <span class="at">width=</span>.<span class="dv">2</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(.<span class="dv">9</span>)) <span class="sc">+</span></span>
<span id="cb12-90"><a href="#cb12-90" tabindex="-1"></a> <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb12-91"><a href="#cb12-91" tabindex="-1"></a> <span class="fu">coord_flip</span>() <span class="sc">+</span></span>
<span id="cb12-92"><a href="#cb12-92" tabindex="-1"></a> <span class="fu">omicsEye_theme</span>() <span class="sc">+</span></span>
<span id="cb12-93"><a href="#cb12-93" tabindex="-1"></a> <span class="fu">theme</span> (<span class="at">strip.background =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span></span>
<span id="cb12-94"><a href="#cb12-94" tabindex="-1"></a> <span class="fu">ylab</span>(<span class="st">'Inclusion proportion'</span>) <span class="sc">+</span></span>
<span id="cb12-95"><a href="#cb12-95" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">''</span>)</span></code></pre></div>
<p>We also illustrate the estimated IntegratedLearner layer weights. We
observe that the layers with highest single-omics predictive accuracy:
microbiome, metabolomics and PlasmaSomalogic are given the most weight
in the stacked model. Furthermore, we highlight the feature importance
scores of top 20 features of microbiome and PlasmaSomalogic layer which
highlights several features that agree with known biology.</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a><span class="fu">plot_grid</span>(p4,</span>
<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a> <span class="at">ncol =</span> <span class="dv">1</span>,</span>
<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a> <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">'Estimated IntegratedLearner layer weights'</span>),</span>
<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a> <span class="at">label_size =</span> <span class="dv">8</span>, <span class="at">vjust =</span> <span class="fl">0.1</span>)<span class="sc">+</span></span>
<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">plot.margin =</span> <span class="fu">unit</span>(<span class="fu">c</span>(<span class="fl">0.5</span>,<span class="fl">0.5</span>,<span class="fl">0.5</span>,<span class="fl">0.5</span>), <span class="st">"cm"</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAGACAYAAAByeNBuAAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAGAAAAAAIttrJEAAEAASURBVHgB7d0JvFzj/fjxJ9YQBBFbNLYGTSyxhFiCWNrY16jaQixVS4kfSqkgRany87cVtbaWhraoNaklBA2NLfY9YgkSIQkiyPmfz9PfGXPnnrn3ZnJncpfP83old+bMOWdm3nNmzvc8z/d5ng5JWoJFAQUUUEABBRRQQIEaCcxTo+fxaRRQQAEFFFBAAQUUiAIGoB4ICiiggAIKKKCAAjUVMACtKbdPpoACCiiggAIKKGAA6jGggAIKKKCAAgooUFMBA9CacvtkCiiggAIKKKCAAgagHgMKKKCAAgoooIACNRUwAK0pt0+mgAIKKKCAAgooYADqMaCAAgoooIACCihQUwED0Jpy+2QKKKCAAgoooIACBqAeAwoooIACCiiggAI1FTAArSm3T6aAAgoooIACCihgANqOj4HBgweHAw44oM6/iy66qFGRsWPHxnVee+21uO3rr7/e6DaNrfDNN9+EcePGNbZancePO+64cP3119dZ9t1338XXdNttt9VZnneH55s5c2beQ826bHaf59FHH43vYerUqWHUqFHhl7/8Zfjss8+a9TU1986a81ho7tdWvL+mvs7f/e534fe//33xpoXbkyZNCu+++27hvjcUUEABBWZfwAB09s3azBY33nhjGDNmTCD4y/59++23Db6/448/Phx22GFxHbb55JNP4rYNbtSEBzfeeONw3XXXNWHN71f5xz/+EV//90tCmDVrVvjzn/8cnnnmmeLF9W7ffvvtYd111w1ff/11vceac0Elz/PWW2/F9zBjxozw3HPPhYsvvjh88cUXzfmymn1fHAe487cll6YesyNHjgz/+te/6r2V9957L/To0SN+LvUedIECCiigQJMF5mvymq7YJgUGDBgQ8mo9qeH5+9//HqgxWn/99WON3MsvvxzuvvvuMGXKlBgU7bzzzmGdddYJnTp1Cs8//3y46aabwh577BEDyR/96Efhpz/9aTj//PNDhw4dwgknnBC6dOkSAxTWe+GFF8JKK60UjjrqqPDQQw8F9j3PPPPE/e+www7ho48+CldeeWWYNm1a+MUvfhFWXnnl6P/UU0+FW2+9Neyzzz5N+jxOPvnkwP4efvjh8MEHH4RDDz00BhCXX355oLZ06NCh4ZxzzomBK6/r6aefDnvttVfYYost4v6nT58err322jBhwoSw0047xdf3m9/8JtZMvvHGG2H++ecP77//fvjtb38bCFruu+++uN/tttsubLrppqH0eRZYYIFw//33hzvuuCNstNFGYdCgQdEnex6s5p133ia9tyRJcvf14osvhr/97W/xdfHZ8Z4JvE477bTQr1+/cM8994T9998/1jgvueSSgZrWJ554InAs7L777vG58/zZrvQ9l3uhpRY877Bhw+JzYMv7xWzXXXcNffv2ja/lmmuuCUsvvXSs8eWYKvd81AZTQ3nggQeGNdZYI9ZULrvssvE9cSzxWZ9xxhnhq6++isdk8We6yCKLFI5ZXjsB5R//+MewxBJLhNVWWy2+vyFDhhTeFhdFjz/+eNhxxx0DxzufJ89PzfsPf/jDeOyXfk84JiwKKKCAAo0IpCcxSzsVSIOhJD3pJj/72c8K/x544IGo0adPnyQ96SbnnntustRSSyWXXXZZ8u9//ztZccUVkzRoSdIgLBk9enSSHl7JY489ltxyyy3xdhp0JYcccki83a1bt+SYY45JFl100SQNtOJ+0wAn6dWrV3LSSSclnTt3Tn7+858n6Uk+WXDBBZM0aE3SIDD5+OOP4/1ddtkl2XbbbZM0cE3SoDdJA7e43zRATDbffPMkDViTNDit8+mlTepxnV//+tdxeRrQJWlQk/zP//xPssoqqyTLL7988vnnnycbbrhhXG/vvfdO0kAlWW+99ZLevXvH185+06bvuP0mm2yS/OAHP0iOOOKIZJlllonb8PrSmuD4Gtdaa624TVrjmqSBY3LkkUcmaSCX8LyvvPJKvedJ0wbifo4++uhk8cUXT9KgLD5PWgOcrLDCCvH9pMFQfJ40CEzSi4N4Ow2U6rxP7uTtK60pja93++23j/vi87n55puTNOCL+8G8f//+8bPkbxroxc9myy23jI+/+uqrZf1L33PxCyo+FvIs0iA9WvB5UnhNvLY333wzSYPxhGPx8MMPT1ZfffVk6623jus09HzpxUty6qmnJpMnT47HQffu3eM2HLO8d0reZ1r8OtPAO+nYsWOSXggk++23X7LwwgsnPXv2jNtutdVWyXzzzZccdNBB8Rjk8+S18hp53dilgW2S9z2JO/A/BRRQQIEGBUKDj/pgmxbgpJ/WICVpbVjh34MPPhjfMydvTvIEbmkzfcFh4MCB8cTOguKTeRaAsi4BHSdpgk/KT37yk4QAi0KA+OyzzyZprVPcfxZsdO3aNQZUrMNjBKQEw3feeWfcV1oLGU/+aa0TqySffvppkwNQgj1KWtMZ98Xru/TSS+NtghACRV7vJZdckjzyyCNJWtsag7K0xjQGkn/5y1/i9ieeeGJcLwtACUrSfMD4GP8RJI4YMaIQgKe5nHWeJ00PiAHwwQcfHJ+HYJyAvvR5jj322Pg8DQWg5fbF6yDA5qIgrSWMQRRBbhaApvmkrBILQRQBNCWtAY3Pee+995b1JyAsfc9x4/S/4mOBZXkWae1hfD1pM32S1pQnm222Wdyci4C0pjaanHnmmfF1vP322zHIL/d8GLFNmusbL1BYL61Vj0Hkn/70p7KfafHrzI7ZLLjnoqQ4AF1zzTXj63vyySfja+ICKK1djrc5LinlvifxQf9TQAEFFCgrYA5oGnm05/LjH/843HDDDYV/aVASOcjno9mUPFGaiunw05SS1hKGtFYprkqzKCWtAQ1Zbum+++4bm2HTmraQ1gDGpu+4UtF/NCGnAUVsguf5acqniZS0gOWWWy6umdbkBZpTm1Ky17HYYovF1Wl6Ly48H4Um3zQwDWkgEtKauPh86TcnPjePk0JQXNhftowm7LRmN1xwwQUhex7yUYtLGjTHNADyOnkemmrTmtzYDMzzZO8trTku3iz3drl9pTWCsUmbtAWa3XmO4teRpTJkO82zKefPNsXvOdtH6d9yFmlNe0hrFWOuaBroxrQOtuX5yB3FhNt83rx2Srnn22233WK6BMcHzeOkgpBOQU5vWnMe98P2pZ8py7LyzjvvxHQHjk9K9llmj2efB+kAlOw1ZY/zt9LvSfE+vK2AAgq0RwED0Pb4qRe95/HjxweCgewfPbDJzyPPkeCIoC+twSx09iGgSWvYAtvlFQLHrJDTWVzoSENuYtpMHYMFnicLTNlvWrMYcz/TWtFAB5y02TuktagxpzKtjYo5eOTzpbWMgXxBchebUvJyKnk+CgEPgTbrpM3tMbeVAJWAhvxJAt0LL7wwpDVe4eqrr67zdORzZoWcTrbj/a266qpxMe+t+HkIuNNm/hhokktIgLP22muHDTbYIN6+4oorwksvvRSGDx+e7bbwN62ZLnxG5FcSLOXtixxZ8mkJ5gjECMgyY3ZW/Jq5n2dTzj9ve5aVlnIWWJJjSqBIUJzWpsdNeT4K+cJcABF0pjXDcVnp640L0//Ir01TQwId0dg+bdqP9nyWLE9TCnI/02x7/rIdn9npp58eA0nyR4tL8bGcLc8+T45/cn/LfU+y9f2rgAIKKJAvUDdCyF/HpW1YgGAhzZkr/Etz3GLNIh1FODFT+0NgSIcRCkFpmgsXT/izy8K+CCrTZtZAJ6U0tzLui9o/9ps2dccTOp1hqAVL8/DCNttsE+goQ1BHwEJHIF4vHYPYR6Ulzd0LCy20UAxkCHbPPvvs+B4JCumAQzBKbR1BYdpEH+iYQtBDyYKQ4uemZpfAh5paOiIRODE8VfHz0AmKoX3SJt34nujQhTOBDkEuywl66QxTWhguK/uc9txzz/hw3r4I4BhRALs0rzF2uJrdYbLK+Ze+pnL3y1mwfppTGTuW8TkSkFPS5vQYUHM8ENBhVi7wjBuk/xE40ymIQiDJcULJOlFxzOR9pnGl//sP6zStIlyXdjS66qqrQpoSkPvZFm+T5prG4DhN64gd0cp9T4q38bYCCiigQH2BDjTO11/sEgX+K0AtY9aknJnQC5jmbwK0Sgo1nwR/pbVvNB8XN4PSi5la1DQftM7TsH1Tm9/rbFhy58svvyQHOgbZPERtGLW02fvl+dOOLrE2jYCJgJygr6EhkfK8Sp+H52IkAYLV4kJNJa+h9P0Wr5N3O29f1FJT4zgnpZx/U/eZZ8FFBj3w77rrrjg6QfG+OK54zXk1j8Xrzc7t0s+0eFvGZ2XkA1ID0lzYGLRzXNJs31Bhn7y34s8v7702tA8fU0ABBdq7gAFoez8CfP8NChx88MGxuZ9ggwCJ5mP+WWZfgGZyxtakpjHt7NWsgebsv5oQJyEg1YKhpdIe8DH4ZwIDaoAtCiiggALVFTAAra6ve28DAqQcMPsNza9Zx5Q28LZq/hYYYJ8gnqCvtPa75i/m/56QWmdSLMiVZYD5rPZ7br0en1cBBRRoLwIGoO3lk/Z9KqCAAgoooIACLUTATkgt5IPwZSiggAIKKKCAAu1FwAC0vXzSvk8FFFBAAQUUUKCFCLTrAJS5qRnQ26KAAgoooIACCihQO4F2HYCm0/3lzm5SO36fSQEFFFBAAQUUaH8C7ToAbX8ft+9YAQUUUEABBRSY+wKVjSQ+l173tGnTArPHMKMMY/X17Nmz3ithuJxbb701Lmed0rmv623gAgUUUEABBRRQQIGaCrSqGtDzzjsvjtfHVH3kb06cOLEO1jfffBOYIm+NNdYI6667bpzFhvH9LAoooIACCiiggAItR6DV1IAy3SADghN4Ut55550wYsSIwBzZWRk1alRYb731AvNhU5i9hqC0eGpDpkWcNWtWfJwp9SwKKKCAAgoooIACtRVoNQHohx9+GJZffvmCDrfHjh1buM+Nd999N86wcuSRR8bAc+DAgWH11Vevs85WW20V5+Fm4ZzOlV1nx95RQAEFFFBAAQUUaJJAqwlAp0+fXqcmc/75549zORe/y6lTp4YxY8aESy+9NCRJEo466qiwwQYbxJrQbL3TTjutsN0NN9yQLfavAgoooIACCiigQI0EWk0A2qVLl0CAmRU6JC277LLZ3fiXJvfNNtssLLXUUvE+wScBKZ2RsrL99ttnN8Odd95ZuO0NBRRQQAEFFFBAgdoItJpOSN27dw8ff/xxoJc7OZyjR48OvXr1ikrkdX777bcx//PFF18M5HayzgsvvBBWXHHF2kj6LAoooIACCiiggAJNEmg1NaA0uQ8ZMiQMHTo0zJgxIw7BtPHGG8c3yfKDDjoo9O3bN/Tu3Tv8/Oc/jzmgm2yySfjRj37UJAhXUkABBRRQQAEFFKiNQIc0VzKpzVM137MQgHbs2LHsDrOhl4p7v+etPHjw4HDOOeeEZZZZJu/h3GVD7/hN7nIXfi9wxi7/Hang+yXeUkABBRRQQAEFvhdoNTWg37/k0GDwyXqNBZ7F+/K2AgoooIACCiigQG0FWk0OaG1ZfDYFFFBAAQUUUECBagkYgFZL1v0qoIACCiiggAIK5AoYgOayuFABBRRQQAEFFFCgWgIGoNWSdb8KKKCAAgoooIACuQIGoLksLlRAAQUUUEABBRSoloABaLVk3a8CCiiggAIKKKBAroABaC6LCxVQQAEFFFBAAQWqJWAAWi1Z96uAAgoooIACCiiQK2AAmsviQgUUUEABBRRQQIFqCRiAVkvW/SqggAIKKKCAAgrkChiA5rK4UAEFFFBAAQUUUKBaAgag1ZJ1vwoooIACCiiggAK5AgaguSwuVEABBRRQQAEFFKiWgAFotWTdrwIKKKCAAgoooECugAFoLosLFVBAAQUUUEABBaolYABaLVn3q4ACCiiggAIKKJArYACay+JCBRRQQAEFFFBAgWoJGIBWS9b9KqCAAgoooIACCuQKGIDmsrhQAQUUUEABBRRQoFoCBqDVknW/CiiggAIKKKCAArkCBqC5LC5UQAEFFFBAAQUUqJaAAWi1ZN2vAgoooIACCiigQK6AAWguiwsVUEABBRRQQAEFqiVgAFotWfergAIKKKCAAgookCtgAJrL4kIFFFBAAQUUUECBagkYgFZL1v0qoIACCiiggAIK5AoYgOayuFABBRRQQAEFFFCgWgIGoNWSdb8KKKCAAgoooIACuQIGoLksLlRAAQUUUEABBRSoloABaLVk3a8CCiiggAIKKKBAroABaC6LCxVQQAEFFFBAAQWqJWAAWi1Z96uAAgoooIACCiiQK2AAmsviQgUUUEABBRRQQIFqCRiAVkvW/SqggAIKKKCAAgrkChiA5rK4UAEFFFBAAQUUUKBaAgag1ZJ1vwoooIACCiiggAK5AgaguSwuVEABBRRQQAEFFKiWgAFotWTdrwIKKKCAAgoooECugAFoLosLFVBAAQUUUEABBaolYABaLVn3q4ACCiiggAIKKJArYACay+JCBRRQQAEFFFBAgWoJGIBWS9b9KqCAAgoooIACCuQKGIDmsrhQAQUUUEABBRRQoFoCBqDVknW/CiiggAIKKKCAArkCBqC5LC5UQAEFFFBAAQUUqJaAAWi1ZN2vAgoooIACCiigQK6AAWguiwsVUEABBRRQQAEFqiVgAFotWfergAIKKKCAAgookCtgAJrL4kIFFFBAAQUUUECBagkYgFZL1v0qoIACCiiggAIK5AoYgOayuFABBRRQQAEFFFCgWgIGoNWSdb8KKKCAAgoooIACuQIGoLksLlRAAQUUUEABBRSoloABaLVk3a8CCiiggAIKKKBAroABaC6LCxVQQAEFFFBAAQWqJWAAWi1Z96uAAgoooIACCiiQK2AAmsviQgUUUEABBRRQQIFqCRiAVkvW/SqggAIKKKCAAgrkChiA5rK4UAEFFFBAAQUUUKBaAgag1ZJ1vwoooIACCiiggAK5AgaguSwuVEABBRRQQAEFFKiWgAFotWTdrwIKKKCAAgoooECugAFoLosLFVBAAQUUUEABBaolYABaLVn3q4ACCiiggAIKKJArYACay+JCBRRQQAEFFFBAgWoJtIgAdPLkyU16f9OmTQu33HJLuOCCC8JLL73U4DZ33313ePbZZxtcxwcVUEABBRRQQAEFai8wVwLQq666Kpx99tnx3e6///5hqaWWCquvvnqYOHFigwLnnXde+Prrr0OfPn3CsGHDyq7/6quvhvPPPz+8//77De7PBxVQQAEFFFBAAQVqL1DzAPT1118Pv/rVr8Jaa60VXnzxxXDjjTeGhx9+OGy11VbhjDPOKCswZcqU8Oabb4ZBgwaFfv36he233z6MGDGi3vozZswIF110Uejfv3+9x1hAzejf//73+I99WhRQQAEFFFBAAQVqKzBfbZ8uhKeffjoMGDAg7LTTTuGss84KG2+8cdhiiy1Cx44dw/HHH1/25Xz44Ydh+eWXLzzO7bFjxxbuZzcuvfTSsPfee4fnn38+W1TnLzWnWeDZuXPnOo95RwEFFFBAAQUUUKD6AjWvASXQ/Pjjj+M7u/3228Puu+8ebz/zzDOxVrTcW54+fXpYcMEFCw/PP//8YebMmYX73Hj88cfjss0337zO8uI7Dz30UOC5+NezZ8/ih7ytgAIKKKCAAgooUAOBmteAbrPNNuHYY48Nq622Wvj0009jbeXVV18daz/vuOOOsm+5S5cuYerUqYXH6ZC07LLLFu5z4/LLLw9LLrlkOPnkk8Nbb70Va1vZrm/fvoX1FlpoocLteeapefxdeG5vKKCAAgoooIAC7VWg5gFop06dwn/+859ATeRGG20UunXrFtZcc80wbty4sPLKK5f9HLp37x5rTidNmhSDzNGjR4edd945rv/ll1+GBRZYIHZs+vbbb+Oym266Ke7bWs6ypD6ggAIKKKCAAgrMFYGaVwHSM33MmDFhzz33DD/4wQ/imyYQJYi85ppryiLQ5D5kyJAwdOjQcOihh8ae8+SPUlhOUMv+CGL5R35n165dw2KLLVZ2nz6ggAIKKKCAAgooUHuBmtWAvvbaa+G9994Lzz33XKCpnVzQrCRJEoYPHx4++OCDMHjw4Gxxvb80pfOPnu7F219xxRX11j3qqKPqLXOBAgoooIACCiigwNwXqFkA+tlnn4V99903dhKitnOfffYpvHtyMamtpFd8U0px8NmU9V1HAQUUUEABBRRQoOUI1CwA3XDDDQNDKTE7EeNwnnnmmS1HwVeigAIKKKCAAgooUDOBmgWg2Tvq3bt34J9FAQUUUEABBRRQoH0K1DwAhZlcUGYxGj9+fCD/Myu9evUKP/3pT7O7/lVAAQUUUEABBRRogwI1D0AnT54ch1/q0KFD7FA077zzFlgZw9OigAIKKKCAAgoo0LYFah6APvroo2HxxReP88C3bVrfnQIKKKCAAgoooECeQM3HAV144YUDg8pbFFBAAQUUUEABBdqnQM0C0C+++CIwfeYGG2wQpkyZEq688sqYC8qy7B/je1oUUEABBRRQQAEF2rZAzZrg6WBEp6OsMBtSaRk4cGAckL50ufcVUEABBRRQQAEF2o5AzQLQxx57LHz33XcNytE8b1FAAQUUUEABBRRo2wI1C0C7desWJd9+++1w1VVX1VOlV3ynTp1Cjx49wq677hqY+92igAIKKKCAAgoo0PYEahaAZnSzZs0Kd911V3j55ZfDJptsErp06RIefPDB8PXXX4etttoq/PGPfww33HBD+Mc//hHmm6/mLy97mf5VQAEFFFBAAQUUqJJAzTohZa+fed/ff//98M4774RRo0bFaTknTZoUmKqTueJfeeWVmCv65JNPZpv4VwEFFFBAAQUUUKANCdQ8ACWw3HzzzUPWJI8lNZ177bVXrAklD5Sa0Oeff74NMftWFFBAAQUUUEABBTKBmgegDMN0//33h7feeit7DYEhmv785z+HH/7wh4Em+rFjxzpffEHHGwoooIACCiigQNsSqHkAuuqqq4ZjjjkmBpvrr79+6N+/f2AKTprmjzrqqHD44YfHILRPnz5tS9p3o4ACCiiggAIKKBAF5kovn3POOSfsvvvugaGZvvnmm3DFFVeE1VZbLb6gU0891ZmSPDgVUEABBRRQQIE2LFCzAHT69OlhgQUWCPPOO29scl999dUD/7IyderUOPSS03RmIv5VQAEFFFBAAQXapkDNmuDXXHPNcMEFF4TRo0eHzp075/4bNGhQ21T2XSmggAIKKKCAAgoUBGpWA/rEE0+ERRZZJNaCvvvuu4UXUHzDmZCKNbytgAIKKKCAAgq0TYGaBaDLLbdcQXDxxRcP5557bnjkkUfCHnvsEVZYYYXQtWvXODxTYSVvKKCAAgoooIACCrRJgZo1wWd63377bdh2223Do48+GhZddNEwefLkmBe6ww47hPvuuy9bzb8KKKCAAgoooIACbVSg5gEoged7770XRo4cGYdgwpW53y+77LJw2223tVFm35YCCiiggAIKKKBAJlDzAHTixIlx2k16xBeXpZdeOrz66qvFi7ytgAIKKKCAAgoo0AYFah6AZjMhjRkzpsD5ySefhPPPPz84+HyBxBsKKKCAAgoooECbFahZJ6RMsEePHuH0008Pm266acwBpec7A9MzJujw4cOz1fyrgAIKKKCAAgoo0EYFah6A4njCCSeEnXbaKfaCnzJlSpwFifvzzTdXXk4b/Wh9WwoooIACCiigQMsUqFnEN2LEiNjEvsQSS0SJNdZYI/DPooACCiiggAIKKNC+BGqWA3r88cfHsT779u0bTjvttDgPPEMyWRRQQAEFFFBAAQXal0DNAtDnnnsu8O+AAw4Ir732Whg4cGDo0qVL2G233cLll18e3nzzzfYl77tVQAEFFFBAAQXaqUDNAtAOHTqEXr16hSOOOCLccsst4YMPPgj/+c9/wo477hgHpe/Zs2c49NBD2+nH4NtWQAEFFFBAAQXaj0DNckCLSadOnRr+9a9/hVGjRgXmiGf8z/79+4cBAwYUr+ZtBRRQQAEFFFBAgTYoULMA9Pnnnw933313nG6ToHO11VYLP/nJT8Jvf/vbOAd8x44d2yCvb0kBBRRQQAEFFFCgVKBmAejOO+8c53w/8cQTw0033RS6detW+lq8r4ACCiiggAIKKNAOBGqWAzp48ODAoPP0hj/88MPDJZdcEt544412QOxbVEABBRRQQAEFFCgWqFkAytBL48aNCy+//HLYZZddwsMPPxyYlnPVVVeNHZPuuOOOMG3atOLX5m0FFFBAAQUUUECBNihQswA0s1thhRXCIYccEm677bYwadKkcP3114cvv/wy7LXXXuHggw/OVvOvAgoooIACCiigQBsVqFkOaOY3Y8aM8NRTT4XRo0fHwejpkLTIIouE/fbbL+yzzz7Zav5VQAEFFFBAAQUUaKMCNQtAhw0bFu65557w9NNPh06dOsVhl3bYYYdw4YUXhh49erRRXt+WAgoooIACCiigQKlAzQJQOhyR+3nxxReH9dZbL8wzT81b/0vfu/cVUEABBRRQQAEF5oJAzQJQcj0tCiiggAIKKKCAAgpYDekxoIACCiiggAIKKFBTAQPQmnL7ZAoooIACCiiggAI1D0AnT54c80CTJFFfAQUUUEABBRRQoB0K1DwAHTVqVDjvvPNChw4d2iG3b1kBBRRQQAEFFFCg5gFov379QteuXcM111wTJkyYEL766qvCv5kzZ/qJKKCAAgoooIACCrRxgZoHoK+//np466234qxH3bt3j/PDM0c8/xiM3qKAAgoooIACCijQtgVqNgxTxsgYoAxGn1eYEcmigAIKKKCAAgoo0LYFah6AduzYMayyyiptW9V3p4ACCiiggAIKKFBWoOYBKK9k2rRp4dxzzw2PPPJI2GOPPcIKK6wQ80I333zzsi/UBxRQQAEFFFBAAQXahkDNc0C//fbbsO2224ZHH300LLroooFhmeadd97AvPD33Xdf21D1XSiggAIKKKCAAgqUFah5AErg+d5774WRI0eG/v37xxe26667hssuuyzcdtttZV+oDyiggAIKKKCAAgq0DYGaB6ATJ04MG264YVhggQXqCC699NLh1VdfrbPMOwoooIACCiiggAJtT6DmAegGG2wQ7r///jBmzJiC5ieffBLOP//80KdPn8IybyiggAIKKKCAAgq0TYGad0Lq0aNHOP3008Omm24ac0AZ//Occ84Jq6++ehg+fHjbVPZdKaCAAgoooIACChQEah6Ajh8/PvTu3Ts8//zzYfTo0WHKlClhtdVWCzvttFOYb76av5wChDdqJzD0jt/U7sla4TOdscuwVviqfckKKKCAAgo0XaDmEd8XX3wRBg8eHOeC33///cOgQYNiANr0l+yaCiiggAIKKKCAAq1ZoOY5oD179gzUgl577bWxN/z6668fNtlkk3DllVeGzz//vDVb+toVUEABBRRQQAEFmiBQ8wCU1zTPPPOErbfeOlx//fXho48+CjvvvHMYMmRIWG655WLt6CuvvNKEl+4qCiiggAIKKKCAAq1RYK4EoEBNmDAhzobUt2/fcPbZZ4d99tkn3HnnnWGllVaKNaIvvvhia/T0NSuggAIKKKCAAgo0IlDzHNB33nknHHTQQXEmJKbePOGEE+J0nPSGp2yzzTbhiSeeCKNGjQq9evVq5OX7sAIKKKCAAgoooEBrE6h5ADp16tSwxRZbhGuuuSasvPLKuV4My0RNqEUBBRRQQAEFFFCg7QnUPABde+21A/8aKgzTZFFAAQUUUEABBRRomwI1D0BhfPPNN8Ndd90VGJKJMmvWrHi7U6dO4dRTT43L/E8BBRRQQAEFFFCgbQrUPACdNGlSnHKzS5cuYdq0aWHFFVcMkydPDu+//364+eab26ay70oBBRRQQAEFFFCgIFDzAJQORt26dQvjxo0LxxxzTJwV6cADDwyHHXZYDEgLryznBgHr3XffHT744IMwYMCAwJiipeXtt98ODzzwQJh//vnDtttuG5ZffvnSVbyvgAIKKKCAAgooMBcFaj4M0/Tp08O6664b3zJ/H3vssTgr0pFHHhluueWWBinOO++88PXXX8ca1GHDhoWJEyfWWZ8OTqecckpYc801wyqrrBKOPfbYuH6dlbyjgAIKKKCAAgooMFcFah6AUmvJHPAEj+uss054+OGHw7fffhtrNRuSYM54ckeZurNfv35h++23DyNGjKizCWOL8jhji7IOPemfeeaZOut4RwEFFFBAAQUUUGDuCtS8CZ6gc7PNNgvbbbddePrpp0Pnzp3jeJ8Ej5dccklZjQ8//LBOczpN62PHjq2zPuOGZmOHfvLJJ4EZlXr06FFnneOPP77Q1M+UoBYFFFBAAQUUUECB2grUPADl7d1www1xPvgOHTqEkSNHhptuuinmghKYlis03S+44IKFh8nxnDlzZuF+8Q2m9/zVr34VjjvuuEBnp+Ly3XffxRpXliVJUvyQtxVQQAEFFFBAAQVqIFDzJvjsPdH7nbLkkkuGo446KtaG0hmpXCGQJMczK3RIWnbZZbO7hb90QiL38/DDDw9bbrllYXl248ILLwxXX311/Odg95mKfxVQQAEFFFBAgdoJzLUAtPQt0rnoyy+/LF1cuN+9e/fw8ccfB4ZxYtxQ8kiz5na2I4+Ux0466aRw2mmnxTzQwsbeUEABBRRQQAEFFGgxAnOlCb6Sd0+T+5AhQ8LQoUPDjBkz4hBMG2+8cdwVy5lffsyYMTEIPfnkkwtPwVBP/fv3L9z3hgIKKKCAAgoooMDcFWg1AShM9G7nHwFox44dC3JXXHFFvM1jBJwWBRRQQAEFFFBAgZYrULMA9KmnnoqBYzkKcjebWoqDz6Zu43oKKKCAAgoooIACLUOgZgHowIEDY8/3ht4261gUUEABBRRQQAEF2rZAzQLQ119/vVHJeeZpMX2iGn2trqCAAgoooIACCihQmUDNAlA6EVkUUEABBRRQQAEFFLDK0WNAAQUUUEABBRRQoKYCNasBrem78skUaOMCQ+/4TRt/h3P29s7YZdic7cCtFVBAAQWqKmANaFV53bkCCiiggAIKKKBAqYABaKmI9xVQQAEFFFBAAQWqKmAAWlVed66AAgoooIACCihQKmAAWirifQUUUEABBRRQQIGqChiAVpXXnSuggAIKKKCAAgqUChiAlop4XwEFFFBAAQUUUKCqAgagVeV15woooIACCiiggAKlAgagpSLeV0ABBRRQQAEFFKiqgAFoVXnduQIKKKCAAgoooECpgAFoqYj3FVBAAQUUUEABBaoqYABaVV53roACCiiggAIKKFAqYABaKuJ9BRRQQAEFFFBAgaoKGIBWldedK6CAAgoooIACCpQKGICWinhfAQUUUEABBRRQoKoCBqBV5XXnCiiggAIKKKCAAqUCBqClIt5XQAEFFFBAAQUUqKqAAWhVed25AgoooIACCiigQKmAAWipiPcVUEABBRRQQAEFqipgAFpVXneugAIKKKCAAgooUCpgAFoq4n0FFFBAAQUUUECBqgoYgFaV150roIACCiiggAIKlAoYgJaKeF8BBRRQQAEFFFCgqgIGoFXldecKKKCAAgoooIACpQIGoKUi3ldAAQUUUEABBRSoqoABaFV53bkCCiiggAIKKKBAqYABaKmI9xVQQAEFFFBAAQWqKmAAWlVed66AAgoooIACCihQKmAAWirifQUUUEABBRRQQIGqChiAVpXXnSuggAIKKKCAAgqUChiAlop4XwEFFFBAAQUUUKCqAgagVeV15woooIACCiiggAKlAgagpSLeV0ABBRRQQAEFFKiqgAFoVXnduQIKKKCAAgoooECpgAFoqYj3FVBAAQUUUEABBaoqYABaVV53roACCiiggAIKKFAqYABaKuJ9BRRQQAEFFFBAgaoKGIBWldedK6CAAgoooIACCpQKGICWinhfAQUUUEABBRRQoKoCBqBV5XXnCiiggAIKKKCAAqUCBqClIt5XQAEFFFBAAQUUqKqAAWhVed25AgoooIACCiigQKmAAWipiPcVUEABBRRQQAEFqipgAFpVXneugAIKKKCAAgooUCpgAFoq4n0FFFBAAQUUUECBqgoYgFaV150roIACCiiggAIKlAoYgJaKeF8BBRRQQAEFFFCgqgIGoFXldecKKKCAAgoooIACpQIGoKUi3ldAAQUUUEABBRSoqoABaFV53bkCCiiggAIKKKBAqYABaKmI9xVQQAEFFFBAAQWqKmAAWlVed66AAgoooIACCihQKmAAWirifQUUUEABBRRQQIGqChiAVpXXnSuggAIKKKCAAgqUChiAlop4XwEFFFBAAQUUUKCqAgagVeV15woooIACCiiggAKlAgagpSLeV0ABBRRQQAEFFKiqgAFoVXnduQIKKKCAAgoooECpgAFoqYj3FVBAAQUUUEABBaoqYABaVV53roACCiiggAIKKFAqYABaKuJ9BRRQQAEFFFBAgaoKzFfVvTfzzqdNmxbuvvvu8MEHH4QBAwaEnj171nuGCRMmhHvuuSfMmjUrDBw4MCy11FL11nGBAgoooIACCiigwNwTaFU1oOedd174+uuvQ58+fcKwYcPCxIkT68h99dVX4bTTTgs9evSIgecJJ5wQkiSps453FFBAAQUUUEABBeauQKsJQKdMmRLefPPNMGjQoNCvX7+w/fbbhxEjRtTR+/e//x1rRbfaaqtY+7nEEkuEF154oc4633zzTZg5c2b8Z3Bah8Y7CiiggAIKKKBATQRaTRP8hx9+GJZffvkCCrfHjh1buM8NmuZL16GWdK211iqsR/BKMEvp3LlzYbk3FFBAAQUUUEABBWoj0GoC0OnTp4cFF1ywoDL//PPHWszCgvQG63Tp0qWwKG+dk08+OTbjs9JNN91UWLepN87YZVhTV3W9MgIaloGZjcUazgaWqyqggAIKtDiBVhOAElhOnTq1AEiHpGWXXbZwnxtNWWeXXXYpbHPfffcVbntDAQUUUEABBRRQoDYCrSYHtHv37uHjjz8OkyZNij3cR48eHXr16hWVvvzyy/Dtt9+G3r17hzFjxgTyPL/44ovw0ksvhVVWWaU2kj6LAgoooIACCiigQJMEWk0NKM3pQ4YMCUOHDg0zZsyInY023njj+CZZftBBB4W+ffsGOiAdc8wxsbZ03333DXREsiiggAIKKKCAAgq0HIEOaU/wVjdOEQFox44dyypSA9qhQ4cw33wNx9eDBw8O55xzTlhmmWXK7ssHFFBAAQUUUEABBZpXoOEIrXmfq9n21lDwyZNQW2pRQAEFFFBAAQUUaJkCrSYHtGXy+aoUUEABBRRQQAEFZlfAAHR2xVxfAQUUUEABBRRQYI4EDEDniM+NFVBAAQUUUEABBWZXwAB0dsVcXwEFFFBAAQUUUGCOBFplJ6Q5esdFGzNtJ8M3tdZOS19//XWcDWrRRRctelfenB2Br776Ko4r26lTp9nZzHWLBBhzd5555gkLLbRQ0VJvzo4AE2sssMACdWZ7m53t2/u6s2bNijPhcQy21t/zuf0ZMpY2Y2rzWzjvvPPO7ZfTKp9/5syZcZjIxRZbrFW+fl50jx49wvnnn1+T198qh2FqLhlGoPruu++aa3c1389VV10VrrzyyvDUU0/V/LnbyhP++te/Dm+88UYYPnx4W3lLNX8fe++9d1hppZXC7373u5o/d1t5wg033DAccsgh4bDDDmsrb6mm74NJSvr37x8uuuiisM0229T0udvKk/373/8OBx98cLjzzjvDqquu2lbeVk3fB9N7n3XWWeHFF1+s6fM255MxhGWtLkDadQ1oU8YKbc4Ptrn3Ra0TpbHxTpv7edvS/jgGWvtx0BI+D45Fj8M5+yQ0rNwvO2Hy1+OwMsfMED8NKzP0nDx7buaAzp6XayuggAIKKKCAAgrMoUC7boKfQ7u5vvk777wT3n777dj0NNdfTCt9ATSVkMNIE6ilMoEnn3wy5o316tWrsh24VXj44YfDiiuuGFZeeWU1KhAgH/6RRx4J66yzTlh66aUr2IObfPrpp2Hs2LFhk002id9nRWZfYMKECeGVV14J22677exv3A63MABthx+6b1kBBRRQQAEFFJibAjbBz019n1sBBRRQQAEFFGiHAgag7fBD9y0roIACCiiggAJzU8AAdG7q+9wKKKCAAgoooEA7FDAAbYcfum9ZAQVahgAdPyzNJzB+/Pjm21k73ZOGc/bBM6A/neIsjQsYgDZu1GLXeOmll8Jxxx0XjjrqqPDggw+22NfZkl/Y9OnTw7XXXhv++c9/tuSX2aJf22effRb+3//7f+HMM88MDAhuaZoAI1jst99+4ec//3m477774qxmTdvStfIEmIWGiSUslQtoWLkdW9IDfvDgweHAAw+MI1vM2d7a/tYGoK30M540aVI477zz4swV+++/f7z90UcftdJ3M3deNsEnAXznzp0D07JSmB3LMnsCp556alh//fXD7rvvHrp27Tp7G7fjtX/wgx+ELl26hEMPPTQwC81ee+0VrrjiiuD3uLKDgqlMv/nmm/ivsj24lYZzdgzcdtttcUa4P/zhD3GWwhEjRszZDtv41gagrfQDZry2DTbYIAZO1OD9/ve/D9dff30c07KVvqWav+xRo0aFjTfeOPz4xz+O49/95Cc/ibVRnMQs5QUYbzErNCEzjuqmm24a1lxzzTir1LBhwzwOM6AG/jLbzIABAwLjqJ5++ulxCr+//e1vsfbk5JNPDv/5z38a2NqHMgGOx4MOOihehDOr2QMPPBC4QLc0XUDDplvlrcl40kOGDAnPPvtsmH/++eN5+X//93/DddddFwxC88T+u8wAtLxNi36E2pPRo0cHTlRHHnlkWGuttWLzp02gTf/YfvjDH8am93333Tfm7DCP7xJLLBFee+21pu+kna1JftOYMWPC559/Ht/5oosuGk/2r776akGiU6dO4eWXXy7c98b3AjNmzAjfffddYcHOO+8cRo4cGd59991wwQUXhLPPPjtQi8KA6sWmhQ28UU9gs802C1z09O3bN3Tr1i384x//CCeccELA9rHHHqu3vgvqC2hY32R2lpCCxHmEyThomaQSgwkRCELHjRs3O7tqV+s6EH0r+7i//PLLsPDCC8dXfe6558ack1/84heBGRioSWGZpbwAARQnKGqX9t577zjzDHMg82/KlCnhN7/5TbjkkksKxuX31L4fufPOO8Obb74Zr/qpPeGHllxGAlJq4i+99FLnk845RK655prw3nvvhVNOOSUec6yCHUHoGWecEVs1cjZzUY4AebO33357WHzxxWMqUo8ePcK9994b6ERz+OGHx5xampQt+QIESbSecd5YbbXVwmGHHRYtNcz3yluK4a9+9auw5JJLBlKRSOGiNZKWIS6KqA21lBewBrS8TYt75O67745NTZzo33rrrXDiiSeGHXfcMfz1r38NH3zwQTjttNNa3Gue2y+ImiU6e2SFH1w6zZB3t+qqq8YfXH4sDjnkkHD++efHADQL8LNt/Pu9ABdAjz76aGw6fv/998OFF14YNt988/jjS0oDzU3UytO8bKkvcMABB4SvvvoqNrdnNaEDBw4MBEprr712/Q1ckitAzea//vWv+H3le/y73/0uzJo1K6yyyiqFmmODz1y6wsIrr7wymmH34YcfhhtuuCE+pmGBqNEbBJjkbj/++OPhjTfeiClI1L4TkNKiZmlEII3YLa1EIO3tnqRXXMkTTzyRpB0+kvSAbyWvfO69zPTqPkmbNJP05BRfRNpMkkydOrXwgtKcuyRtUi7c90Z5AdyuvvrqZIsttkjSUQOSdKiR5H/+53+SNAgtv5GP1BPgO3zSSSclaY1nktbIx8fToD2544476q3rgnyBc845J0lTkJIXXnghSWvukrTjVpKOwpCkKQ5JWgOfv5FL6wikLUBJ2hEz4TcwrbxIXn/99SS9QNewjlLT7nBO3mOPPZK0siNuwPkmHVGgaRu347XmTZPfT28kRvXhuSxwzz33hIsuuiiQP7bLLrsE8j+56h86dGhssuNqy5IvQE4YTXOkKdBJhvzOadOmxdtsMXHixJivSC9uS3mB9DcyHHPMMTE3cauttorJ9YssskhstiOlAdcNN9yw/A7a6SPUdpIfhg+3qV3Hbcstt4y1xemJK5B/t8wyy8RWDI5RS+MCNLNT205NaBqMxhon0j5+9rOfxWO08T24BiMvUHP3ySefxJpk+g+QTrPttttq2MjhQfoHLWb0w1hxxRWjV/fu3QPhFB0yF1tssUKKTSO7atcPG4C28I+fJuSbb745jhdIrg6dP3r37h172RGEMhYoic+W8gLkd5IQTsBO0/vll18eGIJpwQUXDH/5y19i8jidjyzlBTjh0/mInp4cd/zIkgJC4ETuGD+4GFvqCjAg9Y033hhzjkn1IAXklltuCc8991xsLn744YdjLm1ae2ITfF26OvdIVyC3jhx3Rl346U9/GhgxgAvMFVZYIRpzEWkaQx22OncYGSCteQ9/+tOf4veW0T+uuuqq+F2eZ555Qtq6EUgR8Xtch63eHS56qBQi93Py5Mnx+7zTTjvFiiHOxRyPpiDVY8tdYA5oLkvLWJhW58fOCtQ49enTJ9akkH/HCY3CMEzkn1jKC5CzSNloo41iwEQPRQar5qqfzjKcyFZeeeXyO/CRKMBJiU5H5IpRGDeVEz4BfNp050VQVPn+P3Ky77rrrpCmLcQ8WQJ1Andqi+mItOuuu8YLIGo86ZREXrKlvAA1TtQcE8AzOgAdBanxXHbZZWNtPLVP++yzT/kd+Ej44x//GMg3JueT8WZxJADlWOWiiNpja+DrHyhcRFIRlBXOwbvttlv8ztKZlRr4s846Kz5MAErFhqVpAvaCb5rTXFuLq1JmOeKHguY7mo+phfrlL3/p1X4jnwo1JjQz8aNAbR0nMGrxWE5KA7UnlvICDKVERwVqi7nCp5BYTyc4Avvnn38+poRwYqOZ2fJfAWpIGFKJ4+6pp56K3116avO9JU2BWnhLwwKc2Hv27Bl/89Lc2DB8+PDYyXL11VePQ9zQ43ippZYKxx9/fOz40fDe2uejxYbUHPN9pvaT2jma24899tgwaNCgwPjHlvICBOikHzHRBh206GxEayQpcQSffLepyLj11lvL78RHcgWsAc1lmXsLGdaBk/6B6VRe5JNw4qcGlB9aTvoMc8OVv01NDX9GXLEy5BLNdNRwpp1lYiBFTSjD3XDyspQX4KqfIIoBvo844ohYc0cOIz+4BFiczAhESW8gsLf8V4AcOmrpuGBkalJqnB566KFoxIgBnLh4zNKwAFMaMtIHuccM1k8NMi0WFHoe//a3v421y6xjyRcoNqRpnQtJauAptGgw/Nf999/vvOX5fIWltPb0798/1iBz3iBdhlQ4jklaL/he0zfDUoFAO+6A1SLeeppbl6Q/ooXXkjaFJGnzUpIOcZOkgWiSnsCStNYzSa9ck7QjjT3rClL5N+hhnNbGJemwSnV6Z6dDjMTeslha8gXSwLLwQNrkGY/DbEFaC5CkgUBhBAFGF0ibkqNpGuxnq7Xrv+kwVEl64Zik48kWHNKa9yTtgFS4z/HHsZnmdReWeSNfgN/F9OInSXM+Y8/stAY5SYOm/JVdmitQbMh3OE3ZStKZonLXdWF9gXfeeSf+DjJCRXpxmey5554J52yOybQiKOGYTGvnkzRHuf7GLmlUwE5IFQTtzbkJvWO5Uv3Rj34Um5LIJWE8T+aIJscu/QLEx+nowZWYzcYN6zNWKlen1BozPio94HFjZhny8ag9IW/MUleAmndqPMk7Jt+YnEQ6v5GrSC0nNe90mGHMT2o8SQfBmVpQa5P/a0ltHOky9Grn+0tqArWfuP35z3+OPY4ZdYFB0mnKs+a47jFYfI/WHmrp0uGVYs4sHWbonU2LBk2gjrhQrJV/u9SQWjpq8jjH8BtI721LeQFyZEldYIYtzh/MNsgINNmEEcz4xnmZVBumgLXMvoAB6OybNesWpcMEPf300zFIYmaKrPBF6NevX1huueWyRf7NESBXjGR68nVIUaCTFj3fmXKTIJRlBp85cOkigkxOTgSdBKGcrJjfGFOOUZrdyRsjD4qy0EILRVdn+ogc8T9SPTi+OEFx4YgX6TT77bdf2GabbULXrl1j0LneeusZfH7PVu8WOdoXX3xxPP44wXMxRMetLAh1xIV6ZHUWMEoA+cZc9NDsPnjw4NjpEsMsCGXoPi4qLeUFuIBkek0uFrEjJY5jj85aVBIxcorD95X3a8ojdkJqilIN1qFzDD2008HmY24Ovdu56uJLQC0TV1+WhgXIW2QWHpLCs1mhqC1hek2S77litTQsQGI9HbbWWGONeNxRi0xHLn6EDz74YE9aDfPFR6n1JP/zsssuiy0bTdjEVf5PgNxtRvnge0wuLcfeH/7wh9gJifxZAlMveho+XJgml1mgGGuWC3Bqko877rjoqWHDdjxKzTGtGbRO0ukybUeOQSe5yOTS/v3vf298J67RJAED0CYx1WalrIc2NXhMM8fVKzVOjLloyReg6ZgTFQMqr7TSSnFKTZqSaV7CkUJg6tAY+X4sZUgqmuUYZonObtmQVQShdECyNC5QOi951jmB4Ik0EEvDAnQYZHxeRlagSZMe2hRq8RjsG0eG+1p33XUb3lE7fpRxPhmNgotuKi3SGcsKIwZkQeizzz6rYSPHCK0XdHKj0oJWDNKNOnbsGJg0gvE/6YhpaR4Bm+Cbx7HJexk7dmysoeMqnoOZA51xxQieqPEkcCKAYpiRNOE5ML6dpbxAOs1mDNSpOSa/jpMYNcnpVJGFXDEHBS7vx4mfAZVpatp6663jQNU0JVPbSXM8gZTNTOX9eIT0hNKBqY8++uiYMkNzPE3HnMQs5QWakrvNBByW8gKcNzbZZJOYBkLNHQEoTcbUhBI8MayVYx6X98seoQWN7zRpClw8pp0LY418Or1mTG2wMiOTaoa/jXZTcoVmFWAubXqzp7VOSVrblNAzkR52aW5OknZGis/F/MbMaWypL5AG8Ek6HFDhgWw+42xBGnwmadNn9MPR0rBAmueZnHLKKXGlNMcpzvG+3XbbJWkzU3ROa6Qa3oGPJmmNSO685NDQC9nSsMDtt9+epHl1ceQP1mRO8rTlJ0nHsWx4Qx8tCKSdZeKIFNmCe++9N5qmnbiyRf5tQIBe7Wkte5LWEMe10pnKknTovga28KHmEHAc0GYI4mdnFySE04zEX2qW6Fi08847x9505IxRaILyKitflcGVTzjhhDgIMGuQCE7OXVaoAaDTAn44WvIFqPkkNYFencx+wviV5I3tuOOOsYcxTaGkgJh7nO9XvBRDBkqn+ZPmOUYFwI9ijVOxVN3b5NrxfSa3js5bNHlS6DRI72Pm2qZDjaW8QGa4//77x2khyf+kZPmKzHpkqS/AiCi0PjKmJ/na/PZxPqbPBWlHHHczZ86MuaD1t3ZJcwnYBN9ckrOxHw50Tu4029ErkR7IBKLcz2acmY3dtatVscs6KjDUEsNX8SNL8wjzGV933XVxSj4GCbbkCzDsF7meJNMzOgBBJ708mRGF3u0MJUROqM3G+X7kHTNDGekeWDItH03IjBbgvOT5ZnlLmUebDoIEnwemE29giCfD3tD8yW8hx6OlvEBmyIUOQSgjCDAEGPcJ5Gl+NwWprh/f3yOPPDJeaJNrjBU5sqTB8VtIGhwD9nMsMtQcI9BYqiNgAFod13p7zU5a9OIkEZwfC5YxJy8nenIZN954Y3vN1pOrv4DhlbIglJlm6KRF0EQPT0YP4HFLeQGu8qlFJuCkxo7hWOjlzskLQ6Z5JQCw5AtDlcnyAAAZsElEQVTgx7h/OHHRyFR9BFJ0/uA4pEaZqfkcGzDfL1vKOKh8d6mJyoLQ4nE+DZwyqfJ/iw0JpBjyq3icTw3r240cOTLWbqapcPHcS+UFFRd0JCQQpeWMyg2Cd1o2+EsnJEvzC9gLvvlNc/fIcDb0UsxqS/gSUOtER480Byp2BOFKy5NWLl8M1ot7u9NMQg0eY1USOPkDke9WvBQrmjknTJgQa+8YlzKbE5oaKI4/UheoSbaUF2AcQByZxjDNFYsXk/TUxtDSuAAdsxhzlkkNKFxMMjg/w39xEU4gb/pMw44NGa6++upxY8eNzjckTYEL7ewckrU80oKRzpgX/2WtP9lQahyvluYX8EzT/KZ19kjP7HT6vRhkcoKiyfPQQw+Ns80wxAi3hw0bFn+MDT7r0NW5Q7BJjfHZZ58dmzrp9c7VPicqavPIabQ0LECOIr3dMWOkADy54mdO6HHjxsXmToPP8obZd5kTFbVMBJ/UfJIrxhBWlqYJMMJCOt1wzDtmC0b6IHeb2mOOP4PPxh0bMiQH2eCzvCEtP3xnaYZPO7TG2k9qQ+mLwUU5+aEUcuSfeeYZWyXLU87xIzbBzzFh+R0wqDdBJ03CnOAZeskZjsp7NfQIJ/y8KUr54SCIIvfOki9AoEmQTjNnOmpAbFIiDYQr/2yoFgIAS10B8sCYvKBz586x01v2XWaqzauuuio2HxMwkQ/KANUch5bGBRgaiJpOLrypcefikeCTTkfkw1saF9CwcaNya5CWwLSu/BaSApJNUJKOmhIviljO95r1CEqdMrec5JwvNwCdc8PcPXCFddJJJ8UEZ/IUGU/MzjK5VE1ayAmqXABPrbKlvAAndYKov/zlL3GQeTopEHBydc+0m46vWN+OZuGsxpgcseLvMuMEErjT7M7UuXy/mV7TUlcAQ9I+GCWgtBBAEcjTCsRsR8zUY95xqdJ/0xM0rO9SyZKXXnop5rwzRjQpW+TM8ptI5RATmdD8TiUHtaCW2giYA1pFZw5qrvIvvfTS2MmD6b2uv/56ZzgqY05zCD+21BjTu5OBlTmJkX9DjQlBgFOUlsHLWUxSPZ06uMJn2C+GbKGWiX/0+EzHcYu1T05tWB+P4w8zZiKjkxa1x8Xf5fpbuKRUgF7E1Lwz0QHBpmX2BTScfbO8Leh/wcgfHI+MQMMUpZyLaWYnIOU7vuuuu8aB+/O2d1mVBJpjMFH38b3AlClTkosuuihJk8QTBgFOZ1RI0rHGkvHjx3+/krdyBdIcuyQNMJO0tjO58sork3Q60iSdFi1JfzSSdGaoJM2zS9Ir1CStLYkDf+fuxIVRID1xRSeOu7vuuitOdJDWACTpjCjxeExnOFIqRyAN0pO0VjM+kjazJ2nHrCSb0MDvcg5YI4s45tKhbZJ0trd6a6ZjzyZp7XK95S6oK6BhXY9K7qUX40k6Ak3y6aefJuk0pQkTbDB4fxqMVrI7t2kmAWpBLM0okCY2x+AordYv/LimTXXxpP/+++834zO1zV1hRBDKvzQ3LL7JNIcxSYe1KcxS0TbfefO+K2bnIWDKyogRI5K0w1u860xbmUrdv3gxM1napJ6k0+8lTz31VJyNh1l5COgpfpfrmjV0j9llKHkBVDqUVbxASlOVGtqFj/2fgIZzdiikLWvxHJIFn+yNmY6cqWzOXOd0a3vBz2HNMjPIZCW9uoozKJDYzFiA9Gqn2Y4cO6r8yb2zNCxAPueFF14Ym4bpJEMhGZwmPJqQLfUFOAbpIMPwQOR1UjjWHnjggcLKpDAwGgOFXsbOtFWgiTfoqHXLLbfEAahJ/aCXNscbEx/w3f31r38dXn311dhpwe9yXbu8ewwqf9BBB4X0hB+PNUZdYHYj8mYZ+/i1116LI1qY/pGn999l6RTNYYcddogpDAyOrmF5q8Ye6dmzZ5yBkCZ3Ur1ITSI3njxQy9wTsBNShfbfffddnH2HAeQJNklqJlBirEpOWgyFQUnnLo85eEy/yeOWfIFPPvkkpHOSx3zZNdZYIw6xRK4iQ2IwxA0nLk5mDhNU1y9NUwg33nhjtKNjEbOfcFJnhijGmSXvmGORdTgG11lnnbo78F4U4GRErjHj8pL/ySDzXDwyo9aGG24YPemgsPXWW8eTlt/lhg8cJtxI02ii3+mnnx6nhmRwb4at4jvMcGpeBJU3TGvgY49sgvaHH344zhJF/jvHoobl3YofYXxtxpalkxGza2255ZaB8za58XzfmQWJKTgtc0/ATkgV2NPDPa2+jz1h99xzz8DAtgRJQ4YMiT8ajKtIsERiM4nOdELyhFUemmFYOOEzlA3BEz1i+ZHlqvXoo4+OAT3j3jm9Zn3DffbZJ9aMMAxV2hwSCEjHjBkTh6ViKkN6GXNsUivPCcwAvr5h8RKGTuOkxUUQQ7BwLDLmLIOmpzndHoPFWDm3abXgH44MVUUrEPO9M+MWNXgcf7RyGHzm4BUtosMMA6VTGAwdMzpoEpDSkqFhEVbOzTTHPU5QwsXPrbfeGo9BWjYcUikHay4usga0Anx+RGmuSzt0xCsomkm42uIHgjHEGHLp3nvvDWkHmhhAObxIfWSa4LKUBHp6MvsJASgnLYJ6hsAh4GSgfmru6IVsqS9AgEl6wssvvxynNOQvNXmc9DlRHXbYYWH77bcvpITU34NLigW4UMyrCSXAJyi1lBdgxApqkJkggplmmEeb9COCpVVXXTWkeYzxWPVivLwhaR9chNNMTPMw05TSc5tjj1YgCjOWaVjekKHlqMCgxr1Pnz6xAuOzzz6L5xa+29Z6lrer9SMGoBWIc1XPOHb8CNA8QkDK1SpBKD+yTLdJU92WaZU/YwZa6gscc8wxcTYKBv9l2CVOVuTQMq82tZ2vv/56nCaS3EVrPuv7ZUs4uTO+LPmJ5IBSk8xkBxx3jFHJycrSsABDtPzhD3+IYwKmo1jEoInvL8EUKSDUgHIRSYuGJV8gO+lT687vHn40wXMRtPbaa8cg1BmO8u2ypUwpTOoCFRn7779/tEtHD4gBPTmLDJ6+3XbbZav7N0eAfHguejiXkEPL8UhAT+oRQeiHH34YsqlKczZ3UY0F5qnx87WZp+NKih9dcpmYJpJmeG5T9U8HBkvDAkyDRmoCTXGc2Kk5ptMMJ66sMxedPbJc2ob31n4fpWmYPGTmJseOiyMGTmcZV/+WxgXIPaYWmfm1+S4zfi853TQZkztG7adTG5Z3TEcLiBeJBJ433XRT7LzFd5rOhA899FBIh7wpv7GPRAFqj0lFItCk5pMULyo5yFVkogM6zzhbWcMHCx0Jmb2MIJ5KDFp+SGXIOq8S1JNWY2k5AtaANuGzoDmEHwRy61ZKB/CmdokTPVdW1HhS60R+SVYTyg+H0/I1DEszCLPJXH755XHmCVzJYaQWgOY7TE1daNiw+FHyE9PxZwO1eVwAMfsMTaGWugJ0QiBgIkjne0rnN/JmqeUkd5sgnu8xywkErH2v65d3j3QjZpEhXzYdeinmvXOBTr4d825369YtbzOXpQIEnVyIk6dIawYXPKQcUVtHJ0I6YjIZgsFn44dL1pEQS1rQDj/88JCOLR2Pxx//+MdO89o4Yc3XMABtAjkBKDWcNM9Rxc9QNwyLwQ9FOp5dvKraZptt4tU/P7z8kFjyBeiByI8qc5HTo5MmEvKcCOoXXnjhmPOZDtxfyA/N30v7XsqVPrVLZ555ZmGoIGrsaC4mhYEf23Tc1PaNlPPuyY9lVh5OSgwTRA0xNew0FdPsSTMdtU1caNKJhpEELI0LMA0pxx35yAShjCJA50uOQ/PtGvbjOCT9iHzFv/71r7H/AOcPjkW+z1Ry5E1l2vBe29ejtFpQ0873Ocvh5js9YcKEGITi6UVQyzwm7AXfxM+FHwOGbaCpjk5G/MByUFPTSWcampAtDQukA33HJhJGC6CnLHNA0+no448/DulMR3G+codnadiQR6npJGAnH4ymJk5eHJvUylvyBQjaDznkkHB62iu2dOw/LoqoNaYWlIsgAilqQ5nC1JIvQKdBau7o3c7FD4XWCzpe8pecZC7QLeUFyFEk6OSCnCAJUy6QGHOWofwsjQvQvE6LRTZyRTZyALb3339/XE7rmqVlCpgD2sTPheY6Tkr0RKR6n5w7rvCfe+65ePXVxN20u9Vo6swKCfV00KJ5KRua5ayzzoo93PGkI4jDs2Ra+X9JU6AWftCgQTGAZwQGau8IBBgezJIvQIcsxkgtDT7pJMNYqfSaZexFOhVy2+Az35GlnPQXWmih2JmDHtrUFlOodSdXEUODz0jS4H8DBgwo/PaxIscnwywRkJLKYCkvgA/9CMjrZIxj3BhNhVQ4+maQVsPFpsFnecOW8IgB6Gx8CsVBKPk5jK1IVT/NoZb6AqQn0LGDvDsKTUnDhw+PQywRMNH8aQeF+m7llnDip5aT5HqaPEkLoQaUIJScRUYUsOQL4EYqTWkhkKIZlJ7aHJMMNt+9e/fS1bxfJHDSSSfFNAVy7OjZTrM7J306vjEuLcenpbwAKR7UHpPSxTHHBTmtGhSC0Ouuu84LoPJ88RE6rJK+RfDJkH6MlUqnQS7C+Q7vuuuu9sNoxLAlPGwAOpufQnEQSrIzhWY7S30BAiZqOslVJAglt5N8Jzp2MFoAgfsuu+xSf0OX1BEovtq/+uqrw+DBg2NHLXKeCKxIZeBqn1w8S74AQ6+QA0ozZ3Gh+Y4e29Y4FauUv01NE99rOsZQCELpTEitMb+D5MBbyguQr0jzMH0ISKMhR5YgdPz48TEoZUtbgcr70cmSAJNxuLl4JP2DPgSkcRHUk8JACg2dCS0tX8BOSBV8RvxAZJ2NnFmhPOAqq6wSazhJpCd1gVxFmp0IAh588ME4ODrNduYuljfkEXIUmRWKkRgYpoVaYzq9MYIAtXcEAzxuKS/AiYpRFRgzlUG9s4kNuIjkRMYYi5bGBfhOE0Txu8d4sxSmIuYYZDxfv8sNGzK9KxfepC2QfkRaFzXuXFRSueHoKQ370cTOiAB8fxnyi4sf3LZMhwBjYhh+B/k+W1qHgJ2QWsfn1GpfJc0iBJ6ctAhEuXpluAxL4wJc7TMjFAE7U7vyY0tnGjpscaJiMH+Gcck6gTS+R9egIxy5xtQ+kU/LDD0M+cVJzJIvwFBz5GjTvEmgSQBPR8Jf/OIX8cSfv5VL8wT4/aODFrXFdIqjAyu25H1aGhbgXMK0wldccUVckQtzhv8iiDdwb9iupT5qANpSP5lW+LroSUwe4kEHHRQ22mij+A7IVeSqlZonfnwNQpv+wdK0hCOzd1DTyQDL/NAShPKjywlsiSWWaPoOXTMK4EeTHWkh5ns2fFBwEcRg3nQ2mj59emByCEYAwY4glOCdXHhLvgDHGvmddIoheGdqYb7PXADRkkaePLnwDpuW78dSLrI53pig5Oijj45TNmcDyhOEjhgxIp5jyu/BR1qqgDmgLfWTaWWvi9qkbBxK8nPIDSPZnuFFCJJIFCcXlJy70jy8VvZWa/JyudpnHDuahg844IAY1DPUEoETQTwBgcFnZR8FfgyhZvDZuN/YsWNj0zrf42uvvTYOsUQA2rlz59iUTJO8pbwAnWO6du0aa+r++c9/xgtHZtWi1pOh+whEGXvWki9Az3bSEw488MBYc8xFOB2PSOGikNJFDq2ldQpYA9o6P7cW+aoZcomaTq5U+cEgR4fbBFGcyBjj09I0AWpJvNpvmpVrNY8APdnJT6SD4M9+9rMYGNH8zoUjgRLD3qy11lqxRpTbpUNaNc+raN17Ybgv8hBJi+GifM8994x524xYQWsQnTIJ2qlVdpD+xj9rWoEIQCmYcV7hGORinHF7+/Xr1/hOXKPFClgD2mI/mtb3wqjdZIgWOntclw4lQs9sfjQYK5XpDxlz0VJegACeExU93ckN82q/vJWPNK/A+++/H3sQ0xTMcZdN/UgwxZiejO/JMFZ0giOH2+Az358AMxsblQ5ZDpmW79TYUsY2JsWDGfNosaAGnrQjzivjxo2LtxmyytK6BawBbd2fX4t89cU1odR6cqXP0C0OV9Xwx0V+3QorrBBWSmfuoNaYGhSmjfRqv2E3H51zAZoxe/fuHWfXYm8EAMxWxkUlKSBMfvD444/HY5MaKQfqr2tOr3bSYxjrmE4y+DEjFDWht99+e/joo4/imL2nnHJKnOmIFAZLeQE6XZIvS9oC5w7OIwSiGDPMEiOCWFq/gAFo6/8MW+Q7yIJQOi7Y9N74R0TzHOkLNIHee++9saMHvY7pBZ811ZE7ZlGgGgJMGMFYqdR2coKn6Z3aUAZJZ+Y3e2mXV2ccXsZH5TePvGJq6phiOAtCCZoYrJ/hgZjBjCHULPkCdNpiLE9a0Rjtg4twAnlSuRjCiiDU0nYEDEDbzmfZ4t4JP8hMc2hTSeMfDc2bzGpEUyeBALVMNOfR+3PfffdtfAeuocAcCDCHO0EotU30MGbSCGrvuAhiukPmLLfUF6BmmBpPOg0ywQaBJrPwMPQcPdyzIJTAieDKIdPqG5L+QW07o3pkE2uQd8zFN+eOLAjFlwDe0nYEDEDbzmfpO2llAkyfyWDUjG1HrixDLlFjTKoCwTs1AIwmwLiLFgXmhsDNN98cOyWR12ipK0DwSZBJqwXBEYXaOgZJJ2inZEEo68w333xxmf/VFeA3jyZ1LrzJ+8yCUGrd+UdKEkEowzFZA1rXrrXfsxNSa/8Eff2tRoAcuqzwY0rOJ0Ox0FmLMVKZLYoB0U888cTYw5MetAafmZh/ayHAcUltHp2NaPIcNWpUHHC+Fs/d2p6D1gqmcCWAotA55sknnww77rhj4a1wAUmtncFngaTeDWbVYnIIBugnUCfY7Nu3b7wo58KcGlKCUoPPenStfoE1oK3+I/QNtAYBmt+4yiegPOKII2KOHcNUMcwITZ/rr79+YMxAEu9322231vCWfI1tVIARK5jWkPnKCaacuaz8Bz1y5MjABByHHXZYzNsm392Uo/JeDT2SVxNKCgOeDNpvaXsCzgXf9j5T31ELEyCBnpqSnXbaKdCkyXiLjF/3+eefx+R6OiCR/0lnDzosMLe2Iwa0sA+xHb0cBuknHYSLJXrBW8oLMJUrMxkxigDNx3369Cm/so80KEBny/79+8fxo/ktpBaUmd+sPW6QrVU/6K9Lq/74fPEtXYC5ihk8mX/cpnmT2T3oaUzz+wMPPBC23nrrGIgSpNIszwnNooACrUOAIdPI16YmlHxuS+UCxc3xDF9ladsCNsG37c/XdzcXBTghjR8/PubSkS+23377hWuuuSYsuuiiMc+TGib+MVc0QyyR72Tz3Vz8wHxqBeZAIGuOZ7B0p8mdA8h0Uy7Gqfm06X3OHFv61nbLa+mfkK+vVQoQfL777ruFAZRpXmLwaQakZm5oakLpbERi/S233BJ/aG3ubJUftS9agShATShN8gafc35AONHBnBu2hj0YgLaGT8nX2OoEGD+RHE9qPrMgk+FZmNqQwniABKHkOjFAtUUBBVq/APO8WxRQoGkCNsE3zcm1FJhtgQsuuCAOLbLFFluEhx56KHCf5neLAgoooIAC7V3AALS9HwG+/6oKEHTeeeedsZmdGlCLAgoooIACCoRgAOpRoECVBbKaUAZbppenRQEFFFBAgfYuYADa3o8A339NBAhCGf/z4osvrsnz+SQKKKCAAgq0ZAED0Jb86fja2pQAAejKK6/cpt6Tb0YBBRRQQIFKBAxAK1FzGwUUUEABBRRQQIGKBZwJqWI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoRMAAtBI1t1FAAQUUUEABBRSoWMAAtGI6N1RAAQUUUEABBRSoROD/AxCJIezQwqAXAAAAAElFTkSuQmCC" /><!-- --></p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a><span class="fu">plot_grid</span>(p5,</span>
<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a> <span class="at">ncol =</span> <span class="dv">1</span>,</span>
<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a> <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">'Top 20 features of Microbiome and PlasamaSomalogic layer'</span>),</span>
<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a> <span class="at">label_size =</span> <span class="dv">8</span>, <span class="at">vjust =</span> <span class="fl">0.1</span>)<span class="sc">+</span></span>
<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">plot.margin =</span> <span class="fu">unit</span>(<span class="fu">c</span>(<span class="fl">0.5</span>,<span class="fl">0.5</span>,<span class="fl">0.5</span>,<span class="fl">0.5</span>), <span class="st">"cm"</span>))</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a><span class="fu">sessionInfo</span>()</span>
<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a><span class="co">#> R version 4.4.0 (2024-04-24)</span></span>
<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a><span class="co">#> Platform: aarch64-apple-darwin20</span></span>
<span id="cb15-4"><a href="#cb15-4" tabindex="-1"></a><span class="co">#> Running under: macOS Sonoma 14.6.1</span></span>
<span id="cb15-5"><a href="#cb15-5" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb15-6"><a href="#cb15-6" tabindex="-1"></a><span class="co">#> Matrix products: default</span></span>
<span id="cb15-7"><a href="#cb15-7" tabindex="-1"></a><span class="co">#> BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib </span></span>
<span id="cb15-8"><a href="#cb15-8" tabindex="-1"></a><span class="co">#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0</span></span>
<span id="cb15-9"><a href="#cb15-9" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb15-10"><a href="#cb15-10" tabindex="-1"></a><span class="co">#> locale:</span></span>
<span id="cb15-11"><a href="#cb15-11" tabindex="-1"></a><span class="co">#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8</span></span>
<span id="cb15-12"><a href="#cb15-12" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb15-13"><a href="#cb15-13" tabindex="-1"></a><span class="co">#> time zone: America/New_York</span></span>
<span id="cb15-14"><a href="#cb15-14" tabindex="-1"></a><span class="co">#> tzcode source: internal</span></span>
<span id="cb15-15"><a href="#cb15-15" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb15-16"><a href="#cb15-16" tabindex="-1"></a><span class="co">#> attached base packages:</span></span>
<span id="cb15-17"><a href="#cb15-17" tabindex="-1"></a><span class="co">#> [1] splines stats graphics grDevices utils datasets methods </span></span>
<span id="cb15-18"><a href="#cb15-18" tabindex="-1"></a><span class="co">#> [8] base </span></span>
<span id="cb15-19"><a href="#cb15-19" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb15-20"><a href="#cb15-20" tabindex="-1"></a><span class="co">#> other attached packages:</span></span>
<span id="cb15-21"><a href="#cb15-21" tabindex="-1"></a><span class="co">#> [1] bartMachine_1.3.4.1 missForest_1.5 randomForest_4.7-1.1 </span></span>
<span id="cb15-22"><a href="#cb15-22" tabindex="-1"></a><span class="co">#> [4] bartMachineJARs_1.2.1 rJava_1.0-11 mcmcplots_0.4.3 </span></span>
<span id="cb15-23"><a href="#cb15-23" tabindex="-1"></a><span class="co">#> [7] coda_0.19-4.1 cowplot_1.1.3 caret_6.0-94 </span></span>
<span id="cb15-24"><a href="#cb15-24" tabindex="-1"></a><span class="co">#> [10] lattice_0.22-6 SuperLearner_2.0-29 gam_1.22-4 </span></span>
<span id="cb15-25"><a href="#cb15-25" tabindex="-1"></a><span class="co">#> [13] foreach_1.5.2 nnls_1.5 lubridate_1.9.3 </span></span>
<span id="cb15-26"><a href="#cb15-26" tabindex="-1"></a><span class="co">#> [16] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 </span></span>
<span id="cb15-27"><a href="#cb15-27" tabindex="-1"></a><span class="co">#> [19] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 </span></span>
<span id="cb15-28"><a href="#cb15-28" tabindex="-1"></a><span class="co">#> [22] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 </span></span>
<span id="cb15-29"><a href="#cb15-29" tabindex="-1"></a><span class="co">#> [25] IntegratedLearner_0.0.1</span></span>
<span id="cb15-30"><a href="#cb15-30" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb15-31"><a href="#cb15-31" tabindex="-1"></a><span class="co">#> loaded via a namespace (and not attached):</span></span>
<span id="cb15-32"><a href="#cb15-32" tabindex="-1"></a><span class="co">#> [1] pROC_1.18.5 rlang_1.1.4 magrittr_2.0.3 </span></span>
<span id="cb15-33"><a href="#cb15-33" tabindex="-1"></a><span class="co">#> [4] compiler_4.4.0 vctrs_0.6.5 reshape2_1.4.4 </span></span>
<span id="cb15-34"><a href="#cb15-34" tabindex="-1"></a><span class="co">#> [7] quadprog_1.5-8 crayon_1.5.3 pkgconfig_2.0.3 </span></span>
<span id="cb15-35"><a href="#cb15-35" tabindex="-1"></a><span class="co">#> [10] fastmap_1.2.0 labeling_0.4.3 utf8_1.2.4 </span></span>
<span id="cb15-36"><a href="#cb15-36" tabindex="-1"></a><span class="co">#> [13] rmarkdown_2.27 prodlim_2024.06.25 tzdb_0.4.0 </span></span>
<span id="cb15-37"><a href="#cb15-37" tabindex="-1"></a><span class="co">#> [16] nloptr_2.1.1 itertools_0.1-3 xfun_0.46 </span></span>
<span id="cb15-38"><a href="#cb15-38" tabindex="-1"></a><span class="co">#> [19] cachem_1.1.0 jsonlite_1.8.8 recipes_1.1.0 </span></span>
<span id="cb15-39"><a href="#cb15-39" tabindex="-1"></a><span class="co">#> [22] highr_0.11 parallel_4.4.0 R6_2.5.1 </span></span>
<span id="cb15-40"><a href="#cb15-40" tabindex="-1"></a><span class="co">#> [25] bslib_0.8.0 stringi_1.8.4 denstrip_1.5.4 </span></span>
<span id="cb15-41"><a href="#cb15-41" tabindex="-1"></a><span class="co">#> [28] parallelly_1.38.0 rpart_4.1.23 jquerylib_0.1.4 </span></span>
<span id="cb15-42"><a href="#cb15-42" tabindex="-1"></a><span class="co">#> [31] Rcpp_1.0.13 iterators_1.0.14 knitr_1.48 </span></span>
<span id="cb15-43"><a href="#cb15-43" tabindex="-1"></a><span class="co">#> [34] future.apply_1.11.2 Matrix_1.7-0 nnet_7.3-19 </span></span>
<span id="cb15-44"><a href="#cb15-44" tabindex="-1"></a><span class="co">#> [37] timechange_0.3.0 tidyselect_1.2.1 rstudioapi_0.16.0 </span></span>
<span id="cb15-45"><a href="#cb15-45" tabindex="-1"></a><span class="co">#> [40] yaml_2.3.10 timeDate_4032.109 codetools_0.2-20 </span></span>
<span id="cb15-46"><a href="#cb15-46" tabindex="-1"></a><span class="co">#> [43] listenv_0.9.1 doRNG_1.8.6 plyr_1.8.9 </span></span>
<span id="cb15-47"><a href="#cb15-47" tabindex="-1"></a><span class="co">#> [46] withr_3.0.1 ROCR_1.0-11 evaluate_0.24.0 </span></span>
<span id="cb15-48"><a href="#cb15-48" tabindex="-1"></a><span class="co">#> [49] future_1.34.0 survival_3.7-0 pillar_1.9.0 </span></span>
<span id="cb15-49"><a href="#cb15-49" tabindex="-1"></a><span class="co">#> [52] rngtools_1.5.2 stats4_4.4.0 generics_0.1.3 </span></span>
<span id="cb15-50"><a href="#cb15-50" tabindex="-1"></a><span class="co">#> [55] hms_1.1.3 munsell_0.5.1 scales_1.3.0 </span></span>
<span id="cb15-51"><a href="#cb15-51" tabindex="-1"></a><span class="co">#> [58] globals_0.16.3 class_7.3-22 glue_1.7.0 </span></span>
<span id="cb15-52"><a href="#cb15-52" tabindex="-1"></a><span class="co">#> [61] tools_4.4.0 data.table_1.15.4 ModelMetrics_1.2.2.2</span></span>
<span id="cb15-53"><a href="#cb15-53" tabindex="-1"></a><span class="co">#> [64] gower_1.0.1 grid_4.4.0 ipred_0.9-15 </span></span>
<span id="cb15-54"><a href="#cb15-54" tabindex="-1"></a><span class="co">#> [67] colorspace_2.1-1 nlme_3.1-165 sfsmisc_1.1-18 </span></span>
<span id="cb15-55"><a href="#cb15-55" tabindex="-1"></a><span class="co">#> [70] cli_3.6.3 fansi_1.0.6 lava_1.8.0 </span></span>
<span id="cb15-56"><a href="#cb15-56" tabindex="-1"></a><span class="co">#> [73] gtable_0.3.5 sass_0.4.9 digest_0.6.36 </span></span>
<span id="cb15-57"><a href="#cb15-57" tabindex="-1"></a><span class="co">#> [76] farver_2.1.2 htmltools_0.5.8.1 lifecycle_1.0.4 </span></span>
<span id="cb15-58"><a href="#cb15-58" tabindex="-1"></a><span class="co">#> [79] hardhat_1.4.0 MASS_7.3-61</span></span></code></pre></div>
</div>
<div id="references" class="section level2">
<h2>References</h2>
<p>Franzosa EA et al. (2019). <a href="https://www.ncbi.nlm.nih.gov/pubmed/30531976">Gut microbiome
structure and metabolic activity in inflammatory bowel disease</a>.
<em>Nature Microbiology</em> 4(2):293–305.</p>
<p>Ghaemi MS et al. (2019). <a href="https://pubmed.ncbi.nlm.nih.gov/30561547/">Multiomics modeling of
the immunome, transcriptome, microbiome, proteome and metabolome
adaptations during human pregnancy</a>. <em>Bioinformatics</em>
35(1):95-103.</p>
</div>
<div id="citation" class="section level2">
<h2>Citation</h2>
<p>Mallick et al. (2024). <a href="https://pubmed.ncbi.nlm.nih.gov/38146838/">An integrated Bayesian
framework for multi-omics prediction and classification</a>.
<em>Statistics in Medicine</em> 43(5):983–1002.</p>
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