408 lines
165 KiB
HTML
Executable File
408 lines
165 KiB
HTML
Executable File
<!DOCTYPE html><html lang="en-US"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width,height=device-height,initial-scale=1.0"><meta name="apple-mobile-web-app-capable" content="yes"><meta http-equiv="X-UA-Compatible" content="ie=edge"><meta property="og:type" content="website"><meta name="twitter:card" content="summary"><style>@media screen{body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=next] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-info-container button,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-note-container button{-webkit-tap-highlight-color:transparent;-webkit-appearance:none;-moz-appearance:none;appearance:none;background-color:transparent;border:0;color:inherit;cursor:pointer;font-size:inherit;opacity:.8;outline:none;padding:0;transition:opacity .2s 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/*!
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* Marp default theme.
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*
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* @theme default
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* @author Yuki Hattori
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*
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* @auto-scaling true
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nowrap;font-size:29px;height:720px;justify-content:center;padding:78.5px;width:1280px}div#p>svg>foreignObject>section{--marpit-root-font-size:29px}div#p>svg>foreignObject>section>:last-child,div#p>svg>foreignObject>section[data-footer]>:nth-last-child(2){margin-bottom:0}div#p>svg>foreignObject>section>:first-child,div#p>svg>foreignObject>section>header:first-child+*{margin-top:0}div#p>svg>foreignObject>section:after{bottom:21px;color:#777;font-size:24px;padding:0;position:absolute;right:30px}div#p>svg>foreignObject>section:after{--marpit-root-font-size:24px}div#p>svg>foreignObject>section.invert{background-color:#222;color:#e6eaf0}div#p>svg>foreignObject>section.invert:after{color:#999}div#p>svg>foreignObject>section.invert img{background-color:transparent}div#p>svg>foreignObject>section.invert a{color:#50b3ff}div#p>svg>foreignObject>section.invert h1{color:#a3c5e7}div#p>svg>foreignObject>section.invert h2,div#p>svg>foreignObject>section.invert h3,div#p>svg>foreignObject>section.invert h4,div#p>svg>foreignObject>section.invert h5{color:#ebeff5}div#p>svg>foreignObject>section.invert blockquote,div#p>svg>foreignObject>section.invert h6{border-color:#3d3f43;color:#939699}div#p>svg>foreignObject>section.invert h1 strong,div#p>svg>foreignObject>section.invert h2 strong,div#p>svg>foreignObject>section.invert h3 strong,div#p>svg>foreignObject>section.invert h4 strong,div#p>svg>foreignObject>section.invert h5 strong,div#p>svg>foreignObject>section.invert h6 strong{color:#7bf}div#p>svg>foreignObject>section.invert hr{background-color:#3d3f43}div#p>svg>foreignObject>section.invert footer,div#p>svg>foreignObject>section.invert header{color:hsla(0,0%,60%,.75)}div#p>svg>foreignObject>section.invert code,div#p>svg>foreignObject>section.invert kbd{background-color:#111}div#p>svg>foreignObject>section.invert kbd{border-color:#666;box-shadow:inset 0 -1px 0 #555;color:#e6eaf0}div#p>svg>foreignObject>section.invert table tr{background-color:#12181d;border-color:#60657b}div#p>svg>foreignObject>section.invert table tr:nth-child(2n){background-color:#1b2024}div#p>svg>foreignObject>section.invert table td,div#p>svg>foreignObject>section.invert table th{border-color:#5b5e61}div#p>svg>foreignObject>section.invert pre{background-color:#0a0e12;border-color:#777}div#p>svg>foreignObject>section.invert pre code{background-color:transparent}div#p>svg>foreignObject>section[data-color] h1,div#p>svg>foreignObject>section[data-color] h2,div#p>svg>foreignObject>section[data-color] h3,div#p>svg>foreignObject>section[data-color] h4,div#p>svg>foreignObject>section[data-color] h5,div#p>svg>foreignObject>section[data-color] h6{color:currentcolor}div#p>svg>foreignObject>section[data-marpit-advanced-background=background]{columns:initial!important;display:block!important;padding:0!important}div#p>svg>foreignObject>section[data-marpit-advanced-background=background]:after,div#p>svg>foreignObject>section[data-marpit-advanced-background=background]:before,div#p>svg>foreignObject>section[data-marpit-advanced-background=content]:after,div#p>svg>foreignObject>section[data-marpit-advanced-background=content]:before{display:none!important}div#p>svg>foreignObject>section[data-marpit-advanced-background=background]>div[data-marpit-advanced-background-container]{all:initial;display:flex;flex-direction:row;height:100%;overflow:hidden;width:100%}div#p>svg>foreignObject>section[data-marpit-advanced-background=background]>div[data-marpit-advanced-background-container][data-marpit-advanced-background-direction=vertical]{flex-direction:column}div#p>svg>foreignObject>section[data-marpit-advanced-background=background][data-marpit-advanced-background-split]>div[data-marpit-advanced-background-container]{width:var(--marpit-advanced-background-split,50%)}div#p>svg>foreignObject>section[data-marpit-advanced-background=background][data-marpit-advanced-background-split=right]>div[data-marpit-advanced-background-container]{margin-left:calc(100% - var(--marpit-advanced-background-split, 50%))}div#p>svg>foreignObject>section[data-marpit-advanced-background=background]>div[data-marpit-advanced-background-container]>figure{all:initial;background-position:center;background-repeat:no-repeat;background-size:cover;flex:auto;margin:0}div#p>svg>foreignObject>section[data-marpit-advanced-background=content],div#p>svg>foreignObject>section[data-marpit-advanced-background=pseudo]{background:transparent!important}div#p>svg>foreignObject>section[data-marpit-advanced-background=pseudo],div#p>svg[data-marpit-svg]>foreignObject[data-marpit-advanced-background=pseudo]{pointer-events:none!important}div#p>svg>foreignObject>section[data-marpit-advanced-background-split]{width:100%;height:100%}</style></head><body><div class="bespoke-marp-osc"><button data-bespoke-marp-osc="prev" tabindex="-1" title="Previous slide">Previous slide</button><span data-bespoke-marp-osc="page"></span><button data-bespoke-marp-osc="next" tabindex="-1" title="Next slide">Next slide</button><button data-bespoke-marp-osc="fullscreen" tabindex="-1" title="Toggle fullscreen (f)">Toggle fullscreen</button><button data-bespoke-marp-osc="presenter" tabindex="-1" title="Open presenter view (p)">Open presenter view</button></div><div id="p"><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="1" data-theme="default" style="--theme:default;">
|
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<p><img src="R_Logo.png" alt="" style="width:150px;" /></p>
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<h1><code>caret</code> & <code>bigstatsR</code> Workshop</h1>
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</section>
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</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="2" data-theme="default" style="--theme:default;">
|
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<h1>Required Libraries</h1>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># "bigstatsR" and related packages.</span>
|
|
install.packages(<span class="hljs-string">"bigstatsr"</span>)
|
|
install.packages(<span class="hljs-string">"bigreadr"</span>)
|
|
install.packages(<span class="hljs-string">"data.table"</span>)
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|
|
|
<span class="hljs-comment"># "impute" library.</span>
|
|
install.packages(<span class="hljs-string">"BiocManager"</span>)
|
|
BiocManager::install(<span class="hljs-string">"impute"</span>)
|
|
|
|
<span class="hljs-comment"># "caret" library and sample models.</span>
|
|
install.packages(<span class="hljs-string">"caret"</span>)
|
|
install.packages(<span class="hljs-string">"glmnet"</span>)
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
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</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="3" data-theme="default" style="--theme:default;">
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<h1>Getting Started</h1>
|
|
<ul>
|
|
<li>
|
|
<p>All files used in the workshop can be downloaded here:</p>
|
|
<ul>
|
|
<li><a href="https://cloud.ami.sc/s/gABPQGrrLa6cCiy">https://cloud.ami.sc/s/gABPQGrrLa6cCiy</a></li>
|
|
</ul>
|
|
</li>
|
|
<li>
|
|
<p>Throughout the workshop, you will need to fill out the missing code in the scripts, marked as <code>(...)</code>.</p>
|
|
</li>
|
|
</ul>
|
|
</section>
|
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</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="4" data-theme="default" style="--theme:default;">
|
|
<p><img src="R_Logo.png" alt="" style="width:150px;" /></p>
|
|
<h1><code>bigstatsR</code> Package</h1>
|
|
<blockquote>
|
|
<p>Provides functions for fast statistical analysis of large-scale data encoded as matrices. The package can handle matrices that are too large to fit in memory thanks to memory-mapping to binary files on disk.</p>
|
|
</blockquote>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="5" data-theme="default" style="--theme:default;">
|
|
<h1>Reading Files with <code>bigstatsR</code> [1]</h1>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Read only the first row of the file.</span>
|
|
<span class="hljs-comment"># This allows us to obtain the number of columns.</span>
|
|
first_row <- fread2(<span class="hljs-string">"Test_Genotype_Data.csv"</span>,
|
|
nrows = <span class="hljs-number">1</span>)
|
|
col_num <- ncol(first_row)
|
|
|
|
<span class="hljs-comment"># Read the entire file using big_read.</span>
|
|
gen_data <- big_read(<span class="hljs-string">"Test_Genotype_Data.csv"</span>, <span class="hljs-comment"># Our large data file.</span>
|
|
select = <span class="hljs-number">1</span>:col_num, <span class="hljs-comment"># The number of columns we want to read.</span>
|
|
backingfile = <span class="hljs-string">"Data"</span>, <span class="hljs-comment"># Path to the backing file.</span>
|
|
progress = <span class="hljs-literal">TRUE</span>, <span class="hljs-comment"># Show progress.</span>
|
|
type = <span class="hljs-string">"double"</span>) <span class="hljs-comment"># Data type of the contents.</span>
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="6" data-theme="default" style="--theme:default;">
|
|
<h1>Reading Files with <code>bigstatsR</code> [2]</h1>
|
|
<ul>
|
|
<li>Our file will be read as a Filebacked Big Matrix (FBM).</li>
|
|
<li>After executing the code, we should see the following:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap>> gen_data
|
|
|
|
A Filebacked Big Matrix of type <span class="hljs-string">'double'</span> with <span class="hljs-number">1000</span> rows and <span class="hljs-number">1000</span> columns.
|
|
</span></span></foreignObject></svg></code></pre>
|
|
<ul>
|
|
<li>Be careful to remove the backing files before trying to re-read the same file.</li>
|
|
</ul>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="7" data-theme="default" style="--theme:default;">
|
|
<h1>Working with FBMs [1]</h1>
|
|
<ul>
|
|
<li>We can interact with FBMs <em>in a similar manner</em> as we do with normal dataframes.</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Examine the data.</span>
|
|
gen_data
|
|
gen_data[<span class="hljs-number">1</span>:<span class="hljs-number">10</span>, <span class="hljs-number">1</span>:<span class="hljs-number">10</span>]
|
|
str(gen_data)
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="8" data-theme="default" style="--theme:default;">
|
|
<h1>Working with FBMs [2]</h1>
|
|
<ul>
|
|
<li>Functions need to be applied differently.</li>
|
|
<li>Say we wanted to find out the number of missing values in our data.</li>
|
|
<li>The following approach does not work anymore:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Will not work!</span>
|
|
<span class="hljs-built_in">sum</span>(<span class="hljs-built_in">is.na</span>(gen_data))
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="9" data-theme="default" style="--theme:default;">
|
|
<h1>Applying Functions [1]</h1>
|
|
<ul>
|
|
<li>In order for the function to be applied, we need to define it separately and use <code>big_apply</code>.</li>
|
|
<li>Our function <strong>must</strong> take the following as arguments:
|
|
<ul>
|
|
<li>An FBM object: <code>X</code></li>
|
|
<li>A vector of indices: <code>ind</code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Function for checking the number of missing values in the dataframe.</span>
|
|
check_na <- <span class="hljs-keyword">function</span>(X, ind) {
|
|
<span class="hljs-built_in">sum</span>(<span class="hljs-built_in">is.na</span>(X[, ind]))
|
|
}
|
|
</span></span></foreignObject></svg></code></pre>
|
|
<ul>
|
|
<li><code>big_apply</code> will take care of splitting our data into subsets, applying our function to each subset, and combining the results.</li>
|
|
</ul>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="10" data-theme="default" style="--theme:default;">
|
|
<h1>Applying Functions [2]</h1>
|
|
<ul>
|
|
<li>Now, we can find the number of missing values in our dataset.</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Check how many missing values we have.</span>
|
|
big_apply(gen_data, <span class="hljs-comment"># FBM to which we want to apply the function.</span>
|
|
a.FUN = check_na, <span class="hljs-comment"># The function that we want to apply.</span>
|
|
a.combine = <span class="hljs-string">"+"</span>, <span class="hljs-comment"># How the results of each subset should be combined.</span>
|
|
ncores = <span class="hljs-number">2</span>) <span class="hljs-comment"># Number of CPU cores to use.</span>
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="11" data-theme="default" style="--theme:default;">
|
|
<h1>Applying Functions [3]</h1>
|
|
<ul>
|
|
<li>If we <em>really</em> want to, we can manually create subsets and apply functions using a <code>for</code> loop.</li>
|
|
<li>First, we must get the indices for each subset:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Subset the dataframe into sets of 100 columns.</span>
|
|
sub_idx <- split(<span class="hljs-number">1</span>:col_num, <span class="hljs-built_in">ceiling</span>(<span class="hljs-built_in">seq_along</span>(<span class="hljs-number">1</span>:col_num) / <span class="hljs-number">100</span>))
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="12" data-theme="default" style="--theme:default;">
|
|
<h1>Applying Functions [4]</h1>
|
|
<ul>
|
|
<li>Now, we can extract our subsets and apply our function:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-keyword">for</span> (i <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:<span class="hljs-built_in">length</span>(sub_idx)) {
|
|
|
|
<span class="hljs-comment"># Display current subset being evaluated.</span>
|
|
print(i)
|
|
|
|
<span class="hljs-comment"># Get the Genotype Data for the current subset.</span>
|
|
gen_subset <- gen_data[, sub_idx[[i]]]
|
|
|
|
<span class="hljs-comment"># Impute missing data using KNN clustering.</span>
|
|
gen_imputed <- <span class="hljs-built_in">round</span>(
|
|
t(impute::impute.knn(t(gen_subset), k = <span class="hljs-number">50</span>, rowmax = <span class="hljs-number">1</span>, colmax = <span class="hljs-number">1</span>)$data), <span class="hljs-number">0</span>)
|
|
|
|
<span class="hljs-comment"># "Write back" imputed data to the FBM.</span>
|
|
gen_data[, sub_idx[[i]]] <- gen_imputed
|
|
}
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="13" data-theme="default" style="--theme:default;">
|
|
<h1>Applying Functions [5]</h1>
|
|
<ul>
|
|
<li>Check that the <code>impute</code> function worked:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Now, verify there are no missing values anymore.</span>
|
|
big_apply(gen_data,
|
|
a.FUN = check_na,
|
|
a.combine = <span class="hljs-string">"+"</span>,
|
|
ncores = <span class="hljs-number">2</span>)
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="14" data-theme="default" style="--theme:default;">
|
|
<h1>Saving FBMs</h1>
|
|
<ul>
|
|
<li>How do we save our modified data back to a <code>.csv</code> file? Easy, use <code>big_write</code>:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Write the newly-imputed data to a new file.</span>
|
|
big_write(gen_data, <span class="hljs-comment"># Our FBM object.</span>
|
|
<span class="hljs-string">"Test_Genotype_Data_Imputed.csv"</span>, <span class="hljs-comment"># Name of the file.</span>
|
|
every_nrow = <span class="hljs-number">100</span>, <span class="hljs-comment"># How many rows do we want to save at once.</span>
|
|
progress = <span class="hljs-built_in">interactive</span>()) <span class="hljs-comment"># Show a fancy progress bar.</span>
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="15" data-theme="default" style="--theme:default;">
|
|
<h1>Removing Backing Files</h1>
|
|
<ul>
|
|
<li><code>big_read</code> will create backing files that are of no use after we saved our file.</li>
|
|
<li>They can be safely removed:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Remove backing files.</span>
|
|
system(<span class="hljs-string">"rm *.bk"</span>)
|
|
system(<span class="hljs-string">"rm *.rds"</span>)
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="16" data-theme="default" style="--theme:default;">
|
|
<p><img src="R_Logo.png" alt="" style="width:150px;" /></p>
|
|
<h1><code>caret</code> Package</h1>
|
|
<blockquote>
|
|
<p>Short for Classification And REgression Training. It is a set of functions that attempt to streamline the process for creating predictive models. The package started off as a way to provide a uniform interface for modeling functions, as well as a way to standardize common tasks (such parameter tuning and variable importance).</p>
|
|
</blockquote>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="17" data-theme="default" style="--theme:default;">
|
|
<h1>Loading Data</h1>
|
|
<ul>
|
|
<li>First, let's load our newly-imputed Genotype Data:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment">## Data Loading [Predictors]</span>
|
|
|
|
<span class="hljs-comment"># Genotype Data</span>
|
|
gen_path <- <span class="hljs-string">"Test_Genotype_Data_Imputed.csv"</span>
|
|
gen_data <- fread(gen_path, sep = <span class="hljs-string">","</span>, header = <span class="hljs-literal">FALSE</span>)
|
|
</span></span></foreignObject></svg></code></pre>
|
|
<ul>
|
|
<li>Let's load our Phenotype Data as well:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment">## Data Loading [Traits]</span>
|
|
|
|
<span class="hljs-comment"># Phenotype Data</span>
|
|
phn_path <- <span class="hljs-string">"Test_Phenotype_Data.csv"</span>
|
|
phn_data <- fread(phn_path, sep = <span class="hljs-string">","</span>, header = <span class="hljs-literal">FALSE</span>)
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="18" data-theme="default" style="--theme:default;">
|
|
<h1>Data Overview</h1>
|
|
<ul>
|
|
<li>Our Genotype Data is a large matrix of <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>0</mn></mrow><annotation encoding="application/x-tex">0</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">0</span></span></span></span>s and <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>1</mn></mrow><annotation encoding="application/x-tex">1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">1</span></span></span></span>s:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Preview of Genotype Data.</span>
|
|
str(gen_data)
|
|
gen_data[<span class="hljs-number">1</span>:<span class="hljs-number">10</span>, <span class="hljs-number">1</span>:<span class="hljs-number">10</span>]
|
|
</span></span></foreignObject></svg></code></pre>
|
|
<ul>
|
|
<li>Our Phenotype Data, which we want to predict, is a single column of <code>Plant Height (cm)</code> measurements:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Preview of Phenotype Data.</span>
|
|
str(phn_data)
|
|
phn_data[<span class="hljs-number">1</span>:<span class="hljs-number">10</span>, <span class="hljs-number">1</span>]
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="19" data-theme="default" style="--theme:default;">
|
|
<h1>The <code>createDataPartition</code> Function [1]</h1>
|
|
<ul>
|
|
<li>We can easily split our data into training and testing sets as follows:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># We want 80% of our data for training and 20% for testing.</span>
|
|
|
|
<span class="hljs-comment"># Get the indices for the rows to be used in the training data.</span>
|
|
train_index <- createDataPartition(
|
|
phn_data$V1, <span class="hljs-comment"># Our Phenotype Data.</span>
|
|
p = <span class="hljs-number">0.8</span>, <span class="hljs-comment"># Percentage of data to be used for training.</span>
|
|
<span class="hljs-built_in">list</span> = <span class="hljs-literal">FALSE</span>) <span class="hljs-comment"># To return a matrix instead of a list.</span>
|
|
|
|
<span class="hljs-comment"># Check out which rows will be used for training.</span>
|
|
train_index
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="20" data-theme="default" style="--theme:default;">
|
|
<h1>The <code>createDataPartition</code> Function [2]</h1>
|
|
<ul>
|
|
<li>Now that we know which indices will be used for training, we can extract the corresponding data:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># The values used as predictors [x].</span>
|
|
x_train <- gen_data[train_index,]
|
|
x_test <- gen_data[-train_index,]
|
|
|
|
<span class="hljs-comment"># The values to be predicted [y].</span>
|
|
y_train <- phn_data[train_index,]
|
|
y_test <- phn_data[-train_index,]
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="21" data-theme="default" style="--theme:default;">
|
|
<h1>Model Training and Tuning [1]</h1>
|
|
<blockquote>
|
|
<p><img src="R_Logo.png" alt="" style="width:50px;" /> <code>glmnet</code></p>
|
|
<hr />
|
|
<p>Fits generalized linear and similar models via penalized maximum likelihood. The regularization path is computed for the lasso or elastic net penalty at a grid of values (on the log scale) for the regularization parameter <code>lambda</code>.</p>
|
|
</blockquote>
|
|
<br />
|
|
<ul>
|
|
<li>We can perform this tuning automatically with <code>caret</code>. Let's first create a custom parameter grid:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Define a custom tuning grid.</span>
|
|
tune_grid <- expand.grid(alpha = seq(<span class="hljs-number">0.0001</span>, <span class="hljs-number">1</span>, <span class="hljs-built_in">length</span> = <span class="hljs-number">5</span>), <span class="hljs-comment"># Values to try for "alpha".</span>
|
|
lambda = <span class="hljs-number">5</span>) <span class="hljs-comment"># Values to try for "lambda".</span>
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="22" data-theme="default" style="--theme:default;">
|
|
<h1>Model Training and Tuning [2]</h1>
|
|
<ul>
|
|
<li>The <code>trainControl</code> function allows us to further customize how our model is made:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Parameter tuning.</span>
|
|
param_tune <- trainControl(method = <span class="hljs-string">"repeatedcv"</span>, <span class="hljs-comment"># Method to be used for resampling.</span>
|
|
number = <span class="hljs-number">2</span>, <span class="hljs-comment"># Numebr of folds (subsets) for cross-validation.</span>
|
|
repeats = <span class="hljs-number">5</span>, <span class="hljs-comment"># Number of iterations for cross-validation.</span>
|
|
trim = <span class="hljs-literal">TRUE</span>, <span class="hljs-comment"># Reduces memory consumption.</span>
|
|
search = <span class="hljs-string">"grid"</span>, <span class="hljs-comment"># How the tuning grid should be made.</span>
|
|
verboseIter = <span class="hljs-literal">TRUE</span>) <span class="hljs-comment"># To display additional information.</span>
|
|
</span></span></foreignObject></svg></code></pre>
|
|
<ul>
|
|
<li>Other methods are available, such as a single cross-validation (<code>cv</code>), a leave-one-out cross-validation (<code>LOOCV</code>), or <code>none</code>, which will only train a single model.</li>
|
|
<li>It is also possible to specify a <code>random</code> tuning grid.</li>
|
|
</ul>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="23" data-theme="default" style="--theme:default;">
|
|
<h1>Model Training and Tuning [3]</h1>
|
|
<ul>
|
|
<li>Now, we can finally train our model:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Train a model.</span>
|
|
glmnet_model <- train(x_train, <span class="hljs-comment"># Our predictors.</span>
|
|
y_train$V1, <span class="hljs-comment"># What we want to predict.</span>
|
|
method = <span class="hljs-string">"glmnet"</span>, <span class="hljs-comment"># The model we want to use.</span>
|
|
metric = <span class="hljs-string">"MAE"</span>, <span class="hljs-comment"># What metric should we use to pick the best model.</span>
|
|
tuneGrid = tune_grid, <span class="hljs-comment"># Our custom tuning grid.</span>
|
|
trControl = param_tune) <span class="hljs-comment"># Our custom tuning method.</span>
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="24" data-theme="default" style="--theme:default;">
|
|
<h1>Model Evaluation [1]</h1>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap>> glmnet
|
|
|
|
<span class="hljs-number">800</span> samples
|
|
<span class="hljs-number">1000</span> predictors
|
|
|
|
No pre-processing
|
|
Resampling: Cross-Validated (<span class="hljs-number">2</span> fold, repeated <span class="hljs-number">5</span> times)
|
|
Summary of sample sizes: <span class="hljs-number">400</span>, <span class="hljs-number">400</span>, <span class="hljs-number">400</span>, <span class="hljs-number">400</span>, <span class="hljs-number">400</span>, <span class="hljs-number">400</span>, ...
|
|
Resampling results across tuning parameters:
|
|
|
|
alpha RMSE Rsquared MAE
|
|
<span class="hljs-number">0.000100</span> <span class="hljs-number">61.23760</span> <span class="hljs-number">0.002033470</span> <span class="hljs-number">52.85568</span>
|
|
<span class="hljs-number">0.250075</span> <span class="hljs-number">61.83544</span> <span class="hljs-number">0.002543141</span> <span class="hljs-number">52.85747</span>
|
|
<span class="hljs-number">0.500050</span> <span class="hljs-number">60.02843</span> <span class="hljs-number">0.002118089</span> <span class="hljs-number">52.03982</span>
|
|
<span class="hljs-number">0.750025</span> <span class="hljs-number">59.16487</span> <span class="hljs-number">0.001651774</span> <span class="hljs-number">51.61048</span>
|
|
<span class="hljs-number">1.000000</span> <span class="hljs-number">58.81907</span> <span class="hljs-number">0.002781994</span> <span class="hljs-number">51.40225</span>
|
|
|
|
Tuning parameter <span class="hljs-string">'lambda'</span> was held constant at a value of <span class="hljs-number">5</span>
|
|
MAE was used to select the optimal model using the smallest value.
|
|
The final values used <span class="hljs-keyword">for</span> the model were alpha = <span class="hljs-number">1</span> and lambda = <span class="hljs-number">5.</span>
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="25" data-theme="default" style="--theme:default;">
|
|
<h1>Model Evaluation [2]</h1>
|
|
<ul>
|
|
<li>Now, let's predict on our testing data:</li>
|
|
</ul>
|
|
<pre><code class="language-r"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap><span class="hljs-comment"># Predict on testing data.</span>
|
|
glmnet_prediction <- predict(glmnet_model, x_test)
|
|
|
|
<span class="hljs-comment"># Check prediction accuracy.</span>
|
|
postResample(pred = glmnet_prediction, <span class="hljs-comment"># Our predicted values.</span>
|
|
obs = y_test$V1) <span class="hljs-comment"># Our expected vaues.</span>
|
|
</span></span></foreignObject></svg></code></pre>
|
|
<ul>
|
|
<li>This gives us a nice summary of the prediction:</li>
|
|
</ul>
|
|
<pre><code class="language-text"><svg data-marp-fitting="svg" data-marp-fitting-code><foreignObject><span data-marp-fitting-svg-content><span data-marp-fitting-svg-content-wrap> RMSE Rsquared MAE
|
|
5.649786e+01 6.306793e-06 4.883650e+01
|
|
</span></span></foreignObject></svg></code></pre>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="26" data-theme="default" style="--theme:default;">
|
|
<h1>More Models</h1>
|
|
<ul>
|
|
<li><code>caret</code> supports <em>a lot</em> of models (last time I checked, <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>238</mn></mrow><annotation encoding="application/x-tex">238</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">238</span></span></span></span>).</li>
|
|
<li>More examples are given in the file for you to try:
|
|
<ul>
|
|
<li><code>earth</code></li>
|
|
<li><code>keras</code></li>
|
|
<li><code>xgboost</code></li>
|
|
</ul>
|
|
</li>
|
|
<li>You can also define your own models as well.</li>
|
|
</ul>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="27" data-theme="default" style="--theme:default;">
|
|
<p><img src="Models.png" alt="" style="width:650px;" /></p>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="28" data-theme="default" style="--theme:default;">
|
|
<h1>References</h1>
|
|
<ul>
|
|
<li>
|
|
<p><code>bigstatsR</code> Package:</p>
|
|
<p>[1] <a href="https://github.com/privefl/bigstatsr">https://github.com/privefl/bigstatsr</a><br />
|
|
[2] <a href="https://privefl.github.io/bigstatsr/articles/read-FBM-from-file.html">https://privefl.github.io/bigstatsr/articles/read-FBM-from-file.html</a></p>
|
|
</li>
|
|
<li>
|
|
<p><code>caret</code> Package:</p>
|
|
<p>[1] <a href="https://topepo.github.io/caret/">https://topepo.github.io/caret/</a><br />
|
|
[2] <a href="https://topepo.github.io/caret/available-models.html">https://topepo.github.io/caret/available-models.html</a></p>
|
|
</li>
|
|
</ul>
|
|
</section>
|
|
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="29" data-theme="default" style="--theme:default;">
|
|
<h1>Thank You!</h1>
|
|
</section>
|
|
<script>!function(){"use strict";const t="marpitSVGPolyfill:setZoomFactor,",e=Symbol();let r,o;function n(n){const i="object"==typeof n&&n.target||document,a="object"==typeof n?n.zoom:n;window[e]||(Object.defineProperty(window,e,{configurable:!0,value:!0}),window.addEventListener("message",(({data:e,origin:r})=>{if(r===window.origin)try{if(e&&"string"==typeof e&&e.startsWith(t)){const[,t]=e.split(","),r=Number.parseFloat(t);Number.isNaN(r)||(o=r)}}catch(t){console.error(t)}})));let l=!1;Array.from(i.querySelectorAll("svg[data-marpit-svg]"),(t=>{var e,n,i,s;t.style.transform||(t.style.transform="translateZ(0)");const c=a||o||t.currentScale||1;r!==c&&(r=c,l=c);const d=t.getBoundingClientRect(),{length:f}=t.children;for(let r=0;r<f;r+=1){const o=t.children[r];if(o.getScreenCTM){const t=o.getScreenCTM();if(t){const r=null!==(n=null===(e=o.x)||void 0===e?void 0:e.baseVal.value)&&void 0!==n?n:0,a=null!==(s=null===(i=o.y)||void 0===i?void 0:i.baseVal.value)&&void 0!==s?s:0,l=o.children.length;for(let e=0;e<l;e+=1){const n=o.children[e];if("SECTION"===n.tagName){const{style:e}=n;e.transformOrigin||(e.transformOrigin=`${-r}px ${-a}px`),e.transform=`scale(${c}) matrix(${t.a}, ${t.b}, ${t.c}, ${t.d}, ${t.e-d.left}, ${t.f-d.top}) translateZ(0.0001px)`;break}}}}}})),!1!==l&&Array.from(i.querySelectorAll("iframe"),(({contentWindow:e})=>{null==e||e.postMessage(`${t}${l}`,"null"===window.origin?"*":window.origin)}))}r=1,o=void 0;const i=(t,e,r)=>{if(t.getAttribute(e)!==r)return t.setAttribute(e,r),!0};function a({once:t=!1,target:e=document}={}){const r="Apple Computer, Inc."===navigator.vendor?[n]:[];let o=!t;const a=()=>{for(const t of r)t({target:e});!function(t=document){Array.from(t.querySelectorAll('svg[data-marp-fitting="svg"]'),(t=>{var e;const r=t.firstChild,o=r.firstChild,{scrollWidth:n,scrollHeight:a}=o;let l,s=1;if(t.hasAttribute("data-marp-fitting-code")&&(l=null===(e=t.parentElement)||void 0===e?void 0:e.parentElement),t.hasAttribute("data-marp-fitting-math")&&(l=t.parentElement),l){const t=getComputedStyle(l),e=Math.ceil(l.clientWidth-parseFloat(t.paddingLeft||"0")-parseFloat(t.paddingRight||"0"));e&&(s=e)}const c=Math.max(n,s),d=Math.max(a,1),f=`0 0 ${c} ${d}`;i(r,"width",`${c}`),i(r,"height",`${d}`),i(t,"preserveAspectRatio",getComputedStyle(t).getPropertyValue("--preserve-aspect-ratio")||"xMinYMin meet"),i(t,"viewBox",f)&&t.classList.toggle("__reflow__")}))}(e),o&&window.requestAnimationFrame(a)};return a(),()=>{o=!1}}const l=Symbol(),s=document.currentScript;((t=document)=>{if("undefined"==typeof window)throw new Error("Marp Core's browser script is valid only in browser context.");if(t[l])return t[l];const e=a({target:t}),r=()=>{e(),delete t[l]};Object.defineProperty(t,l,{configurable:!0,value:r})})(s?s.getRootNode():document)}();
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</script></foreignObject></svg></div><script>/*!! License: https://unpkg.com/@marp-team/marp-cli@1.7.1/lib/bespoke.js.LICENSE.txt */
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