ryan
/
resume
spiegel van https://github.com/DarwinAwardWinner/resume.git


			
				
					
						
						
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<h2></h2>

<div class="highlight"><pre><span></span><span class="c1">#!/usr/bin/env Rscript</span>

<span class="c1"># Script to check reproducibility of fRMA training process</span>

<span class="kn">library</span><span class="p">(</span>xlsx<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>frma<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>frmaTools<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>stringr<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>magrittr<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>plyr<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>affy<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>preprocessCore<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>ggplot2<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>proto<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>dplyr<span class="p">)</span>
<span class="kp">load</span><span class="p">(</span><span class="s">&quot;consistency.rda&quot;</span><span class="p">)</span>

<span class="c1">## Select a random subset of 20 arrays from each tissue (Would rather</span>
<span class="c1">## use entire dataset but ENOMEM)</span>
<span class="kp">set.seed</span><span class="p">(</span><span class="m">1986</span><span class="p">)</span>
norm.exprs <span class="o">&lt;-</span> <span class="kp">lapply</span><span class="p">(</span><span class="kp">names</span><span class="p">(</span>vectors<span class="p">),</span> <span class="kr">function</span><span class="p">(</span>ttype<span class="p">)</span> <span class="p">{</span>
    stab <span class="o">&lt;-</span> sample.tables<span class="p">[[</span>ttype<span class="p">]]</span> <span class="o">%&gt;%</span> sample_n<span class="p">(</span><span class="m">20</span><span class="p">)</span>
    tsmsg<span class="p">(</span><span class="s">&quot;Reading 20 random arrays for &quot;</span><span class="p">,</span> ttype<span class="p">)</span>
    affy <span class="o">&lt;-</span> ReadAffy<span class="p">(</span>filenames<span class="o">=</span>stab<span class="o">$</span>Filename<span class="p">,</span> sampleNames<span class="o">=</span><span class="kp">rownames</span><span class="p">(</span>stab<span class="p">))</span>
    tsmsg<span class="p">(</span><span class="s">&quot;Normalizing with RMA for comparison&quot;</span><span class="p">)</span>
    eset.rma <span class="o">&lt;-</span> rma<span class="p">(</span>affy<span class="p">)</span>
    rma.exprs <span class="o">&lt;-</span> eset.rma <span class="o">%&gt;%</span> exprs <span class="o">%&gt;%</span> <span class="kp">as.vector</span>
    tsmsg<span class="p">(</span><span class="s">&quot;Normalizing with 5 trains fRMA vector sets&quot;</span><span class="p">)</span>
    esets.frma <span class="o">&lt;-</span> <span class="kp">lapply</span><span class="p">(</span>vectors<span class="p">[[</span>ttype<span class="p">]],</span> <span class="m">.</span> <span class="o">%&gt;%</span> frma<span class="p">(</span>affy<span class="p">,</span> input.vecs<span class="o">=</span><span class="m">.</span><span class="p">))</span>
    frma.exprs <span class="o">&lt;-</span> <span class="kp">sapply</span><span class="p">(</span>esets.frma<span class="p">,</span> <span class="m">.</span> <span class="o">%&gt;%</span> exprs <span class="o">%&gt;%</span> <span class="kp">as.vector</span><span class="p">)</span>
    <span class="kt">data.frame</span><span class="p">(</span>RMA<span class="o">=</span>rma.exprs<span class="p">,</span> fRMA<span class="o">=</span>frma.exprs<span class="p">)</span>
<span class="p">})</span> <span class="o">%&gt;%</span> setNames<span class="p">(</span><span class="kp">names</span><span class="p">(</span>vectors<span class="p">))</span>

<span class="c1">## Save because the above takes a while</span>
<span class="kp">save.image</span><span class="p">(</span><span class="s">&quot;consistency.rda&quot;</span><span class="p">)</span>

<span class="kp">dir.create</span><span class="p">(</span><span class="s">&quot;fRMA_consistency_results&quot;</span><span class="p">,</span> <span class="kc">FALSE</span><span class="p">)</span>

<span class="c1">## Compute M/A data for all pairwise comparisons</span>
ma.data <span class="o">&lt;-</span> <span class="kp">lapply</span><span class="p">(</span>norm.exprs<span class="p">,</span> <span class="kr">function</span><span class="p">(</span>normexprs<span class="p">)</span> <span class="p">{</span>
    normexprs <span class="o">%&gt;%</span> names <span class="o">%&gt;%</span> combn<span class="p">(</span><span class="m">2</span><span class="p">,</span> simplify<span class="o">=</span><span class="kc">FALSE</span><span class="p">)</span> <span class="o">%&gt;%</span> ldply<span class="p">(</span><span class="m">.</span> <span class="o">%&gt;%</span> <span class="p">{</span>
        col1 <span class="o">&lt;-</span> <span class="m">.</span><span class="p">[</span><span class="m">1</span><span class="p">]</span>
        col2 <span class="o">&lt;-</span> <span class="m">.</span><span class="p">[</span><span class="m">2</span><span class="p">]</span>
        x1 <span class="o">&lt;-</span> normexprs<span class="p">[[</span>col1<span class="p">]]</span>
        x2 <span class="o">&lt;-</span> normexprs<span class="p">[[</span>col2<span class="p">]]</span>
        <span class="kt">data.frame</span><span class="p">(</span>Comparison <span class="o">=</span> str_c<span class="p">(</span>col1<span class="p">,</span><span class="s">&quot;.vs.&quot;</span><span class="p">,</span>col2<span class="p">),</span>
                   x1<span class="o">=</span>x1<span class="p">,</span> x2<span class="o">=</span>x2<span class="p">,</span>
                   M <span class="o">=</span> x2 <span class="o">-</span> x1<span class="p">,</span>
                   A <span class="o">=</span> <span class="p">(</span>x1<span class="o">+</span>x2<span class="p">)</span><span class="o">/</span><span class="m">2</span><span class="p">)</span>
    <span class="p">})</span>
<span class="p">})</span>

<span class="kr">for</span> <span class="p">(</span>ttype <span class="kr">in</span> <span class="kp">names</span><span class="p">(</span>norm.exprs<span class="p">))</span> <span class="p">{</span>
    madata <span class="o">&lt;-</span> ma.data<span class="p">[[</span>ttype<span class="p">]]</span>

    pdf<span class="p">(</span>str_c<span class="p">(</span><span class="s">&quot;fRMA_consistency_results/MA Plots for &quot;</span><span class="p">,</span> ttype<span class="p">,</span> <span class="s">&quot;.pdf&quot;</span><span class="p">))</span>
    <span class="c1">## MA plot for every pair of normalizations</span>
    ddply<span class="p">(</span>madata<span class="p">,</span> <span class="m">.</span><span class="p">(</span>Comparison<span class="p">),</span> <span class="m">.</span> <span class="o">%$%</span> <span class="p">{</span>
        smoothScatter<span class="p">(</span>x<span class="o">=</span>A<span class="p">,</span> y<span class="o">=</span>M<span class="p">,</span> nbin<span class="o">=</span><span class="m">512</span><span class="p">,</span> main<span class="o">=</span>Comparison<span class="p">[</span><span class="m">1</span><span class="p">],</span> xlab<span class="o">=</span><span class="s">&quot;A&quot;</span><span class="p">,</span> ylab<span class="o">=</span><span class="s">&quot;M&quot;</span><span class="p">)</span>
    <span class="p">})</span>
    dev.off<span class="p">()</span>

    <span class="c1">## M boxplots &amp; violin plots</span>
    pdf<span class="p">(</span>str_c<span class="p">(</span><span class="s">&quot;fRMA_consistency_results/M Boxplots for &quot;</span><span class="p">,</span> ttype<span class="p">,</span> <span class="s">&quot;.pdf&quot;</span><span class="p">),</span>
        width<span class="o">=</span><span class="m">8</span><span class="p">,</span> height<span class="o">=</span><span class="m">12</span><span class="p">)</span>
    p <span class="o">&lt;-</span> ggplot<span class="p">(</span>madata<span class="p">)</span> <span class="o">+</span> aes<span class="p">(</span>x<span class="o">=</span>Comparison<span class="p">,</span> y<span class="o">=</span>M<span class="p">)</span> <span class="o">+</span>
        scale_x_discrete<span class="p">(</span>limits <span class="o">=</span> <span class="kp">rev</span><span class="p">(</span><span class="kp">levels</span><span class="p">(</span>madata<span class="o">$</span>Comparison<span class="p">)))</span> <span class="o">+</span>
            coord_flip<span class="p">()</span>
    <span class="kp">print</span><span class="p">(</span>p <span class="o">+</span> geom_boxplot<span class="p">(</span>notch<span class="o">=</span><span class="kc">TRUE</span><span class="p">,</span> outlier.shape <span class="o">=</span> <span class="kc">NA</span><span class="p">)</span> <span class="o">+</span>
          ggtitle<span class="p">(</span>str_c<span class="p">(</span><span class="s">&quot;Boxplots of M value distributions for &quot;</span><span class="p">,</span> ttype<span class="p">)))</span>
    <span class="kp">print</span><span class="p">(</span>p <span class="o">+</span> geom_violin<span class="p">(</span>scale<span class="o">=</span><span class="s">&quot;width&quot;</span><span class="p">)</span> <span class="o">+</span>
          ggtitle<span class="p">(</span>str_c<span class="p">(</span><span class="s">&quot;Violin plots of M value distributions for &quot;</span><span class="p">,</span> ttype<span class="p">)))</span>
    dev.off<span class="p">()</span>
<span class="p">}</span>
</pre></div>
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