ryan
/
resume
镜像自地址 https://github.com/DarwinAwardWinner/resume.git


			
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<h2></h2>

<div class="highlight"><pre><span></span><span class="c1"># We will use base 2 logarithms for now</span>
log.base <span class="o">=</span> <span class="m">2</span>

<span class="c1"># Read the data in from the file</span>
filename.data <span class="o">=</span> <span class="s">&quot;Processed_Data_Files/Normalized_Pair_Files/All_norm_pair.txt&quot;</span>
<span class="c1">#filename.data = &quot;Raw_Data_Files/Pair_Files/All_pair.txt&quot;</span>
intensity.lin <span class="o">&lt;-</span> read.table<span class="p">(</span>file<span class="o">=</span>filename.data<span class="p">,</span> header<span class="o">=</span><span class="kc">TRUE</span><span class="p">,</span>row.names<span class="o">=</span><span class="s">&quot;PROBE_ID&quot;</span><span class="p">)</span>

<span class="c1"># Column names and numbers used to categorize the data</span>
intensity.maincols <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">&quot;GENE_EXPR_OPTION&quot;</span><span class="p">,</span> <span class="s">&quot;SEQ_ID&quot;</span><span class="p">,</span> <span class="s">&quot;POSITION&quot;</span><span class="p">)</span>
intensity.maincolnums <span class="o">=</span> <span class="kp">which</span><span class="p">(</span><span class="kp">names</span><span class="p">(</span>intensity.lin<span class="p">)</span> <span class="o">%in%</span> intensity.maincols<span class="p">)</span>
<span class="c1"># Column names and numbers containing intensity data</span>
intensity.datacolnums <span class="o">=</span> <span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="kp">dim</span><span class="p">(</span>intensity.lin<span class="p">)[</span><span class="m">2</span><span class="p">])[</span><span class="o">-</span>intensity.maincolnums<span class="p">]</span> <span class="c1"># i.e. &quot;the rest&quot;</span>
intensity.datacols <span class="o">=</span> <span class="kp">names</span><span class="p">(</span>intensity.lin<span class="p">)[</span>intensity.datacolnums<span class="p">]</span>

<span class="c1"># Take the logarithm of the data</span>
intensity.log <span class="o">&lt;-</span> <span class="kt">data.frame</span><span class="p">(</span>intensity.lin<span class="p">[</span>intensity.maincols<span class="p">],</span><span class="kp">log</span><span class="p">(</span>intensity.lin<span class="p">[</span>intensity.datacols<span class="p">],</span> base <span class="o">=</span> log.base<span class="p">))</span>

<span class="c1"># Separate into random (i.e. background) and data probes</span>
<span class="c1"># Split into &quot;intensity$rand&quot; and &quot;intensity$data&quot;</span>
intensity <span class="o">&lt;-</span> <span class="kp">split</span><span class="p">(</span>x <span class="o">=</span> intensity.log<span class="p">,</span> f <span class="o">=</span> <span class="p">(</span><span class="kp">ifelse</span><span class="p">(</span>intensity.log<span class="o">$</span>GENE_EXPR_OPTION <span class="o">==</span> <span class="s">&quot;RANDOM&quot;</span><span class="p">,</span><span class="s">&quot;rand&quot;</span><span class="p">,</span><span class="s">&quot;data&quot;</span><span class="p">)))</span>
<span class="kp">rm</span><span class="p">(</span>intensity.lin<span class="p">,</span> intensity.log<span class="p">)</span> <span class="c1"># Discard unused stuff to free memory</span>

<span class="c1"># This gives a QQ plot of the data against the noise. I used it to select my cutoffs.</span>
<span class="c1">#qqplot( y=as.vector(as.matrix(intensity$data[sample(1:dim(intensity$data)[1],5000),intensity.datacols])), x=as.vector(as.matrix(intensity$rand[intensity.datacols])), ylab=&quot;Data&quot;, xlab=&quot;Random Control&quot;,main = &quot;Log10 QQ plot of Specific Probe Intensities vs. Random Controls&quot;)</span>

<span class="c1"># Detection (low) threshold is 2 sd above mean random background</span>
intensity.rand.vector <span class="o">=</span> <span class="kp">as.vector</span><span class="p">(</span><span class="kp">as.matrix</span><span class="p">(</span>intensity<span class="o">$</span>rand<span class="p">[</span>intensity.datacols<span class="p">]))</span>
threshold.low <span class="o">=</span> <span class="kp">mean</span><span class="p">(</span>intensity.rand.vector<span class="p">)</span> <span class="o">+</span> <span class="m">2</span><span class="o">*</span>sd<span class="p">(</span>intensity.rand.vector<span class="p">)</span>
<span class="kp">rm</span><span class="p">(</span>intensity.rand.vector<span class="p">)</span>
<span class="c1"># Saturation (high) threshold is 2-fold down from max possible</span>
threshold.high <span class="o">=</span> <span class="kp">log</span><span class="p">(</span><span class="m">65535</span><span class="o">/</span><span class="m">2</span><span class="p">,</span> base<span class="o">=</span>log.base<span class="p">)</span>
<span class="kr">if</span> <span class="p">(</span>threshold.high <span class="o">&lt;=</span> threshold.low<span class="p">)</span> <span class="kp">print</span><span class="p">(</span><span class="s">&quot;Error: low threshold is too high&quot;</span><span class="p">)</span>

<span class="c1"># Compute needed row statistics</span>
intensity<span class="o">$</span>data<span class="o">$</span>MAX <span class="o">&lt;-</span> <span class="kp">apply</span><span class="p">(</span>intensity<span class="o">$</span>data<span class="p">[</span>intensity.datacols<span class="p">],</span><span class="m">1</span><span class="p">,</span><span class="kp">max</span><span class="p">)</span>
<span class="c1"># Actually, we only need the max, it seems. Uncomment these if needed.</span>
<span class="c1">#intensity$data$MIN &lt;- apply(intensity$data[intensity.datacols],1,min)</span>
<span class="c1">#intensity$data$MEAN &lt;- rowMeans(intensity$data[intensity.datacols]) # Don&#39;t need that one</span>
<span class="c1">#intensity$data$SD &lt;- apply(intensity$data[intensity.datacols],1,sd) # Don&#39;t need that one</span>

<span class="c1"># Sort probes into three bins: absent, present, and saturated</span>
<span class="c1"># The integer value of BIN also serves as a rank:</span>
<span class="c1"># present &lt; saturated &lt; absent; lower is better</span>
intensity<span class="o">$</span>data<span class="o">$</span>BIN <span class="o">&lt;-</span> <span class="kp">factor</span><span class="p">(</span><span class="m">1</span> <span class="o">+</span> <span class="p">(</span>intensity<span class="o">$</span>data<span class="o">$</span>MAX <span class="o">&gt;</span> threshold.high<span class="p">)</span> <span class="o">+</span> <span class="p">(</span>intensity<span class="o">$</span>data<span class="o">$</span>MAX <span class="o">&lt;</span> threshold.low<span class="p">)</span> <span class="o">*</span> <span class="m">2</span><span class="p">,</span> labels <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">&quot;present&quot;</span><span class="p">,</span> <span class="s">&quot;saturated&quot;</span><span class="p">,</span> <span class="s">&quot;absent&quot;</span><span class="p">))</span>

<span class="c1"># Count how many probes from each probeset (SEQ_ID) went into each bin</span>
<span class="c1"># Also count total probes per set</span>
<span class="c1"># Counting is done by measuring length of data aggregated by SEQ_ID</span>
num.probes <span class="o">&lt;-</span> <span class="kp">lapply</span><span class="p">(</span><span class="kt">c</span><span class="p">(</span><span class="kt">list</span><span class="p">(</span>total<span class="o">=</span>intensity<span class="o">$</span>data<span class="p">),</span><span class="kp">split</span><span class="p">(</span>x <span class="o">=</span> intensity<span class="o">$</span>data<span class="p">,</span> f <span class="o">=</span> intensity<span class="o">$</span>data<span class="o">$</span>BIN<span class="p">)),</span> FUN <span class="o">=</span> <span class="kr">function</span> <span class="p">(</span>y<span class="p">)</span> aggregate<span class="p">(</span>x<span class="o">=</span><span class="kp">rep</span><span class="p">(</span><span class="kc">NA</span><span class="p">,</span><span class="kp">dim</span><span class="p">(</span>y<span class="p">)[</span><span class="m">1</span><span class="p">]),</span>by<span class="o">=</span><span class="kt">list</span><span class="p">(</span>SEQ_ID<span class="o">=</span>y<span class="o">$</span>SEQ_ID<span class="p">),</span>FUN<span class="o">=</span><span class="kp">length</span><span class="p">))</span>
<span class="kr">if</span><span class="p">(</span><span class="kp">mean</span><span class="p">(</span>num.probes<span class="o">$</span>present<span class="o">$</span>x<span class="p">)</span> <span class="o">&lt;</span> <span class="m">3</span><span class="p">)</span> <span class="p">{</span> <span class="kp">print</span><span class="p">(</span><span class="s">&quot;Error: Not enough present probes.&quot;</span><span class="p">)</span> <span class="p">}</span>


<span class="c1"># Something below this needs updating</span>


<span class="c1"># 2 rankings: Nimblegen&#39;s and distance from probeset mean</span>
<span class="c1"># Nimblegen&#39;s rank is read from the design file</span>
<span class="c1"># Probeinfo file</span>
<span class="c1"># This is information parsed from the design file.</span>
<span class="c1"># The design file can&#39;t be used directly because info on probe</span>
<span class="c1"># selection is not in separate columns.</span>
filename.probeinfo <span class="o">=</span> <span class="s">&quot;Design_Files/071031_U_Va_Tobacco_Expr.probeinfo&quot;</span>
probeinfo <span class="o">&lt;-</span> read.table<span class="p">(</span>filename.probeinfo<span class="p">,</span>header<span class="o">=</span><span class="kc">TRUE</span><span class="p">,</span>row.names<span class="o">=</span><span class="s">&quot;PROBE_ID&quot;</span><span class="p">,</span>as.is<span class="o">=</span><span class="s">&quot;SEQ&quot;</span><span class="p">)</span>
<span class="c1"># Use probe names as row names for indexing</span>
<span class="c1">#row.names(probeinfo) &lt;- as.character(probeinfo$PROBE_ID)</span>

<span class="c1"># Add Nimblegen rank to the main data frame</span>
intensity<span class="o">$</span>data<span class="p">[</span><span class="kt">c</span><span class="p">(</span><span class="s">&quot;RANK&quot;</span><span class="p">,</span><span class="s">&quot;SEQ&quot;</span><span class="p">)]</span> <span class="o">&lt;-</span> probeinfo<span class="p">[</span><span class="kp">row.names</span><span class="p">(</span>intensity<span class="o">$</span>data<span class="p">),</span><span class="kt">c</span><span class="p">(</span><span class="s">&quot;RANK&quot;</span><span class="p">,</span><span class="s">&quot;SEQ&quot;</span><span class="p">)]</span>


<span class="c1"># For present probes, rank is based on correlation to probeset mean</span>

<span class="c1"># Read probeset means from calls file</span>
filename.calls <span class="o">=</span> <span class="s">&quot;Processed_Data_Files/Normalized_Calls_Files/All_norm_calls.txt&quot;</span>
calls.lin <span class="o">&lt;-</span> read.table<span class="p">(</span>filename.calls<span class="p">,</span>header<span class="o">=</span><span class="kc">TRUE</span><span class="p">,</span>row.names<span class="o">=</span><span class="s">&quot;SEQ_ID&quot;</span><span class="p">)</span>
<span class="c1"># This line simultaneously logs the data and gives the same column order as the intensity table</span>
probeset.means <span class="o">=</span> <span class="kp">log</span><span class="p">(</span>calls.lin<span class="p">[</span>intensity.datacols<span class="p">],</span> base<span class="o">=</span>log.base<span class="p">)</span>
<span class="kp">rm</span><span class="p">(</span>calls.lin<span class="p">)</span>

<span class="c1"># Collect the relevant data into matrices for efficiency</span>
probes.present.data <span class="o">=</span> <span class="kp">t</span><span class="p">(</span>intensity<span class="o">$</span>data<span class="p">[</span>intensity<span class="o">$</span>data<span class="o">$</span>BIN <span class="o">==</span> <span class="s">&quot;present&quot;</span><span class="p">,</span>intensity.datacols<span class="p">])</span>
probeset.means.data <span class="o">=</span> <span class="kp">t</span><span class="p">(</span>probeset.means<span class="p">[</span>intensity<span class="o">$</span>data<span class="o">$</span>SEQ_ID<span class="p">[</span>intensity<span class="o">$</span>data<span class="o">$</span>BIN <span class="o">==</span> <span class="s">&quot;present&quot;</span><span class="p">],])</span>

<span class="c1"># We invert the correlation so that lower is better</span>
intensity<span class="o">$</span>data<span class="o">$</span>RANK<span class="p">[</span>intensity<span class="o">$</span>data<span class="o">$</span>BIN <span class="o">==</span> <span class="s">&quot;present&quot;</span><span class="p">]</span> <span class="o">&lt;-</span> <span class="kp">sapply</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="kp">dim</span><span class="p">(</span>probes.present.data<span class="p">)[</span><span class="m">2</span><span class="p">],</span><span class="kr">function</span> <span class="p">(</span>x<span class="p">)</span> <span class="p">{</span> <span class="o">-</span>cor<span class="p">(</span>probes.present.data<span class="p">[,</span>x<span class="p">],</span>probeset.means.data<span class="p">[,</span>x<span class="p">])</span> <span class="p">})</span>

<span class="c1"># Done with these</span>
<span class="kp">rm</span><span class="p">(</span><span class="s">&quot;probes.present.data&quot;</span><span class="p">,</span><span class="s">&quot;probeset.means.data&quot;</span><span class="p">)</span>

<span class="c1"># Sort by bin, then rank</span>
intensity<span class="o">$</span>data.ranked <span class="o">=</span> intensity<span class="o">$</span>data<span class="p">[</span><span class="kp">order</span><span class="p">(</span>intensity<span class="o">$</span>data<span class="o">$</span>BIN<span class="p">,</span>intensity<span class="o">$</span>data<span class="o">$</span>RANK<span class="p">),]</span>

<span class="c1"># Split by SEQ_ID</span>
probes.ranked <span class="o">&lt;-</span> <span class="kp">split</span><span class="p">(</span>x<span class="o">=</span><span class="kp">row.names</span><span class="p">(</span>intensity<span class="o">$</span>data.ranked<span class="p">),</span>
                       f<span class="o">=</span>intensity<span class="o">$</span>data.ranked<span class="o">$</span>SEQ_ID<span class="p">,</span>
                       drop<span class="o">=</span><span class="kc">TRUE</span><span class="p">)</span>

<span class="c1"># Set the desired number of probes per sequence</span>
num.probes.desired <span class="o">&lt;-</span> <span class="m">3</span>

<span class="c1"># A function to make any vector have length n, by truncating longer ones and padding shorter ones with NA</span>
firstN <span class="o">&lt;-</span> <span class="kr">function</span> <span class="p">(</span>v<span class="p">,</span>n<span class="p">)</span> <span class="kt">c</span><span class="p">(</span>v<span class="p">,</span><span class="kp">rep</span><span class="p">(</span><span class="kc">NA</span><span class="p">,</span>n<span class="p">))[</span><span class="m">1</span><span class="o">:</span>n<span class="p">]</span>
<span class="c1"># A function to take a string and append P1, P2, P3, etc. up to PN.</span>
probenamesN <span class="o">&lt;-</span> <span class="kr">function</span> <span class="p">(</span>s<span class="p">,</span>n<span class="p">)</span> <span class="p">(</span><span class="kp">paste</span><span class="p">(</span>s<span class="p">,</span><span class="s">&quot;P&quot;</span><span class="p">,</span><span class="m">1</span><span class="o">:</span>n<span class="p">,</span>sep<span class="o">=</span><span class="s">&quot;&quot;</span><span class="p">))[</span><span class="m">1</span><span class="o">:</span>n<span class="p">]</span>

probes.selected <span class="o">&lt;-</span> <span class="kt">c</span><span class="p">(</span><span class="kp">sapply</span><span class="p">(</span>probes.ranked<span class="p">,</span>firstN<span class="p">,</span>num.probes.desired<span class="p">))</span>
<span class="kp">names</span><span class="p">(</span>probes.selected<span class="p">)</span> <span class="o">&lt;-</span> <span class="kt">c</span><span class="p">(</span><span class="kp">sapply</span><span class="p">(</span><span class="kp">names</span><span class="p">(</span>probes.ranked<span class="p">),</span>probenamesN<span class="p">,</span>num.probes.desired<span class="p">))</span>
<span class="c1"># Filter NA</span>
probes.selected <span class="o">&lt;-</span> probes.selected<span class="p">[</span><span class="o">!</span><span class="kp">is.na</span><span class="p">(</span>probes.selected<span class="p">)]</span>

probes.selected.fasta <span class="o">&lt;-</span> <span class="kp">paste</span><span class="p">(</span>sep<span class="o">=</span><span class="s">&quot;&quot;</span><span class="p">,</span><span class="s">&quot;&gt;&quot;</span><span class="p">,</span><span class="kp">names</span><span class="p">(</span>probes.selected<span class="p">),</span><span class="s">&quot;\n&quot;</span><span class="p">,</span>intensity<span class="o">$</span>data<span class="p">[</span>probes.selected<span class="p">,</span><span class="s">&quot;SEQ&quot;</span><span class="p">])</span>

<span class="kp">cat</span><span class="p">(</span>sep<span class="o">=</span><span class="s">&quot;\n&quot;</span><span class="p">,</span>probes.selected.fasta<span class="p">,</span>file<span class="o">=</span><span class="s">&quot;selected_probes.fasta&quot;</span><span class="p">)</span>

<span class="kp">save.image</span><span class="p">(</span>file<span class="o">=</span><span class="s">&quot;probesel.rda&quot;</span><span class="p">)</span>
</pre></div>
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