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<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html><head><title>R: Calculate p-values for Copynumber Variations across Samples</title>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<link rel="stylesheet" type="text/css" href="R.css">
</head><body>
<table width="100%" summary="page for pconvolve"><tr><td>pconvolve</td><td align="right">R Documentation</td></tr></table>
<h2>Calculate p-values for Copynumber Variations across Samples</h2>
<h3>Description</h3>
<p>The amplification and deletion scores for a given loci i are computed
as equations which can be found in the 1/30/2013 meeting
minutes. Conceptually, the computation of the p-value is based on
permutation, where by each "column" of the matrix is shuffled multiple
times, abd a distribution of permuted score is computed for each locus
and compared to the observed score for that locus. See the equation in
the meeting minutes. In practice, the p-value can be computed in a
closed form using the concept of convolution. In R, this function uses
<code>convolve</code> to calculate the p-value analytically.
</p>
<h3>Usage</h3>
<pre>
pconvolve(fn, tau.amp=0.5, tau.del=-0.5, p.value.cutoff=0.05, fdr.c
utoff=0.25, plot.pdf=TRUE)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>fn</code></td>
<td>
<p>The file containing the log2 ratios for all segments across
samples. Each row represents a segment and each column is from one
sample.</p>
</td></tr>
<tr valign="top"><td><code>tau.amp</code></td>
<td>
<p>log2 ratios above this value are kept.</p>
</td></tr>
<tr valign="top"><td><code>tau.del</code></td>
<td>
<p>log2 ratios below this value are kept</p>
</td></tr>
<tr valign="top"><td><code>p.value.cutoff</code></td>
<td>
<p>Cutoff for p-values.</p>
</td></tr>
<tr valign="top"><td><code>fdr.cutoff</code></td>
<td>
<p>Cutoff for FDRs</p>
</td></tr>
<tr valign="top"><td><code>plot.pdf</code></td>
<td>
<p>If this is set to <code>TRUE</code>, then this function will
skip plotting on the default device and print it to a PDF file.</p>
</td></tr>
</table>
<h3>Author(s)</h3>
<p>Yu Fu <a href="mailto:[email protected]">[email protected]</a></p>
<h3>References</h3>
<p><a href="https://projects.zoho.com/portal/montilab#wiki/465555000000032025/Scoring-SCNAs.html">https://projects.zoho.com/portal/montilab#wiki/465555000000032025/Scoring-SCNAs.html</a>
</p>
<p><a href="http://sas.uwaterloo.ca/~dlmcleis/s901/chapt5.pdf">http://sas.uwaterloo.ca/~dlmcleis/s901/chapt5.pdf</a>
</p>
<h3>See Also</h3>
<p><code>convolve</code>, <code>p.adjust</code>
</p>
<h3>Examples</h3>
<pre>
## Calculate the p-value and FDR using the default parameters
pconvolve("matrix10_10.T.txt")
## Calculate the p-value and FDR using user defined parameters
pconvolve("matrix", tau.amp=1, tau.del=-1, p.value.cutoff=0.02, fdr.c
utoff=0.05, plot.pdf=TRUE)
</pre>
</body></html>