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<li class="navelem"><a class="el" href="namespaceOpenMS.html">OpenMS</a></li><li class="navelem"><a class="el" href="classOpenMS_1_1MRMRTNormalizer.html">MRMRTNormalizer</a></li> </ul>
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<p>The <a class="el" href="classOpenMS_1_1MRMRTNormalizer.html" title="The MRMRTNormalizer will find retention time peptides in data. ">MRMRTNormalizer</a> will find retention time peptides in data.
<a href="classOpenMS_1_1MRMRTNormalizer.html#details">More...</a></p>
<p><code>#include <<a class="el" href="MRMRTNormalizer_8h_source.html">OpenMS/ANALYSIS/OPENSWATH/MRMRTNormalizer.h</a>></code></p>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
<tr class="memitem:aba4109486fe20c9b33bb69992cd101d4"><td class="memItemLeft" align="right" valign="top">static int </td><td class="memItemRight" valign="bottom"><a class="el" href="classOpenMS_1_1MRMRTNormalizer.html#aba4109486fe20c9b33bb69992cd101d4">outlier_candidate</a> (std::vector< <a class="el" href="classdouble.html">double</a> > &x, std::vector< <a class="el" href="classdouble.html">double</a> > &y)</td></tr>
<tr class="memdesc:aba4109486fe20c9b33bb69992cd101d4"><td class="mdescLeft"> </td><td class="mdescRight">This function computes a candidate outlier peptide by iteratively leaving one peptide out to find the one which results in the maximum R^2 of a first order linear regression of the remaining ones. The datapoints are submitted as two vectors of doubles (x- and y-coordinates). <a href="#aba4109486fe20c9b33bb69992cd101d4">More...</a><br/></td></tr>
<tr class="separator:aba4109486fe20c9b33bb69992cd101d4"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8c2ec4e09bdd94bc5f528f40dc96b01b"><td class="memItemLeft" align="right" valign="top">static std::vector< std::pair<br class="typebreak"/>
< <a class="el" href="classdouble.html">double</a>, <a class="el" href="classdouble.html">double</a> > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classOpenMS_1_1MRMRTNormalizer.html#a8c2ec4e09bdd94bc5f528f40dc96b01b">rm_outliers</a> (std::vector< std::pair< <a class="el" href="classdouble.html">double</a>, <a class="el" href="classdouble.html">double</a> > > &pairs, <a class="el" href="classdouble.html">double</a> rsq_limit, <a class="el" href="classdouble.html">double</a> coverage_limit)</td></tr>
<tr class="memdesc:a8c2ec4e09bdd94bc5f528f40dc96b01b"><td class="mdescLeft"> </td><td class="mdescRight">This function removes potential outliers from a set of paired points. Two thresholds need to be defined, first a lower R^2 limit to accept the regression for the RT normalization and second, the lower limit of peptide coverage. The algorithms then selects candidate outlier peptides and applies the Chauvenet's criterion on the assumption that the residuals are normal distributed to determine whether the peptides can be removed. This is done iteratively until both limits are reached. <a href="#a8c2ec4e09bdd94bc5f528f40dc96b01b">More...</a><br/></td></tr>
<tr class="separator:a8c2ec4e09bdd94bc5f528f40dc96b01b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac224bc5bdde5e5d1692f50dcaf763847"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classdouble.html">double</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classOpenMS_1_1MRMRTNormalizer.html#ac224bc5bdde5e5d1692f50dcaf763847">chauvenet_probability</a> (std::vector< <a class="el" href="classdouble.html">double</a> > &residuals, int pos)</td></tr>
<tr class="memdesc:ac224bc5bdde5e5d1692f50dcaf763847"><td class="mdescLeft"> </td><td class="mdescRight">This function computes Chauvenet's criterion probability for a vector and a value whose position is submitted. <a href="#ac224bc5bdde5e5d1692f50dcaf763847">More...</a><br/></td></tr>
<tr class="separator:ac224bc5bdde5e5d1692f50dcaf763847"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a33bb531d7b4c02a5cf48aced48e7f69a"><td class="memItemLeft" align="right" valign="top">static bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classOpenMS_1_1MRMRTNormalizer.html#a33bb531d7b4c02a5cf48aced48e7f69a">chauvenet</a> (std::vector< <a class="el" href="classdouble.html">double</a> > &residuals, int pos)</td></tr>
<tr class="memdesc:a33bb531d7b4c02a5cf48aced48e7f69a"><td class="mdescLeft"> </td><td class="mdescRight">This function computes Chauvenet's criterion for a vector and a value whose position is submitted. <a href="#a33bb531d7b4c02a5cf48aced48e7f69a">More...</a><br/></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>The <a class="el" href="classOpenMS_1_1MRMRTNormalizer.html" title="The MRMRTNormalizer will find retention time peptides in data. ">MRMRTNormalizer</a> will find retention time peptides in data. </p>
<p>This tool will take a description of RT peptides and their normalized retention time to write out a transformation file on how to transform the RT space into the normalized space.</p>
<p>The principle is adapted from Escher et al. Escher, C. et al. (2012), Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics, 12: 1111-1121. </p>
</div><h2 class="groupheader">Member Function Documentation</h2>
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<td class="memname">static bool chauvenet </td>
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<td class="paramname"><em>residuals</em>, </td>
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<p>This function computes Chauvenet's criterion for a vector and a value whose position is submitted. </p>
<dl class="section return"><dt>Returns</dt><dd>TRUE, if Chauvenet's criterion is fullfilled and the outlier can be removed. </dd></dl>
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<td class="memname">static <a class="el" href="classdouble.html">double</a> chauvenet_probability </td>
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<td class="paramname"><em>residuals</em>, </td>
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<p>This function computes Chauvenet's criterion probability for a vector and a value whose position is submitted. </p>
<dl class="section return"><dt>Returns</dt><dd>Chauvenet's criterion probability </dd></dl>
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<td class="memname">static int outlier_candidate </td>
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<p>This function computes a candidate outlier peptide by iteratively leaving one peptide out to find the one which results in the maximum R^2 of a first order linear regression of the remaining ones. The datapoints are submitted as two vectors of doubles (x- and y-coordinates). </p>
<dl class="section return"><dt>Returns</dt><dd>The position of the candidate outlier peptide as supplied by the vector is returned.</dd></dl>
<dl class="exception"><dt>Exceptions</dt><dd>
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<tr><td class="paramname"><a class="el" href="classOpenMS_1_1Exception_1_1UnableToFit.html" title="Exception used if an error occurred while fitting a model to a given dataset. ">Exception::UnableToFit</a></td><td>is thrown if fitting cannot be performed </td></tr>
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<td class="memname">static std::vector<std::pair<<a class="el" href="classdouble.html">double</a>, <a class="el" href="classdouble.html">double</a>> > rm_outliers </td>
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<td class="paramtype">std::vector< std::pair< <a class="el" href="classdouble.html">double</a>, <a class="el" href="classdouble.html">double</a> > > & </td>
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<p>This function removes potential outliers from a set of paired points. Two thresholds need to be defined, first a lower R^2 limit to accept the regression for the RT normalization and second, the lower limit of peptide coverage. The algorithms then selects candidate outlier peptides and applies the Chauvenet's criterion on the assumption that the residuals are normal distributed to determine whether the peptides can be removed. This is done iteratively until both limits are reached. </p>
<dl class="section return"><dt>Returns</dt><dd>A vector of pairs is returned if the R^2 limit was reached without reaching the coverage limit. If the limits are reached, an exception is thrown.</dd></dl>
<dl class="exception"><dt>Exceptions</dt><dd>
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<tr><td class="paramname"><a class="el" href="classOpenMS_1_1Exception_1_1UnableToFit.html" title="Exception used if an error occurred while fitting a model to a given dataset. ">Exception::UnableToFit</a></td><td>is thrown if fitting cannot be performed </td></tr>
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<TD><font color="#c0c0c0">OpenMS / TOPP release 1.11.1</font></TD>
<TD align="right"><font color="#c0c0c0">Documentation generated on Thu Nov 14 2013 11:19:27 using doxygen 1.8.5</font></TD>
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