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<div class="title">Profile data processing </div> </div>
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<div class="textblock"><p><b>Goal:</b> You want to find all peaks in your profile data.</p>
<p>The first step shown here is the elimination of noise using a <b>NoiseFilter</b>. The now smoothed profile data can be further processed by subtracting the baseline with the <b>BaselineFilter</b>. Then use one of the <b>PeakPickers</b> to find all peaks in the baseline-reduced profile data.</p>
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<p>We offer two different smoothing filters: NoiseFilterGaussian and NoiseFilterSGolay. If you want to use the Savitzky Golay filter, or our <b>BaselineFilter</b> with non equally spaced profile data, e.g. TOF data, you have to generate equally spaced data using the <b>Resampler</b> tool.</p>
<h1><a class="anchor" id="TOPP_example_signalprocessing_peakpicker"></a>
Picking peaks with a PeakPicker</h1>
<p>The <b>PeakPicker</b> tools allow for picking peaks in profile data. Currently, there are two different TOPP tools available, PeakPickerWavelet and PeakPickerHiRes.</p>
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<td><b>PeakPickerWavelet</b>    <b>Input data:</b> profile data (low/medium resolution) </td></tr>
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<td><p class="starttd"><b>Description:</b> <br/>
This peak picking algorithm uses the continuous wavelet transform of a raw data signal to detect mass peaks. Afterwards a given asymmetric peak function is fitted to the raw data and important peak parameters (e.g. fwhm) are extracted. In an optional step these parameters can be optimized using a non-linear opimization method. <br/>
</p>
<p>The algorithm is descripted in detail in Lange et al. (2006) Proc. PSB-06.</p>
<p><b>Application:</b> <br/>
This algorithm was designed for low and medium resolution data. It can also be applied to high-resolution data, but can be slow on large datasets.<br/>
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<p class="endtd">See the PeakPickerCWT class documentation for a parameter list. </p>
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<td><b>PeakPickerHiRes</b>    <b>Input data:</b> profile data (high resolution) </td></tr>
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<td><p class="starttd"><b>Description:</b> <br/>
This peak-picking algorithm detects ion signals in raw data and reconstructs the corresponding peak shape by cubic spline interpolation. Signal detection depends on the signal-to-noise ratio which is adjustable by the user (see parameter <em>signal_to_noise</em>). A picked peak's m/z and intensity value is given by the maximum of the underlying peak spline. Please notice that this method is still <b>experimental</b> since it has not been tested thoroughly yet.<br/>
</p>
<p><b>Application:</b> <br/>
The algorithm is best suited for high-resolution MS data (FT-ICR-MS, Orbitrap). In high-resolution data, the signals of ions with similar mass-to-charge ratios (m/z) exhibit little or no overlapping and therefore allow for a clear separation. Furthermore, ion signals tend to show well-defined peak shapes with narrow peak width. These properties faciliate a fast computation of picked peaks so that even large data sets can be processed very quickly.</p>
<p class="endtd">See the PeakPickerHiRes class documentation for a parameter list. </p>
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<h1><a class="anchor" id="TOPP_example_signalprocessing_parameters"></a>
Finding the right parameters for the</h1>
<p>NoiseFilters, the BaselineFilter and the PeakPickers</p>
<p>Finding the right parameters is not trivial. The default parameters will not work on most datasets. In order to find good parameters, we propose the following procedure:</p>
<ol type="1">
<li>Load the data in TOPPView</li>
<li>Extract a single scan from the middle of the HPLC gradient (Right click on scan)</li>
<li>Experiment with the parameters until you have found the proper settings</li>
</ol>
<ul>
<li>You can find the <b>NoiseFilters</b>, the <b>BaselineFilter</b>, and the <b>PeakPickers</b> in <b>TOPPView</b> in the menu 'Layer' - 'Apply TOPP tool' </li>
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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 11:19:25 using doxygen 1.8.5</font></TD>
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