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<div class="title">RTModel </div> </div>
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<div class="textblock"><p>Used to train a model for peptide retention time prediction or peptide separation prediction.</p>
<p>For retention time prediction, a support vector machine is trained with peptide sequences and their measured retention times. For peptide separation prediction, two files have to be given: One file contains the positive examples (the peptides which are collected) and the other contains the negative examples (the flowthrough peptides).</p>
<p>These methods and applications of this model are described in the following publications:</p>
<p>Nico Pfeifer, Andreas Leinenbach, Christian G. Huber and Oliver Kohlbacher Statistical learning of peptide retention behavior in chromatographic separations: A new kernel-based approach for computational proteomics. BMC Bioinformatics 2007, 8:468</p>
<p>Nico Pfeifer, Andreas Leinenbach, Christian G. Huber and Oliver Kohlbacher Improving Peptide Identification in Proteome Analysis by a Two-Dimensional Retention Time Filtering Approach J. Proteome Res. 2009, 8(8):4109-15</p>
<p>There are a number of parameters which can be changed for the svm (specified in the ini file and command line): </p>
<ul>
<li>
svm_type: the type of the svm (can be NU_SVR or EPSILON_SVR for RT prediction and is C_SVC for separation prediction) </li>
<li>
kernel_type: the kernel function (e.g., POLY for the polynomial kernel, LINEAR for the linear kernel or RBF for the gaussian kernel); we recommend SVMWrapper::OLIGO for our paired oligo-border kernel (POBK) </li>
<li>
border_length: border length for the POBK </li>
<li>
k_mer_length: length of the signals considered in the POBK </li>
<li>
sigma: the amount of positional smoothing for the POBK </li>
<li>
degree: the degree parameter for the polynomial kernel </li>
<li>
c: the penalty parameter of the svm </li>
<li>
nu: the nu parameter for nu-SVR </li>
<li>
p: the epsilon parameter for epsilon-SVR </li>
</ul>
<p><br/>
</p>
<p>The last five parameters (sigma, degree, c, nu and p) can be used in a cross validation (CV) to find the best parameters according to the training set. Therefore you have to specify the start value of a parameter, the step size in which the parameters should be increased and a final value for the particular parameter such that the tested parameter is never bigger than the given final value. If you want to perform a cross validation for example for the parameter c, enable CV (across all 5 parameters) and set <em>skip_cv</em> to <b>false</b> in the INI file. This can be easily done with using the INIFileEditor.</p>
<p>Furthermore, you can specify the number of partitions for the CV with <b>number_of_partitions</b> in the ini file and the number of runs with <b>number_of_runs</b>.</p>
<p><br/>
Consequently you have two choices to use this application:</p>
<ol>
<li>
Set the parameters of the svm: The RTModel application will train the svm with the training data and store the svm model </li>
<li>
Give a range of parameters for which a CV should be performed: The RTModel application will perform a CV to find the best parameter combination in the given range and afterwards train the svm with the best parameters and the whole training data. Then the model is stored. </li>
</ol>
<p><br/>
The model can be used in <a class="el" href="TOPP_RTPredict.html">RTPredict</a>, to predict retention times for peptides or peptide separation depending on how you trained the model.</p>
<p><b>The command line parameters of this tool are:</b> </p>
<pre class="fragment">
RTModel -- Trains a model for the retention time prediction of peptides from a training set.
Version: 1.11.1 Nov 14 2013, 11:18:15, Revision: 11976
Usage:
RTModel <options>
Options (mandatory options marked with '*'):
-in <file> This is the name of the input file (RT prediction). It is assumed that the
file type is idXML. Alternatively you can provide a .txt file having a sequ
ence and the corresponding rt per line.
(valid formats: 'idXML', 'txt')
-in_positive <file> Input file with positive examples (peptide separation prediction)
(valid formats: 'idXML')
-in_negative <file> Input file with negative examples (peptide separation prediction)
(valid formats: 'idXML')
-out <file>* Output file: the model in libsvm format (valid formats: 'txt')
-svm_type <type> The type of the svm (NU_SVR or EPSILON_SVR for RT prediction, automatically
set
to C_SVC for separation prediction)
(default: 'NU_SVR' valid: 'NU_SVR', 'NU_SVC', 'EPSILON_SVR', 'C_SVC')
-nu <float> The nu parameter [0..1] of the svm (for nu-SVR) (default: '0.5' min: '0'
max: '1')
-p <float> The epsilon parameter of the svm (for epsilon-SVR) (default: '0.1')
-c <float> The penalty parameter of the svm (default: '1')
-kernel_type <type> The kernel type of the svm (default: 'OLIGO' valid: 'LINEAR', 'RBF', 'POLY'
, 'OLIGO')
-degree <int> The degree parameter of the kernel function of the svm (POLY kernel)
(default: '1' min: '1')
-border_length <int> Length of the POBK (default: '22' min: '1')
-max_std <float> Max standard deviation for a peptide to be included (if there are several
ones for one peptide string)(median is taken) (default: '10' min: '0')
-k_mer_length <int> K_mer length of the POBK (default: '1' min: '1')
-sigma <float> Sigma of the POBK (default: '5')
-total_gradient_time <time> The time (in seconds) of the gradient (only for RT prediction) (default:
'1' min: '1e-05')
-first_dim_rt If set the model will be built for first_dim_rt
-additive_cv If the step sizes should be interpreted additively (otherwise the actual
value is multiplied
with the step size to get the new value
Parameters for the grid search / cross validation::
-cv:skip_cv Set to enable Cross-Validation or set to true if the model should just be
trained with 1 set of specified parameters.
-cv:number_of_runs <int> Number of runs for the CV (each run creates a new random partition of the
data) (default: '1' min: '1')
-cv:number_of_partitions <int> Number of CV partitions (default: '10' min: '2')
-cv:degree_start <int> Starting point of degree (default: '1' min: '1')
-cv:degree_step_size <int> Step size point of degree (default: '2')
-cv:degree_stop <int> Stopping point of degree (default: '4')
-cv:p_start <float> Starting point of p (default: '1')
-cv:p_step_size <float> Step size point of p (default: '10')
-cv:p_stop <float> Stopping point of p (default: '1000')
-cv:c_start <float> Starting point of c (default: '1')
-cv:c_step_size <float> Step size of c (default: '10')
-cv:c_stop <float> Stopping point of c (default: '1000')
-cv:nu_start <float> Starting point of nu (default: '0.3' min: '0' max: '1')
-cv:nu_step_size <float> Step size of nu (default: '1.2')
-cv:nu_stop <float> Stopping point of nu (default: '0.7' min: '0' max: '1')
-cv:sigma_start <float> Starting point of sigma (default: '1')
-cv:sigma_step_size <float> Step size of sigma (default: '1.3')
-cv:sigma_stop <float> Stopping point of sigma (default: '15')
Common TOPP options:
-ini <file> Use the given TOPP INI file
-threads <n> Sets the number of threads allowed to be used by the TOPP tool (default:
'1')
-write_ini <file> Writes the default configuration file
--help Shows options
--helphelp Shows all options (including advanced)
</pre><p> <b>INI file documentation of this tool:</b> <div class="ini_global">
<div class="legend">
<b>Legend:</b><br>
<div class="item item_required">required parameter</div>
<div class="item item_advanced">advanced parameter</div>
</div>
<div class="node"><span class="node_name">+RTModel</span><span class="node_description">Trains a model for the retention time prediction of peptides from a training set.</span></div>
<div class="item item_advanced"><span class="item_name" style="padding-left:16px;">version</span><span class="item_value">1.11.1</span>
<span class="item_description">Version of the tool that generated this parameters file.</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="node"><span class="node_name">++1</span><span class="node_description">Instance '1' section for 'RTModel'</span></div>
<div class="item"><span class="item_name" style="padding-left:24px;">in</span><span class="item_value"></span>
<span class="item_description">This is the name of the input file (RT prediction). It is assumed that the file type is idXML. Alternatively you can provide a .txt file having a sequence and the corresponding rt per line.<br></span><span class="item_tags">input file</span><span class="item_restrictions">*.idXML,*.txt</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">in_positive</span><span class="item_value"></span>
<span class="item_description">input file with positive examples (peptide separation prediction)<br></span><span class="item_tags">input file</span><span class="item_restrictions">*.idXML</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">in_negative</span><span class="item_value"></span>
<span class="item_description">input file with negative examples (peptide separation prediction)<br></span><span class="item_tags">input file</span><span class="item_restrictions">*.idXML</span></div> <div class="item"><span class="item_name item_required" style="padding-left:24px;">out</span><span class="item_value"></span>
<span class="item_description">output file: the model in libsvm format</span><span class="item_tags">output file</span><span class="item_restrictions">*.txt</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">svm_type</span><span class="item_value">NU_SVR</span>
<span class="item_description">the type of the svm (NU_SVR or EPSILON_SVR for RT prediction, automatically set<br>to C_SVC for separation prediction)<br></span><span class="item_tags"></span><span class="item_restrictions">NU_SVR,NU_SVC,EPSILON_SVR,C_SVC</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">nu</span><span class="item_value">0.5</span>
<span class="item_description">the nu parameter [0..1] of the svm (for nu-SVR)</span><span class="item_tags"></span><span class="item_restrictions">0:1</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">p</span><span class="item_value">0.1</span>
<span class="item_description">the epsilon parameter of the svm (for epsilon-SVR)</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:24px;">c</span><span class="item_value">1</span>
<span class="item_description">the penalty parameter of the svm</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:24px;">kernel_type</span><span class="item_value">OLIGO</span>
<span class="item_description">the kernel type of the svm</span><span class="item_tags"></span><span class="item_restrictions">LINEAR,RBF,POLY,OLIGO</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">degree</span><span class="item_value">1</span>
<span class="item_description">the degree parameter of the kernel function of the svm (POLY kernel)<br></span><span class="item_tags"></span><span class="item_restrictions">1:∞</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">border_length</span><span class="item_value">22</span>
<span class="item_description">length of the POBK</span><span class="item_tags"></span><span class="item_restrictions">1:∞</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">max_std</span><span class="item_value">10</span>
<span class="item_description">max standard deviation for a peptide to be included (if there are several ones for one peptide string)(median is taken)</span><span class="item_tags"></span><span class="item_restrictions">0:∞</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">k_mer_length</span><span class="item_value">1</span>
<span class="item_description">k_mer length of the POBK</span><span class="item_tags"></span><span class="item_restrictions">1:∞</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">sigma</span><span class="item_value">5</span>
<span class="item_description">sigma of the POBK</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:24px;">total_gradient_time</span><span class="item_value">1</span>
<span class="item_description">the time (in seconds) of the gradient (only for RT prediction)</span><span class="item_tags"></span><span class="item_restrictions">1e-05:∞</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">first_dim_rt</span><span class="item_value">false</span>
<span class="item_description">if set the model will be built for first_dim_rt</span><span class="item_tags"></span><span class="item_restrictions">true,false</span></div> <div class="item"><span class="item_name" style="padding-left:24px;">additive_cv</span><span class="item_value">false</span>
<span class="item_description">if the step sizes should be interpreted additively (otherwise the actual value is multiplied<br>with the step size to get the new value</span><span class="item_tags"></span><span class="item_restrictions">true,false</span></div> <div class="item item_advanced"><span class="item_name" style="padding-left:24px;">log</span><span class="item_value"></span>
<span class="item_description">Name of log file (created only when specified)</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item item_advanced"><span class="item_name" style="padding-left:24px;">debug</span><span class="item_value">0</span>
<span class="item_description">Sets the debug level</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:24px;">threads</span><span class="item_value">1</span>
<span class="item_description">Sets the number of threads allowed to be used by the TOPP tool</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item item_advanced"><span class="item_name" style="padding-left:24px;">no_progress</span><span class="item_value">false</span>
<span class="item_description">Disables progress logging to command line</span><span class="item_tags"></span><span class="item_restrictions">true,false</span></div> <div class="item item_advanced"><span class="item_name" style="padding-left:24px;">test</span><span class="item_value">false</span>
<span class="item_description">Enables the test mode (needed for internal use only)</span><span class="item_tags"></span><span class="item_restrictions">true,false</span></div> <div class="node"><span class="node_name">+++cv</span><span class="node_description">Parameters for the grid search / cross validation:</span></div>
<div class="item"><span class="item_name" style="padding-left:32px;">skip_cv</span><span class="item_value">false</span>
<span class="item_description">Set to enable Cross-Validation or set to true if the model should just be trained with 1 set of specified parameters.</span><span class="item_tags"></span><span class="item_restrictions">true,false</span></div> <div class="item"><span class="item_name" style="padding-left:32px;">number_of_runs</span><span class="item_value">1</span>
<span class="item_description">number of runs for the CV (each run creates a new random partition of the data)</span><span class="item_tags"></span><span class="item_restrictions">1:∞</span></div> <div class="item"><span class="item_name" style="padding-left:32px;">number_of_partitions</span><span class="item_value">10</span>
<span class="item_description">number of CV partitions</span><span class="item_tags"></span><span class="item_restrictions">2:∞</span></div> <div class="item"><span class="item_name" style="padding-left:32px;">degree_start</span><span class="item_value">1</span>
<span class="item_description">starting point of degree</span><span class="item_tags"></span><span class="item_restrictions">1:∞</span></div> <div class="item"><span class="item_name" style="padding-left:32px;">degree_step_size</span><span class="item_value">2</span>
<span class="item_description">step size point of degree</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">degree_stop</span><span class="item_value">4</span>
<span class="item_description">stopping point of degree</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">p_start</span><span class="item_value">1</span>
<span class="item_description">starting point of p</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">p_step_size</span><span class="item_value">10</span>
<span class="item_description">step size point of p</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">p_stop</span><span class="item_value">1000</span>
<span class="item_description">stopping point of p</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">c_start</span><span class="item_value">1</span>
<span class="item_description">starting point of c</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">c_step_size</span><span class="item_value">10</span>
<span class="item_description">step size of c</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">c_stop</span><span class="item_value">1000</span>
<span class="item_description">stopping point of c</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">nu_start</span><span class="item_value">0.3</span>
<span class="item_description">starting point of nu</span><span class="item_tags"></span><span class="item_restrictions">0:1</span></div> <div class="item"><span class="item_name" style="padding-left:32px;">nu_step_size</span><span class="item_value">1.2</span>
<span class="item_description">step size of nu</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">nu_stop</span><span class="item_value">0.7</span>
<span class="item_description">stopping point of nu</span><span class="item_tags"></span><span class="item_restrictions">0:1</span></div> <div class="item"><span class="item_name" style="padding-left:32px;">sigma_start</span><span class="item_value">1</span>
<span class="item_description">starting point of sigma</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">sigma_step_size</span><span class="item_value">1.3</span>
<span class="item_description">step size of sigma</span><span class="item_tags"></span><span class="item_restrictions"> </span></div> <div class="item"><span class="item_name" style="padding-left:32px;">sigma_stop</span><span class="item_value">15</span>
<span class="item_description">stopping point of sigma</span><span class="item_tags"></span><span class="item_restrictions"> </span></div></div>
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