Mercurial > repos > malex > secimtools
diff partial_least_squares.xml @ 1:2e7d47c0b027 draft
"planemo upload for repository https://malex@toolshed.g2.bx.psu.edu/repos/malex/secimtools"
author | malex |
---|---|
date | Mon, 08 Mar 2021 22:04:06 +0000 |
parents | |
children | caba07f41453 |
line wrap: on
line diff
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/partial_least_squares.xml Mon Mar 08 22:04:06 2021 +0000 @@ -0,0 +1,145 @@ +<tool id="secimtools_partial_least_squares" name="Partial Least Squares Discriminant Analysis (PLS-DA)" version="@WRAPPER_VERSION@"> + <description></description> + <macros> + <import>macros.xml</import> + </macros> + <expand macro="requirements" /> + <command detect_errors="exit_code"><![CDATA[ +partial_least_squares.py +--input $input +--design $design +--ID $uniqID +--group $group +--toCompare "$toCompare" +--cross_validation $cross_validation +--nComp $nComp +--outScores $outScores +--outWeights $outWeights +--outClassification $outClassification +--outClassificationAccuracy $outClassificationAccuracy +--figure $figures + ]]></command> + <inputs> + <param name="input" type="data" format="tabular" label="Wide Dataset" help="Input your tab-separated wide format dataset. If file is not tab separated see TIP below."/> + <param name="design" type="data" format="tabular" label="Design File" help="Input your design file (tab-separated). Note you need a 'sampleID' column. If not tab separated see TIP below."/> + <param name="uniqID" type="text" size="30" value="" label="Unique Feature ID" help="Name of the column in your wide dataset that has unique identifiers."/> + <param name="group" type="text" size="30" label="Group/Treatment" help="Name of the column in your design file that contains group classifications."/> + <param name="toCompare" type="text" size="30" label="Names of the Groups to Compare" help="Names of the two groups to compare. The user should insure that group names do not contain commas. The separator for the two groups should only include commas (no spaces)."/> + <param name="cross_validation" type="select" size="30" display="radio" value="double" label="Cross-Validation Options"> + <option value="none">None</option> + <option value="single">Single</option> + <option value="double">Double</option> + </param> + <param name="nComp" type="text" size="30" value="2" label="Number of Components" help="Number of components for the analysis to use (default = 2). This field is used only when the cross validation field is set to none."/> + </inputs> + <outputs> + <data format="tabular" name="outScores" label="${tool.name} on ${on_string}: Scores"/> + <data format="tabular" name="outWeights" label="${tool.name} on ${on_string}: Weights"/> + <data format="tabular" name="outClassification" label="${tool.name} on ${on_string}: Classification of Samples"/> + <data format='tabular' name="outClassificationAccuracy" label="${tool.name} on ${on_string}: Classification Accuracy of Samples"/> + <data format="pdf" name="figures" label="${tool.name} on ${on_string}: Scatter Plots"/> + </outputs> + <tests> + <test> + <param name="input" value="ST000006_data.tsv"/> + <param name="design" value="ST000006_design_group_name_underscore.tsv"/> + <param name="uniqID" value="Retention_Index" /> + <param name="group" value="White_wine_type_and_source" /> + <param name="toCompare" value="Chardonnay_ Napa_ CA 2003,Riesling_ CA 2004" /> + <param name="cross_validation" value="none"/> + <param name="nComp" value="2"/> + <output name="outScores" file="ST000006_partial_least_squares_none_scores.tsv" /> + <output name="outWeights" file="ST000006_partial_least_squares_none_weights.tsv" /> + <output name="outClassification" file="ST000006_partial_least_squares_none_classification.tsv" /> + <output name="outClassificationAccuracy" file="ST000006_partial_least_squares_none_classification_accuracy.tsv" /> + <output name="figures" file="ST000006_partial_least_squares_none_figure.pdf" compare="sim_size" delta="10000"/> + </test> + </tests> + <help><![CDATA[ + +@TIP_AND_WARNING@ + +**Tool Description** + +The tool performs partial least square discriminant analysis (PLS-DA) for two treatment groups selected by the user. + +**NOTE: A minimum of 100 samples is required by the tool for single or double cross validation** + +The subspace dimension defines the number of components that will be used to describe the variability within the data. +The user can specify subspace dimension in the range of two to the sample number. +The user has the option to specify the dimension of the subspace directly (Default =2) or to perform single or double cross-validation to determine the dimension of the subspace. + +For single and double cross-validation: the data set is split differently when the model fit is performed. + +For double cross-validation: the data set is split into pieces and the model fit is performed on one piece using cross-validation and evaluated on the other pieces. + +For single cross-validation: the same data are used to fit the model and to evaluate the model using three-fold cross validation. + +More details can be found in: + +Geladi, Paul, and Bruce R. Kowalski. "Partial least-squares regression: a tutorial." Analytica chimica acta 185 (1986): 1-17. + + +-------------------------------------------------------------------------------- + +**Note** + +- This tool currently treats all variables as continuous numeric + variables. Running the tool on categorical variables may result in + incorrect results. +- Rows containing non-numeric (or missing) data in any + of the chosen columns will be skipped from the analysis. + +-------------------------------------------------------------------------------- + +**Input** + + - Two input datasets are required. + +@WIDE@ + +**NOTE:** The sample IDs must match the sample IDs in the Design File (below). +Extra columns will automatically be ignored. + + +@METADATA@ + +@UNIQID@ + +@GROUP@ + + +**Names of the Groups to Compare** + + - Comma separated names of the two groups in your Group/Treatment column that you want to compare. The user should ensure that group names do not contain commas. The separator for the two groups should only include commas (no spaces). + +**Cross-Validation Options** + + - The choice of cross-validation options available for the user. None corresponds to no cross-validation when the user specifies the number of components manually. + +**Number of Components** + + - The parameter is used only when the "None" cross-validation option is selected. If the field is left blank, the number of components is set to the default value (2). +-------------------------------------------------------------------------------- + +**Output** + + +Three different files are generated: + +(1) a TSV file containing the scores produced by the model for each sample + +(2) a TSV file containing the weights produced by the model for each feature. + +(3) a TSV file containing the classification produced by the model for each sample. + +(4) a TSV file containing the algorithm classification accuracy (in percent). + +(5) a PDF file containing the 2D plots for all pairwise comparisons of components between the two treatment groups. + +**NOTE:** Regardless how many components are selected for the algorithm, pairwise 2D plots are produced for the pairs of components. +Increasing the number of components will increase the number of plots produced. + + ]]></help> + <expand macro="citations"/> +</tool>