diff principal_component_analysis.xml @ 1:2e7d47c0b027 draft

"planemo upload for repository https://[e-mail]/repos/malex/secimtools"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/principal_component_analysis.xml	Mon Mar 08 22:04:06 2021 +0000
@@ -0,0 +1,95 @@
+<tool id="secimtools_principal_component_analysis" name="Principal Component Analysis (PCA)" version="@WRAPPER_VERSION@">
+    <description>for visual summaries of the components.</description>
+    <macros>
+        <import>macros.xml</import>
+    </macros>
+    <expand macro="requirements" />
+    <command detect_errors="exit_code"><![CDATA[
+principal_component_analysis.py
+--input $input
+--design $design
+--ID $uniqID
+--load_out $loadings
+--score_out $scores
+--summary_out $summary
+--figure $figures
+
+#if $group
+    --group $group
+#end if
+    ]]></command>
+    <inputs>
+        <param name="input" type="data" format="tabular" label="Wide Dataset" help="Input your tab-separated wide format dataset. If file is not tab separated see TIP below."/>
+        <param name="design" type="data" format="tabular" label="Design File" help="Input your design file (tab-separated). Note you need a 'sampleID' column. If not tab separated see TIP below."/>
+        <param name="uniqID" type="text" size="30" value="" label="Unique Feature ID" help="Name of the column in your wide dataset that has unique identifiers."/>
+        <param name="group" type="text" size="30" label="Group/Treatment [Optional]" help="Name of the column in your design file that contains group classifications."/>
+    </inputs>
+    <outputs>
+        <data format="tabular" name="loadings" label="${tool.name} on ${on_string}: loadings"/>
+        <data format="tabular" name="scores" label="${tool.name} on ${on_string}: scores"/>
+        <data format="tabular" name="summary" label="${tool.name} on ${on_string}: summary"/>
+        <data format="pdf" name="figures" label="${tool.name} on ${on_string}: scatter plots"/>
+    </outputs>
+    <tests>
+        <test>
+            <param name="input"  value="ST000006_data.tsv"/>
+            <param name="design" value="ST000006_design.tsv"/>
+            <param name="uniqID" value="Retention_Index" />
+            <param name="group"  value="White_wine_type_and_source" />
+            <output name="loadings" file="ST000006_principal_component_analysis_load_out.tsv" />
+            <output name="scores"   file="ST000006_principal_component_analysis_score_out.tsv" />
+            <output name="summary"  file="ST000006_principal_component_analysis_summary_out.tsv" />
+            <output name="figures"  file="ST000006_principal_component_analysis_figure.pdf" compare="sim_size" delta="10000" />
+        </test>
+    </tests>
+    <help><![CDATA[
+
+@TIP_AND_WARNING@
+
+**Tool Description**
+
+The tool performs principal component analysis (PCA) of the data.
+Visual summaries are provided in the from of 2D and 3D scatter plots for the first three principal components.
+Samples in the scatter plots are colored based on the group classification.
+
+--------------------------------------------------------------------------------
+
+**Note**
+
+- This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might 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_OPTIONAL@
+
+--------------------------------------------------------------------------------
+
+**Output**
+
+Four different outputs are produced by the Principal Component Analysis tool:
+
+(1) a TSV file containing eigenvectors/variable loadings
+(2) a TSV file containing scores of input data on principal components
+(3) a TSV file with the summary for each component
+(4) and a PDF file of scatter plots of the first three principal components
+
+There are a total of four scatterplots: three pairwise plots for the first three components and a single 3D plot of the first three components.
+
+    ]]></help>
+    <expand macro="citations"/>
+</tool>