Mercurial > repos > timpalpant > java_genomics_toolkit

annotate galaxy-conf/KMeans.xml @ 25:b43c420a6135 draft default tip

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Incorporate fix: https://github.com/timpalpant/java-genomics-toolkit/commit/9a6c61b7c6b8d85a1cd3f595eed657a537b85dc9
author timpalpant
date Sat, 09 Feb 2019 14:02:24 -0500
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1 <tool id="KMeans" name="KMeans cluster" version="1.0.0">
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2 <description>an aligned matrix</description>
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3 <command interpreter="bash">galaxyToolRunner.sh visualization.KMeans -i $input -k $K -1 $min -2 $max -o $output</command>
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4 <inputs>
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5 <param format="tabular" name="input" type="data" label="Aligned matrix" />
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6 <param name="K" type="integer" value="10" label="Number of clusters" />
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7 <param name="min" type="integer" value="1" label="Minimum column to use for clustering" />
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8 <param name="max" type="integer" value="-1" label="Maximum column to use for clustering (-1 to end)" />
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9 </inputs>
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10 <outputs>
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11 <data format="tabular" name="output" metadata="input" />
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12 </outputs>
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13 <tests>
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14 </tests>
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15
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16 <help>
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17
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18 .. class:: warningmark
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20 This tool requires tabular data in matrix2png format (with column AND row headers). For more information about the required format and usage instructions, see the matrix2png_ website.
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22 .. _matrix2png: http://bioinformatics.ubc.ca/matrix2png/dataformat.html
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24 .. class:: infomark
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26 You can use the "Align values in a matrix" tool to create a matrix, then use this tool to cluster the matrix with k-means.
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28 .. class:: infomark
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30 **TIP:** You can use the **min** and **max** columns to cluster a large matrix based on a subset of the columns. For example, you could cluster a 4000x4000 matrix on columns 200-300 by setting min = 200 and max = 300. This will greatly increase the efficiency of distance calculations during the k-means EM, and also allows you to cluster based on specific regions, such as promoters or coding sequences.
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32 -----
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34 This tool will cluster the rows in an aligned matrix with KMeans_. The implementation builds upon the KMeansPlusPlusClusterer available in commons-math3_.
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36 .. _KMeans: http://en.wikipedia.org/wiki/K-means_clustering
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38 .. _commons-math3: http://commons.apache.org/math/
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40 </help>
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41 </tool>