Mercurial > repos > timpalpant > java_genomics_toolkit
diff galaxy-conf/KMeans.xml @ 24:a77e126ae856 draft
Reupload since last upload did not load correctly
author | timpalpant |
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date | Tue, 19 Jun 2012 22:15:09 -0400 |
parents | 9d56b5b85740 |
children | b43c420a6135 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/galaxy-conf/KMeans.xml Tue Jun 19 22:15:09 2012 -0400 @@ -0,0 +1,41 @@ +<tool id="KMeans" name="KMeans cluster" version="1.0.0"> + <description>an aligned matrix</description> + <command interpreter="sh">galaxyToolRunner.sh visualization.KMeans -i $input -k $K -1 $min -2 $max -o $output</command> + <inputs> + <param format="tabular" name="input" type="data" label="Aligned matrix" /> + <param name="K" type="integer" value="10" label="Number of clusters" /> + <param name="min" type="integer" value="1" label="Minimum column to use for clustering" /> + <param name="max" type="integer" value="-1" label="Maximum column to use for clustering (-1 to end)" /> + </inputs> + <outputs> + <data format="tabular" name="output" metadata="input" /> + </outputs> + <tests> + </tests> + + <help> + +.. class:: warningmark + +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. + +.. _matrix2png: http://bioinformatics.ubc.ca/matrix2png/dataformat.html + +.. class:: infomark + +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. + +.. class:: infomark + +**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. + +----- + +This tool will cluster the rows in an aligned matrix with KMeans_. The implementation builds upon the KMeansPlusPlusClusterer available in commons-math3_. + +.. _KMeans: http://en.wikipedia.org/wiki/K-means_clustering + +.. _commons-math3: http://commons.apache.org/math/ + + </help> +</tool>