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1 <tool id="dichotomize_sample_clusters" name="Dichotomize Previous Sample Cluster Result" force_history_refresh="True">
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2 <command interpreter="python">dichotomize.sample.clusters.py
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3 -d $dataset
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4 #if str($k_range) != "-1":
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5 -k ${k_range}
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6 #end if
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7 -o ${output_fname}
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8
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9 </command>
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10 <inputs>
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11 <param format="rdata" name="dataset" type="data" label="Sample Clustering Classification" help="Cluster result file from CCPLUS, HAC, or PAM"/>
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12 <param name="k_range" type="text" label="Range of K to Dichotomize" value="-1" help="Specify the number of clusters to use (-1 to use default; see help below)"/>
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13 </inputs>
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14 <outputs>
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15 <data format="tabular" name="output_fname" label="Dichotomized Sample Clusters (phenotype)"/>
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16 </outputs>
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17 <help>
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18 - **Sample Clustering Classification Result** - Specify the cluster result to analayze (MUST BE IN rdata format), and must contain the same objects that are produced by the 'Partition Clustering,' 'Hierarchical Clustering (HAC),' or 'Consensus Clustering' tools.
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19
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20 - **Range of K to Dichotomize** Specify the maximum number of clusters to use; use -1 to indicate to use the default cluster size (i.e. if K-means and K was 5, 5 will be used)
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21 * **To specify a range of K's to explore** users can use characters like commas (,), hyphens (-) and colons (:) to specify ranges.
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22 * **Examples:**
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23 * 2,4,6 - would generate a dictotomy for K's equal to 2, 4 and 6
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24 * 2,4:6 - would generate a dictotomy for K's equal to 2, 4, 5 and 6
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25 * 2,4-6 - would generate a dictotomy for K's equal to 2, 4, 5 and 6
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26 * 2, 5:7, 9, 11-14 - would generate a dictotomy for K's equal to 2, 5, 6, 7, 9, 11, 12, 13 and 14.
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27
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28 - **NOTE,** however, the following limitations:
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29 * IF cluster result is a **Partition** clustering (K-Means or PAM) - users **CAN NOT** specify alternate choices of K.
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30 * IF cluster result is a result from the **Select K from Consensus Clustering** or **Extract Clustering from Consensus Clustering** tools - users **CAN NOT** specify alternate choices of K.
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31 * IF cluster results is a "raw" result from Consensus Clustering, only the range of K's that Consensus Clustering considered can be used as alternate choices of K. Alternate choices of K outside of that range will be ignored.
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32 * IF cluster result is a **Hierarchical** clustering - alternate choices of K that are greater than the total number of samples will be ignored
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33
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34 </help>
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35 </tool>
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