Mercurial > repos > vandelj > giant_hierarchical_clustering
diff galaxy/wrappers/ExprHeatmapClustering.xml @ 3:dd0f4da5f68f draft
Uploaded
author | vandelj |
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date | Tue, 15 Sep 2020 15:54:23 +0000 |
parents | 0b09345fa632 |
children | d75a74a93587 |
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--- a/galaxy/wrappers/ExprHeatmapClustering.xml Mon Sep 14 13:17:45 2020 +0000 +++ b/galaxy/wrappers/ExprHeatmapClustering.xml Tue Sep 15 15:54:23 2020 +0000 @@ -1,4 +1,4 @@ -<tool name="GIANT-Heatmap and Hierarchical clustering" id="giant_hierarchical_clustering" version="0.5.2"> +<tool name="GIANT-Heatmap and Hierarchical clustering" id="giant_hierarchical_clustering" version="0.5.3"> <description>Run hierarchical clustering and plot heatmap from expression data and/or differential expression analysis</description> <requirements> <requirement type="package" version="4.8.0">r-plotly</requirement> @@ -619,13 +619,16 @@ <option value="log2">Base 2 Logarithm</option> </param> - <param name="distanceMeasure" type="select" label="Distance measure used for clustering" help="See documentation of 'Dist' R package for more information"> + <param name="distanceMeasure" type="select" label="Distance measure used for clustering"> <option value="euclidean" selected="true">euclidean</option> <option value="manhattan">manhattan</option> <option value="binary">binary</option> <option value="pearson">pearson</option> <option value="spearman">spearman</option> <option value="kendall">kendall</option> + <option value="absPearson">absolute pearson</option> + <option value="absSpearman">absolute spearman</option> + <option value="absKendall">absolute kendall</option> </param> <param name="aggloMethod" type="select" label="Agglomeration method used for clustering" help="See documentation of 'hclust' R method for more information"> @@ -879,7 +882,7 @@ \- **Mathematical transformation** can be applied to clustered data before clustering and visualization. Data used for the filtering step are not modified by this transformation. -\- **Distance measure** used to cluster rows and columns. +\- **Distance measure** used to cluster rows and columns. For "euclidean", "manhattan" and "binary" distances the 'dist' R function is directly called, for "pearson", "spearman" and "kendall" distances the '(1-correlation)/2' term is used as a classical distance, for "absPearson", "absSpearman" and "absKendall" the 'abs(1-correlation)'' term is used. \- **Agglomeration method** used to cluster rows and columns.