[Blends-commit] r1914 - projects/med/trunk/debian-med/tasks

[Debian Pure Blends Subversion Commit]

Author: tille
Date: Wed Oct  7 09:20:24 2009
New Revision: 1914
URL: http://svn.debian.org/viewsvn/blends?rev=1914&view=rev

Log:
Added compclust


Modified:
   projects/med/trunk/debian-med/tasks/bio

Modified: projects/med/trunk/debian-med/tasks/bio
URL: http://svn.debian.org/viewsvn/blends/projects/med/trunk/debian-med/tasks/bio?rev=1914&view=diff&r1=1914&r2=1913&p1=projects/med/trunk/debian-med/tasks/bio&p2=projects/med/trunk/debian-med/tasks/bio
==============================================================================
--- projects/med/trunk/debian-med/tasks/bio	(original)
+++ projects/med/trunk/debian-med/tasks/bio	Wed Oct  7 09:20:24 2009
@@ -3077,3 +3077,25 @@
  the lab of Paul Sharp,  Dept of Genetics,  University of Nottingham. John is working
  in human genetics and is currently employed as ProCardis database manager at the WTCHG
  in Oxford University.
+
+Depends: compclust
+Homepage: http://woldlab.caltech.edu/compclust/
+License: MLX (http://woldlab.caltech.edu/compclust/LICENSE.txt)
+Language: Python
+X-Category: Genomics; Clustering analysis (+GUI)
+Pkg-URL: http://woldlab.caltech.edu/compclust/debian_install.shtml
+Pkg-Description: explore and quantify relationships between clustering results
+ CompClust is a python package written using the pyMLX and IPlot APIs. It provides
+ software tools to explore and quantify relationships between clustering results. Its
+ development has been largely built around needs of microarray data analysis but could
+ be easily used in other domains.
+ .
+ Briefly pyMLX provides for efficient and convenient execution of many clustering
+ algorithms using a extendable library of algorithms. It also provides many-to-many
+ linkages between data features and annotations (such as cluster labels, gene names,
+ gene ontology information, etc.) These linkages persist through varied data
+ manipulations. IPlot provides an abstraction of the plotting process in which any
+ arbitrary feature or derived feature of the data can be projected onto any feature
+ of the plot, including the X,Y coordinates of points, marker symbol, marker size,
+ maker/line color, etc. These plots are intrinsically linked to the dataset, the
+ View and the Labeling classes found within pyMLX.