Changeset 90

Timestamp:

Apr 4, 2006, 1:56:22 PM (15 years ago)

Author:

Jari Häkkinen

Message:

New plugin definition file.

File:

• trunk/se/lu/thep/wenni/bin/base_plugin_script/plugin_WeNNI.base (modified) (1 diff)

trunk/se/lu/thep/wenni/bin/base_plugin_script/plugin_WeNNI.base

@@ -2 +2 @@
 section plugin
 uniqueName  thep.lu.se/jari/wenni
-versionNumber 0.5
+versionNumber 0.6
 name  Transformation: WeNNI
-descr Weighted nearest neighbour imputation, WeNNI.\r\n\r\nMicroarray technology has become popular for gene expression profiling, and many analysis tools have been developed for data interpretation. Many of these tools require complete data, but measurement values are often missing. A way to overcome the problem of incomplete data is to impute the missing data before analysis. Many imputation methods have been suggested, some naive and others more sophisticated taking into account correlations in data. WeNNI is an approach in which a spot quality weight is built into the imputation methods, allowing for smooth imputations of all spots to larger or lesser degree.\r\n\r\nThe method is described in a paper submitted for publication. The paper compares the performance of WeNNI with other imputation schemes.\r\n\r\nFeedback, suggestions for improvements, criticism, or questions regarding the algorithm should be sent to Peter Johansson, peter\@thep.lu.se, or Jari H\344kkinen, jari\@thep.lu.se.
+descr Weighted nearest neighbour imputation, WeNNI.\r\n\r\nWeNNI is a method for imputing unreliable expression data values. WeNNI imputes the missing values by using continuous spot quality weights. In WeNNI the quality of data values are not divided into groups of either missing or present, but rather a continuous quality weight is assigned to each data value.\r\n\r\nThe imputation performed by WeNNI is controlled with two parameters: the number of nearest neighbours to consider in the calculations and beta that is used in the calculation of weights. In the present version of WeNNI, the quality weights are calculated from a signal-to-noise ratio. The results presented in the forthcoming WeNNI publication show that the choice of parameters is not crucial, and a value around 10 as the number of nearest neighbours and a beta in the range 0.1 to 1 is suggested when parameter tuning cannot be performed.\r\n\r\nWeNNI is described in a paper submitted to a peer-reviewed publication. In the paper, we assessed imputation methods on three data sets containing replicate measurements. Of the compared methods, best performance, and robustness were achieved with WeNNI.\r\n\r\nFeedback, suggestions for improvements, criticism, or questions regarding the algorithm should be sent to Peter Johansson, peter\@thep.lu.se, or Jari H\344kkinen, jari\@thep.lu.se.
 execName  thep.lu.se/jari/wenni/wenni.pl
 geneAverages  0
 serialFormat  0
-url http://lev.thep.lu.se/trac/baseplugins
+url http://lev.thep.lu.se/trac/baseplugins/wiki/WeNNI
 minChannels 2
 maxChannels 2
-leaveStdin  1
-leaveStdout 1
+leaveStdin  0
+leaveStdout 0
 estimatedTime 3600
 defaultMaxRam 134217728