Changeset 826


Ignore:
Timestamp:
Nov 27, 2008, 12:32:45 AM (13 years ago)
Author:
Jari Häkkinen
Message:

Fixes #148.

Location:
plugins/base1/se.lu.thep.wenni/trunk
Files:
3 edited

Legend:

Unmodified
Added
Removed
  • plugins/base1/se.lu.thep.wenni/trunk/base/base1/base_plugin_script/Makefile.am

    r825 r826  
    2929
    3030plugin_WeNNI.base: plugin_WeNNI.base.in Makefile
    31   @sed "s/MAKESUBSTOFVERSIONNUMBER/$(VERSION)/" $< > $@
     31  @$(SED) -e "s|MAKESUBSTOFVERSIONNUMBER|$(VERSION)|" \
     32          -e "s|MAKESUBSTOFEXECNAME|$(BASEPLUGINEXECNAME)|" $< > $@
  • plugins/base1/se.lu.thep.wenni/trunk/base/base1/base_plugin_script/plugin_WeNNI.base.in

    r825 r826  
    55name  Transformation: WeNNI
    66descr 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 WeNNI publication (referenced below) 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 "Improving missing value imputation of microarray data by using spot quality weights", P. Johansson and J. H\344kkinen, BMC Bioinformatics 7, 306 (2006). In this 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\nComments on WeNNI:\r\n\r\n1) The notion of weights becomes obsolete after running WeNNI, i.e., do not use the weight fed into WeNNI in any subsequent analysis because all weights are now strictly 1.\r\n\r\n2) Running WeNNI as a BASE plug-in makes WeNNI destined to impute log ratios of channel 1 and channel 2 (M values in BASE world). A consequence of imputing log ratios is that a change in ratio cannot be assigned to a specific channel. This implies that log(channel1*channel2) (A values in BASE world) become undefined and useless. However, on request from BASE users it was decided that A values should not be affected by the transformation in cases where the A value is well defined before imputation. In cases when an A value do not exist before transformation (i.e. channel1<=0 or channel2<=0) it was decided that A should be set to 0. NOTE, this does not change the underlying WeNNI algorithm in any way but is rather conventions needed for BASE plug-in usage.\r\n\r\n3) It is recommended to filter reporter on presence before running WeNNI. The filter can be liberal, say 50%. You can perform the presence filter in the "Gene filter" box above. Select "In # of Assays".\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.
    7 execName  thep.lu.se/jari/wenni/wenni.pl
     7execName  MAKESUBSTOFEXECNAME
    88geneAverages  0
    99serialFormat  0
  • plugins/base1/se.lu.thep.wenni/trunk/configure.ac

    r825 r826  
    9494
    9595if (test x$state = xbase1); then
     96  # check for BASE1 API
    9697  OLD_CPPFLAGS=$CPPFLAGS;
    9798  OLD_LDFLAGS=$LDFLAGS;
     
    107108  AC_SUBST(BASECPPFLAGS)
    108109  AC_SUBST(BASELDFLAGS)
     110  # Set BASE1 plug-in name
     111  BASEPLUGINEXECNAME=$PLUGINDIR/wenni.pl
     112  AC_SUBST(BASEPLUGINEXECNAME)
    109113fi
    110114if (test x$state = xbase2); then
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