Description Usage Arguments Details Value Author(s) References Examples
Dimension Reduction for Array CGH Data with Minimal Information Loss
1 | CGHregions(input, averror=0.01)
|
input |
An object of class |
averror |
Maximal information loss allowed. |
Please read the article and the supplementary information for detailed information on the algorithm.
If the input is not an object of class cghCall
it should be either a dataframe or a tabseparated textfile (textfiles must contain a header). The first three columns should contain the name, chromosome and position in bp for each array target respectively. The chromosome and position column must contain numbers only. Following these is a column with log2 ratios for each of your samples. If the input type is a textfile, missing values should be represented as 'NA' or an empty field.
The algorithm reduces the call matrix to a smaller matrix that contains regions rather than individual clones.
The regions consist of consequtive clones the signatures of which are very much alike. The dimension reduction
is potentially for testing and clustering puposes. The amount of information lost by this dimension
reduction is controlled by averror
. The larger averror
, the less regions will result.
This function returns an object of class cghRegions
Mark van de Wiel and Sjoerd Vosse Maintainer: Mark van de Wiel <mark.vdwiel@vumc.nl>
Mark A. van de Wiel and Wessel N. van Wieringen (2007). CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss. Cancer Informatics, 2, 55-63.
1 2 | data(WiltingCalled)
result <- CGHregions(WiltingCalled)
|
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