down-weighting control probes in limma (with Agilent arrays)?
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@daniel-aaen-hansen-5052
Last seen 10 months ago
Denmark
Dear List, I have a question regarding what weighting function to use when analyzing Agilent data with limma. My question is whether or not it makes sense to down-weight the control probes? I have defined a weighting function: wtfun.Agilent = function(x) { okPopnOutlier = x[,"rIsFeatPopnOL"]==0 & x[,"gIsFeatPopnOL"]==0 okNonUnifOutlier = x[,"rIsFeatNonUnifOL"]==0 & x[,"gIsFeatNonUnifOL"]==0 okControlType = x[,"ControlType"]==0 as.numeric(okPopnOutlier & okNonUnifOutlier & okControlType) } And I would then use read.maimages() to read the data: RG = read.maimages(targets, source="agilent", wt.fun=wtfun.Agilent) This line assigns weight 0 (FALSE) to control probes and weight 1 (TRUE) to non-control probes: okControlType = x[,"ControlType"]==0 As far as I can see, the normalization methods don't make explicit use of the control probes and hence I would think it would be a good idea to down-weight them. Any opinions on this? Are there any way to make use of the controls? Best, Daniel
Normalization limma Normalization limma • 693 views
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