[BioC] Weight functions for Agilent chips (limma)
Giovanni Coppola
gcoppola at ucla.edu
Tue Sep 28 00:11:40 CEST 2004
Hi Joel,
thanks for the function, which is useful in excluding flagged features in a
way similar to the Imagene files.
A simple barplot can give you an idea of the valid spots:
barplot(RG$weights,ylab="OK spots")
Last step, I guess you want to replace the 'g' with a 'r' in the second column:
>wtAgilent.mRGOLFilter <- function(qta) {
> mapply(min,1-qta[,"gIsFeatNonUnifOL"],1-qta[,"rlsFeatNonUnifOL"],
> 1-qta[,"gIsBGNonUnifOL"],1-qta[,"rIsBGNonUnifOL"],
> 1-qta[,"gIsFeatPopnOL"],1-qta[,"rIsFeatPopnOL"],
> 1-qta[,"gIsBGPopnOL"],1-qta[,"rIsBGPopnOL"])
> }
Best regards
Giovanni
At 05:30 PM 9/23/2004, you wrote:
>Hi Giovanni,
>
>Sorry, being new to R programs, it took me some time to parse your own.
>
>Could something of the sort be useful to you?
>
>wtAgilent.mRGOLFilter <- function(qta) {
> mapply(min,1-qta[,"gIsFeatNonUnifOL"],1-qta[,"gIsFeatNonUnifOL"],
> 1-qta[,"gIsBGNonUnifOL"],1-qta[,"gIsBGNonUnifOL"],
> 1-qta[,"gIsFeatPopnOL"],1-qta[,"gIsFeatPopnOL"],
> 1-qta[,"gIsBGPopnOL"],1-qta[,"gIsBGPopnOL"])
> }
>I don't see (visually) much difference (for this data set) between this
>last filter and the previous
>
>wtAgilent.mRGFilter <- function(qta) {
>mapply(min,qta[,"gIsPosAndSignif"],qta[,"rIsPosAndSignif"]) }
>
>best regards,
>
>Joel
>
>P.S. For completeness sake, the Agilent manual describes 8 0/1 variables
>related to outliers:
>
>"gIsFeatNonUnifOL" and "rIsFeatNonUnifOL" :
> a value of 1 indicates Feature is a non-uniformity outlier in g(r)
> Boolean flag indicating
> if a feature is a NonUniformity Outlier or not. A feature is
> non-uniform if the pixel noise
> of feature exceeds a threshold established for a "uniform" feature.
>
>"gIsBGNonUnifOL" and " rIsBGNonUnifOL" :
> a value of 1 indicates Local background is a non-uniformity outlier
> in g(r).
>
>"gIsFeatPopnOL" and " rIsFeatPopnOL" :
> a value of 1 indicates Feature is a population outlier in g(r).
> Probes with replicate features on
> a microarray are examined using population statistics. A feature is a
> population outlier if its
> signal is less than a lower threshold or exceeds an upper threshold
> determined using the
> interquartile range (i.e., IQR) of the population.
>
>"gIsBGPopnOL" and "rIsBGPopnOL" :
> a value of 1 indicates local background is a population outlier in g(r).
>
> > Hi Joel,
> > actually, I was trying to use the 8 outlier fields (columns AZ-BG of
> the Agilent output file) in a way similar to the 'flag' fields of other
> platfoms...
> > So, I started with
> > mywtfun <- function(exclude.flags=c(1,2,3)) function(obj)
> > 1-(obj$rIsBGPopnOL %in% exclude.flags)
> > and thenRG <-read.maimages(...blabla... wt.fun=mywtfun(c(1)))
> >
> > and maybe there's a way to include the other fields as well....
> > Any other ideas?
> >
> > Cheers
> > Giovanni
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