[BioC] Unexpected output from Limma background subtraction & Normalization

[Ellen Sebastian]

Hello,

I realize this is a kind of vague question, but I'm getting some very
strange output by using Limma for background subtraction and normalization.
Compared to normalization that I had done separately (in PUMA, the
Princeton microarray database), the spread of red-green ratios is extremely
low.  This happens regardless of what background subtraction methods and
normalization schemes I use, but worst with Loess. (See attached file,
which plots SD of red/green ratio on the X axis - all SDs are less than 1.)
Output was also similar using Marray for normalization.

Can anyone see any obvious mistakes in how I'm handling my data for
background subtraction & normalization?

Thanks very much for any hints you can offer...

for (i in length(files)
    RGraws[[i]]<-read.maimages(files[i],"genepix",wt.fun=wtflags(0.1),
verbose=TRUE)
    #read in files ending with ".gpr". EmptyFlags set flagged and empty
spots' weights to 0, all others to 1.

for (i in length(files)){
        normalized<-normalizeWithinArrays(RGraws[[i]], layout =
RGraws[[i]]$printer, method=NormMethod,  span=0.3, iterations=4,
controlspots=NULL, df=5, robust="M", bc.method=BGsubmethod, offset=0)
          #NormMethod is either "loess" or "median"; BGsubmethod is either
"edwards" or "subtract"

          output <- cbind(output, normalized$M)
          # use $M as my red/green ratio output
}

-- 
Ellen Sebastian
B.S. Candidate, Biomedical Computation
Stanford University, Class of 2015
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