At 05:28 AM 1/09/2004, you wrote: >The reason that we want to read in more columns is to create the >flags. Some people think that spots should be flagged if (e.g.) mean(Rf) >differs considerably from median(Rf), or the s.d. of one of these measures >is large. Right now, they need to create the flags outside of Bioconductor. I think you might want something like myfun <- function(x) { okred <- abs(x[,"F635 Median"]-x[,"F635 Mean"]) < 50 okgreen <- abs(x[,"F532 Median"]-x[,"F532 Mean"]) < 50 as.numeric(okgreen & okred) } RG <- read.maimages(files, source="genepix", wt.fun=myfun) Then all the "bad" spots will get weight zero which, in limma, is equivalent to flagging them out. You can proceed with RG$printer <- getLayout(RG$genes) RG <- backgroundCorrect(RG) # gives more correction options MA <- normalizeWithinArrays(RG) to do print-tip loess normalization in which the flagged spots have no influence on the normalization. Gordon >--Naomi > >At 09:28 AM 8/31/2004 +1000, you wrote: >>At 11:33 PM 30/08/2004, Naomi Altman wrote: >>>The vignettes are great - perhaps I should not call them >>>"tutorials". But like other documentation of this type (the book "SAS >>>for Mixed Models" comes to mind), it is hard to generalize from the >>>examples. We need both the vignettes and the internal >>>documentation. We need good but explicit defaults for the general user, >>>and the option to change these defaults for the expert user. >>> >>>Here is an example where the documentation is OK, but the option to >>>change the defaults is too limited. >>> >>>Both limma and marray allow the user read only a limited set of columns >>>from gpr and spot files. Why not have this as the default, and let the >>>user decide if they want to read in other columns? Some of my clients >>>like to filter spots based on quantities like the difference between the >>>median and mean spot intensity, the sd of intensity, etc. They >>>currently need to flag spots before importing into Bioconductor because >>>they cannot read these other columns readily into an marrayRaw object. >> >>The wt.fun argument to read.maimages() function in limma already provides >>the capability to filter or weights spots based on any number of columns >>in the original file. So there no need to read in the extra columns or to >>flag spots before importing. The computation of the flags is done at the >>time of import. >> >>The help document for read.maimages() says: >> Spot quality weights may be extracted from the image analysis >> files using a ready-made or a user-supplied weight function >> 'wt.fun'. 'wt.fun' may be any user-supplied function which accepts >> a data.frame argument and returns a vector of non-negative >> weights. The columns of the data.frame are as in the image >> analysis output files. See 'QualityWeights' for provided weight >> functions. >> >>I admit that this is brief, but it does seem explicit. >> >>I know that reading in extra columns can be convenient for other >>purposes. The reason why I decided not to implement this in limma was >>explained in a post to this list on 22 July: >>https://www.stat.math.ethz.ch/pipermail/bioconductor/2004-July/00543 4.html >> >>Gordon >> >>>--Naomi >>> >>>Naomi S. Altman 814-865-3791 (voice) >>>Associate Professor >>>Bioinformatics Consulting Center >>>Dept. of Statistics 814-863-7114 (fax) >>>Penn State University 814-865-1348 (Statistics) >>>University Park, PA 16802-2111 >> >>_______________________________________________ >>Bioconductor mailing list >>Bioconductor@stat.math.ethz.ch >>https://stat.ethz.ch/mailman/listinfo/bioconductor > >Naomi S. Altman 814-865-3791 (voice) >Associate Professor >Bioinformatics Consulting Center >Dept. of Statistics 814-863-7114 (fax) >Penn State University 814-865-1348 (Statistics) >University Park, PA 16802-2111