Hi Jenny,

Thanks a lot for the valuable information. I will try to do loess first and
tehn doa scale if necessary. With regarding the correlation in the LmFit, my
the spots in the array are not evenly spaced and not evenly replicated, 90%
spots are spotted twice, 8% are thrice and 2% spots are spotted 26 times (
all are oligos, excluding controls). I found this code in a posting in the
Limma user forum and try to adapt the code to my data. Is there any other
elegant way to deal with this kind of replication ?

once again thanks for the information

with regards,
vinoy


On 12/7/06, Jenny Drnevich <drnevich@uiuc.edu> wrote:
>
> Hi Vinoy,
>
> Using the 'Gquantile' between-array normalization is not appropriate in
> your case because your reference is not always in the Green channel. The
> values you are using for Exp3 and Exp6 in the linear model are actually
> from the reference, so it's no wonder your gene lists don't make sense. To
> clarify, the discussion we were having recently on the mailing list about
> using Gquantile is when your experimental samples are expected to be VERY
> different from the reference, such that the assumption of a within-array
> normalization may not be met. In your case (and in most reference designs)
> you probably meet the assumptions of most genes not changing, and so
> should
> first do a within-array loess-type normalization to help remove dye bias.
> Then check to see if the resulting distributions of M values are similar
> between arrays. If they are very different, and you would expect them not
> to be very different, do a between-array normalization on the M values -
> the scale method of 'normalizeBetweenArrays' is my favorite. The design
> matrix you have below will correctly adjust for dye swaps, assuming that
> the 'dye swaps' are all biological replicates and not technical
> replicates.
>
> I'm a little confused about the way you're calling the 'lmFit' function.
> Your arrays appear to have duplicate spots, but you have the correlation
> as
> zero. Something is very wrong with your arrays if there is zero
> correlation
> between the duplicate spots! I suggested you read the limma vignette very
> closely, especially the sections on common reference designs and
> within-array replicate spots.
>
> Good luck,
> Jenny
>
> At 12:58 AM 12/7/2006, Vinoy Kumar Ramachandran wrote:
> >  Dear Limma users,
> >
> >I am working on custom spotted 70mer oligo arrays, and use Bluefuse to
> >analyse the images. With the help of the excellent user guide and
> >Bioconductor user forum(GMANE), i have analysed my direct comparison
> >experiements. I also have common reference, time course and direct two
> color
> >design type experiments to analyse. I have read the recent posting in the
> >list  about using Rquantile or Gquantile for normalizing between arrays
> in
> >common reference experiments. I tried to do a common references analysis
> >using the discussed code.But the resulting gene list is different from
> the
> >expected list.i am also wondering how to account for dye swaps. I have
> >pasted the code which i used for common reference.
> >
> >It will also be very useful if you any one could tell me how to use
> >normalization between arrays for direct two color designs.
> >
> >My experiment design is
> >           Cy3   Cy5
> >____________________
> >Exp1  Ref    CpdA
> >Exp2  Ref    CpdA
> >Exp3  CpdA Ref
> >
> >Exp4  Ref   CpdB
> >Exp5  Ref   CpdB
> >Exp6 CpdB Ref
> >
> >Code which i used for analysing common referencec:
>
> >-------------------------------------------------------------------------------------------------------------------------
> >library(limma)
> >targets <- readTargets("commonref.txt", row.names="Name")
> >RG <- read.maimages(targets$FileName, source="bluefuse")
> >RG$genes <- readGAL()
> >RG$printer <- getLayout(RG$genes)
> >spottypes <- readSpotTypes()
> >RG$genes$Status <- controlStatus(spottypes, RG)
> >isGene <- RG$genes$Status == "oligos"
> >MA.Gquantile <- normalizeBetweenArrays(RG[isGene,], method="Gquantile")
> >RG.Gquantile <- RG.MA(MA.Gquantile)
> >MA.dummy <- MA.Gquantile
> >MA.dummy$M <- log2(RG.Gquantile$R)
> >o <- order(MA.dummy$genes$ID)
> >MA.sorted <- MA.dummy[o,]
> >design <- modelMatrix(targets, ref="Ref")
> >fit <- lmFit(MA.sorted, design, ndups=2, spacing=1, correlation=0)
> >fit.eb <- eBayes(fit)
> >write.fit(fit.eb, file="data/commonref.xls", adjust="BH")
>
> >---------------------------------------------------------------------------------------------------------------------------------
> >
> >thanks in advacne
> >
> >with regards,
> >Vinoy......
> >
> >         [[alternative HTML version deleted]]
> >
> >_______________________________________________
> >Bioconductor mailing list
> >Bioconductor@stat.math.ethz.ch
> >https://stat.ethz.ch/mailman/listinfo/bioconductor
> >Search the archives:
> >http://news.gmane.org/gmane.science.biology.informatics.conductor
>
> Jenny Drnevich, Ph.D.
>
> Functional Genomics Bioinformatics Specialist
> W.M. Keck Center for Comparative and Functional Genomics
> Roy J. Carver Biotechnology Center
> University of Illinois, Urbana-Champaign
>
> 330 ERML
> 1201 W. Gregory Dr.
> Urbana, IL 61801
> USA
>
> ph: 217-244-7355
> fax: 217-265-5066
> e-mail: drnevich@uiuc.edu
>
>


-- 
Vinoy......

	[[alternative HTML version deleted]]

