Hi, I've just been looking at some data where a wild relative and model plant have been hybridised to an array designed for the model plant. There are only 2 chips - 2 biological replicates with dye swap. I normalised the data with limma as below - RG.nb <- backgroundCorrect(RG, method="none") MA.nb <- normalizeWithinArrays(RG.nb) MA.nb.s <- normalizeBetweenArrays(MA.nb) rg <- RG.MA(MA.nb.s) plot(rg$R[,1], rg$G[,2]) abline(0,1) The plot (attached, but I guess it will be scrubbed?) has the majority of the genes on the line, but there are a large amount of genes all up regulated (a diffuse 'finger' coming out of the side of the scatter plot). The dye swap shows exactly the same (for the biological rep). Even though these are two independent labellings, and the same amount of labelled cDNA was used, is this likely to be due to different cDNA amounts or quality. Has anyone else had similar experiences, particulary when comparing different species? If this is the case, then why do the majority of genes show a good correlation? Also in more general terms I've been looking at the correlations between replicates (R and G values, not ratios) in other experiments, and whilst in most cases the correlation is good (nice 0,1 scatterplot) in some cases its very poor (think explosion from the bottom left of the plot!). Can anyone offer any experience or suggestions on how to identify whether the Biological sample, RNA, labelling or slide may be at fault. Thanks, Matt P.S - sorry if this doesn't text wrap, our email has been 'upgraded'. -------------- next part -------------- A non-text attachment was scrubbed... Name: RvsG.png Type: image/png Size: 14275 bytes Desc: RvsG.png Url : https://www.stat.math.ethz.ch/pipermail/bioconductor/attachments /20040727/5bb2b4ea/RvsG.png