limma Normalization question
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@cecilia-mcgregor-1508
Last seen 7.1 years ago
Hi Everyone I've described my experiment in an earlier message, on Nov 16. After running the commands (see end of message for commands) I looked at the plotMA(fit2)plot. I attached the plot. It seems to me that at high A-values, the plot is going more in the direction of positive M-values. I tested a few genes in the circled area (for high A-values)with Q-RT-PCR and it confirms that these genes should have M-values around 0. The fact that more genes are down-regulated, than up-regulated is expected from our knowledge of the experiment. In the limma Users Guide (6 Oct 2005) p21, it says that loess does not assume equal number of genes up- and down-regulated, so this should not be a problem. I have about 2500 unique genes on the array.However it does say that most genes need to be not differentially expressed. I estimate about 60% of genes are not differentially expressed. In a message on this board (Sept 3, 2003), Gordon Smyth suggests the use of the following command if a large number of genes are differentially expressed. normalizeWithinArrays(RG, method="robustspline", robust="MM") However I get 18 of the following warning messages: Warning messages: 1: rlm failed to converge in 20 steps in: rlm.default(x, y, weights = w, method = method) I believe Paul Boutros suggested a fix for this in a message on this board(Aug 11 2004), but since I don't know much about the subject, I'm not very eager to change the limma code (I think that is what he suggest). How can I fix this problem I'm having at high A-Values? I get the correct results at low A-values. I have analised this data also with limmaGUI (I just treated all replicates as biological)and I get the similar results. I used several different within and between normalization methods, but cannot get better results. I even tried normalizing with Genespring, altough I know it is not apropriate for loop designs. With Genesspring I can get an acceptable MA plot if I use "Per array normalizations to the 20th percentile". However I'm not very comfortable with this, since inspecting the MA plots before normalization, I can see that I have an intensity dependent bias on all my arrays. Any suggestions would be much appreciated. Code I Used: (I get the same results with the code suggested by Steen Krogsgaard on Nov 17) library(limma) setwd("C:\\Program Files\\R_JSM\\rw2011\\RFlimma") targets <- readTargets() show(targets) RG <- read.maimages(targets$FileName, columns=list(R="Rf",G="Gf",Rb="Rb",Gb="Gb")) RG$genes <- readGAL() spottypes <-readSpotTypes() RG$genes$Status <-controlStatus(spottypes, RG) MA <- normalizeWithinArrays(RG, method="loess") MA <- normalizeBetweenArrays(MA, method="Aquantile") design <-modelMatrix(targets,ref="S1") cor <- duplicateCorrelation(MA,design,ndups=3,spacing=3120) cor\$consensus.correlation fit <-lmFit(MA,design,ndups=3,spacing=3120,correlation=0.3128828) cont.matrix <- makeContrasts(fvss=(F1+F2+F3-S2-S3)/3, levels=design) fit2 <- contrasts.fit(fit, cont.matrix) fit2 <- eBayes(fit2) plotMA(fit2) Thanks Cecilia McGregor PhD Student Sweetpotato Breeding and Genetics Lab JC Miller Hall room 236 Louisiana State University Baton Rouge LA, 70803 USA Phone: (225) 578 2173