One-Color Agilent miRNA microarrays
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@gordon-smyth
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WEHI, Melbourne, Australia
> Date: Mon, 30 Jul 2007 12:39:19 +0200 > From: "Francesco Favero" <favero.francesco at="" gmail.com=""> > Subject: [BioC] One-Color Agilent miRNA microarrays > To: bioconductor at stat.math.ethz.ch > Message-ID: > <794f7b0e0707300339k5028abcscdc9ea2eca18d334 at mail.gmail.com> > Content-Type: text/plain > > Dear all, > > I'm working with new microRNA one-color microarray from Agilent. > I decided to use limma, and in this particular case, I needed to perform a > time-course experiment. > > Thanks to this list I've done quite everything, but I'm not sure it's > perfectly right... > > I've imported the chips thanks to Peter White and Dr. Gordon Smyth: > > https://stat.ethz.ch/pipermail/bioconductor/2007-May/017203.html >> > > So I have a G data with green and dummy red intensity. > > The normalisation: > > Gbg<-backgroundCorrect(G, method="subtract") >> > > This array have 20 spots for each microRNA but they don't have an order in > the chip, so I putted them in order for GeneName, so I'll able to use the > ndups argument: > > spottypes<-readSpotTypes() >> G$genes$Status<-controlStatus(spottypes,G) >> Gbg.subset <- Gbg[Gbg$genes$Status == "Gene",] >> Gbg.sort <- Gbg.subset[order(Gbg.subset$genes[,"GeneName"]),] >> > > Again normalisation between array just the green channel: > > MA <- normalizeBetweenArrays(Gbg.sort$G, method="vsn") >> > > And performed a normal time course experiment as from the limma manual: > > lev <- c("15", "37", "97", "167", "618") >> f <- factor(targets$Cy3, levels=lev) >> design <- model.matrix(~0+f) >> colnames(design) <- levdupcor <- duplicateCorrelation(MA,design,ndups=20, >> spacing=1) >> > > now I have to fit everything in the linear model... but lmFit doesn't work. > It complains an error on chol(V). > > fit <- lmFit(MA,design,ndups=20,spacing=1,correlation=dupcor$consensus) >> Errore in chol(V) : il minore principale di ordine 2 non ? definito >> positivo > > (Sorry for the error in Italian...anyway...) > It works if I don't use ndups, but I need this... What sort of object is MA? Is it just a matrix, or is it a MAList? What version of limma? In any data analysis, you should check you results at every step. For example, have you looked at your normalized data? What is the value of corfit$consensus? Best wishes Gordon
miRNA Microarray limma microRNA miRNA Microarray limma microRNA • 1.1k views
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@francesco-favero-1796
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On Fri, August 3, 2007 2:40 am, Francesco Favero wrote: > 2007/8/2, Gordon K Smyth <smyth at="" wehi.edu.au="">: >> >> > Date: Mon, 30 Jul 2007 12:39:19 +0200 >> > From: "Francesco Favero" <favero.francesco at="" gmail.com=""> >> > Subject: [BioC] One-Color Agilent miRNA microarrays >> >> ... >> > fit <- lmFit(MA,design,ndups=20,spacing=1,correlation=dupcor$consensus) >> >> Errore in chol(V) : il minore principale di ordine 2 non ?? definito >> >> positivo >> > >> > (Sorry for the error in Italian...anyway...) >> > It works if I don't use ndups, but I need this... >> >> What sort of object is MA? Is it just a matrix, or is it a MAList? > > It's just a list of log2 intensities even if is the output of > normalizeBetweenArrays function. I suspect you mean that it is a 'matrix'. It cannot be of class 'list'. Try class(MA) > In any data analysis, you should check you results at every step. For >> example, have you looked at >> your normalized data? What is the value of corfit$consensus? > > Yes, I have a corfit$consensus of 1. This means your data is degenerate in some way, so you cannot use duplicateCorrelation() without reworking your data. > I've seen it's for this I can't use > lmFit. > I use ndups=20 so I expect to have some differences between different spot > for the same genes.. in fact atanh.correlations is good for some gene, but > for a lot is Inf or NA, maybe this is because only few spots have a good > Intensity, in average the intensity is very low. this is a problem for miRNA > in cell-lines. Anyway I think it' possible I still have to work on > RG$weigths. > > Is it so wrong go around all those problems using lm.series function? Using lm.series is the same as using correlation=0 to lmFit(). Since your estimated correlation is large, it would probably be better to average over your duplicates. This can be done by using avedups() before using lmFit(). Best wishes Gordon
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On Wed, August 8, 2007 11:10 pm, Francesco Favero wrote: > Should the problem be the design I'm using? > >> design > Cen15 Cen37 Cen97 Cen167 Cen618 > 1 1 0 0 0 0 > 2 0 1 0 0 0 > 3 0 0 1 0 0 > 4 0 0 0 1 0 > 5 0 0 0 0 1 > attr(,"assign") > [1] 1 1 1 1 1 > attr(,"contrasts") > attr(,"contrasts")$f > [1] "contr.treatment" > > Thanks a lot > > Francesco Yes, the problem certainly is the design matrix you're using. Your design matrix implies that you have 5 different treatments applied to 5 arrays. In other words, you have no replication, so that no statistical analysis is possible. Please have a look at the limma User's Guide on different types of designs, and think about what type of experiment you really have. Best wishes Gordon
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