cDNA array with 2 levels of duplication
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@gordon-smyth
Last seen 21 minutes ago
WEHI, Melbourne, Australia
Dear Noah, Only one level of duplication can be handled using the duplicateCorrelation() technique. I suggest that you average over the most highly correlated duplicate level, which is the side-by-side, then use duplicateCorrelation() for the top and bottom half. E.g., MA2 <- avedups(MA,ndups=2,spacing=1) corfit <- duplicateCorrelation(MA2,design,ndups=2,spacing="topbottom") fit <- lmFit(MA2,design,ndups=2,spacing="topbottom",cor=corfit$consensus) etc Best wishes Gordon > Date: Mon, 09 Oct 2006 18:12:53 -0500 > From: "Noah Cohen" <ncohen at="" cvm.tamu.edu=""> > Subject: [BioC] cDNA array with 2 levels of duplication > Content-Type: text/plain > > Hi - I am looking for help with how to account for duplication at 2 > levels in analysis using lmFit in limma of data from a 2-channel cDNA > microarray (for which ImaGene software was used for analysis). The > array is a 4 x 4 block of 24 rows x 24 columns of spots. The array> has > been duplicated on the top half and the bottom half of the slide. > Furthermore, the spots themselves have been duplicated side-by-side. > Thus, each slide has 4 replications of the same EST, occuring in 2 > adjacent pairs that are equally separated. My reading of the limma > vignette and elsewhere on within-array duplicate spots hasn't yielded a > solution. I've tried using the ndups options in lmFit and > duplicateCorrelation. While I have been successful with accounting for > duplicate spots, I haven't been able to find a solution for the > replication of the arrays on the same slide, where each array has > within-array replication. > > Does anyone have any experience or advice? Thanks - Noah Cohen > > > Noah D. Cohen, VMD, MPH, PhD, DACVIM > Professor > Department of Large Animal Clinical Sciences > College of Veterinary Medicine and Biomedical Sciences > Texas A&M University > College Station, Texas 77843-4475 > Telephone: 979-845-3541 > Fax: 979-847-8863 > e-mail: ncohen at cvm.tamu.edu
Microarray limma Microarray limma • 797 views
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