NormalizeBetweenArrays
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Jason Skelton ▴ 510
@jason-skelton-135
Last seen 10.3 years ago
Hi Gordon and everyone i'm using (Limma V1.5.1, R V1.8.0) I have an experiment with seven treatments as outlined below Germball Cerc TwoDay SevenDay TwentyOneDay AdultWorm Egg 1 -1 0 0 0 0 0 0 2 -1 0 0 0 0 0 0 3 -1 0 0 0 0 0 0 4 1 0 0 0 0 0 0 5 0 -1 0 0 0 0 0 6 0 -1 0 0 0 0 0 7 0 -1 0 0 0 0 0 8 0 1 0 0 0 0 0 9 0 0 -1 0 0 0 0 10 0 0 -1 0 0 0 0 11 0 0 -1 0 0 0 0 12 0 0 1 0 0 0 0 13 0 0 0 1 0 0 0 14 0 0 0 -1 0 0 0 15 0 0 0 -1 0 0 0 16 0 0 0 -1 0 0 0 17 0 0 0 0 -1 0 0 18 0 0 0 0 -1 0 0 19 0 0 0 0 -1 0 0 20 0 0 0 0 1 0 0 21 0 0 0 0 0 1 0 22 0 0 0 0 0 -1 0 23 0 0 0 0 0 -1 0 24 0 0 0 0 0 -1 0 25 0 0 0 0 0 0 -1 26 0 0 0 0 0 0 -1 I'm currently using normalizeWithinArrays for each of the 26 slides and treating them completely seperately then liner model fitting -> classifyTests etc................ However I'd like to normalize within treatments Is there an easy way of taking the normalized *within* arrays results and *normalizing between arrays* for each treatment seperately then combining these back in to a complete MAlist ? for use in further downstream analysis to compare treatments using ClassifyTests etc ? Many thanks J. -- -------------------------------- Jason Skelton Pathogen Microarrays Wellcome Trust Sanger Institute Hinxton Cambridge CB10 1SA Tel +44(0)1223 834244 Ext 7123 Fax +44(0)1223 494919
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@gordon-smyth
Last seen 17 hours ago
WEHI, Melbourne, Australia
Dear Jason, At 04:35 AM 11/03/2004, Jason Skelton wrote: >Hi Gordon and everyone > >i'm using (Limma V1.5.1, R V1.8.0) > >I have an experiment with seven treatments as outlined below > > Germball Cerc TwoDay SevenDay TwentyOneDay AdultWorm Egg >1 -1 0 0 0 0 0 0 >2 -1 0 0 0 0 0 0 >3 -1 0 0 0 0 0 0 >4 1 0 0 0 0 0 0 >5 0 -1 0 0 0 0 0 >6 0 -1 0 0 0 0 0 >7 0 -1 0 0 0 0 0 >8 0 1 0 0 0 0 0 >9 0 0 -1 0 0 0 0 >10 0 0 -1 0 0 0 0 >11 0 0 -1 0 0 0 0 >12 0 0 1 0 0 0 0 >13 0 0 0 1 0 0 0 >14 0 0 0 -1 0 0 0 >15 0 0 0 -1 0 0 0 >16 0 0 0 -1 0 0 0 >17 0 0 0 0 -1 0 0 >18 0 0 0 0 -1 0 0 >19 0 0 0 0 -1 0 0 >20 0 0 0 0 1 0 0 >21 0 0 0 0 0 1 0 >22 0 0 0 0 0 -1 0 >23 0 0 0 0 0 -1 0 >24 0 0 0 0 0 -1 0 >25 0 0 0 0 0 0 -1 >26 0 0 0 0 0 0 -1 > >I'm currently using normalizeWithinArrays for each of the 26 slides and >treating them completely seperately >then liner model fitting -> classifyTests etc................ > >However I'd like to normalize within treatments > >Is there an easy way of taking the normalized *within* arrays results >and *normalizing between arrays* for each treatment seperately then >combining these back in to a complete MAlist ? This isn't built-in to limma but it is easy to do using the subset and cbind operations defined for MAList objects. Suppose that 'design' is the matrix you give above and 'MA' is your within-array normalized data object. MA1 <- MA[, as.logical(design[,"Germball"])] MA1 <- normalizeBetweenArrays(MA1, method="Aq") MA2 <- MA[, as.logical(design[,"Cerc"])] MA2 <- normalizeBetweenArrays(MA1, method="Aq") etc, then MAnorm <- cbind(MA1,MA2,MA3,MA4,MA5,MA6,MA7) Gordon >for use in further downstream analysis to compare treatments using >ClassifyTests etc ? > >Many thanks > >J. > > > >-- >-------------------------------- >Jason Skelton >Pathogen Microarrays >Wellcome Trust Sanger Institute >Hinxton >Cambridge >CB10 1SA > >Tel +44(0)1223 834244 Ext 7123 >Fax +44(0)1223 494919
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Gordon Smyth wrote: > > This isn't built-in to limma but it is easy to do using the subset and > cbind operations defined for MAList objects. Suppose that 'design' is > the matrix you give above and 'MA' is your within-array normalized > data object. Thankyou very much for this Gordon this is exactly what I wanted...... On a different note since I changed to newer version of limma (1.5.1) I receive the following message when I run dupcor.series or duplicateCorrelation duplicateCorrelation(object,design=rep(1,ncol(M)),ndups=2,spacing=1,tr im=0.15,weights=NULL) dupcor.series(M,design=rep(1,ncol(M)),ndups=2,spacing=1,initial=0.8,tr im=0.15,weights=NULL) > NBAScaleMULTIcor <- dupcor.series(NBAScaleMULTI$M, SchistodesignMATRIX, ndups=2, spacing=544) There were 12 warnings (use warnings() to see them) > warnings() Warning messages: 1: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) 2: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) 3: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) 4: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) 5: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) 6: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) 7: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) 8: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) 9: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) 10: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) 11: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) 12: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) Any ideas ? cheers Jason -- -------------------------------- Jason Skelton Pathogen Microarrays Wellcome Trust Sanger Institute Hinxton Cambridge CB10 1SA Tel +44(0)1223 834244 Ext 7123 Fax +44(0)1223 494919
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At 10:43 PM 11/03/2004, Jason Skelton wrote: >Gordon Smyth wrote: >>This isn't built-in to limma but it is easy to do using the subset and >>cbind operations defined for MAList objects. Suppose that 'design' is the >>matrix you give above and 'MA' is your within-array normalized data object. > >Thankyou very much for this Gordon this is exactly what I wanted...... >On a different note > >since I changed to newer version of limma (1.5.1) >I receive the following message when I run dupcor.series or >duplicateCorrelation > >duplicateCorrelation(object,design=rep(1,ncol(M)),ndups=2,spacing=1,t rim=0.15,weights=NULL) >dupcor.series(M,design=rep(1,ncol(M)),ndups=2,spacing=1,initial=0.8,t rim=0.15,weights=NULL) > > >>NBAScaleMULTIcor <- dupcor.series(NBAScaleMULTI$M, SchistodesignMATRIX, >>ndups=2, spacing=544) > >There were 12 warnings (use warnings() to see them) > > warnings() >Warning messages: >1: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) >2: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) >3: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) >4: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) >5: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) >6: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) >7: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) >8: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) >9: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) >10: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) >11: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) >12: Max iterations exceeded in: glmgam.fit(dx, dy, start = c(mean(dy), 0)) > >Any ideas ? Just ignore this - it isn't a problem. I have replaced the call to gls() from the nlme library with my own program randomizedBlock from the statmod library, because it is quite a bit faster on typical microarray problems. It is the new program which is generating the warnings. Gordon >cheers >Jason
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