
Dear Naomi, 
Thank you very much for your answer. I just have few follow up question. 
How big should be the correlation on my duplicate spots in order to "safetly" average them?Before the normalization, the correlation on my duplicate spots is around 0.7-0.8, but after normalizationit is only around 0.4-0.6. Which I think it is not the best. Probably I should mention that the correlation of dye swapped arrays is around -0.2.
Also, for some of the experiments, we had to remove certain arrays, and therefore not all of my biological replicates are dye swapped. In that case I think I should use the contrast matrix to average of the treated vs non-treated comparisons. Isn't then better to use the corfit$consensus on my duplicate spots?
Thank you very much in advance, 
All the best, Ana




> Date: Fri, 12 Mar 2010 12:28:06 -0500
> To: staninska@hotmail.com; bioconductor@stat.math.ethz.ch
> From: naomi@stat.psu.edu
> Subject: Re: [BioC] (no subject)
> 
> The estimated error variance used for the test denominator will be an 
> average of technical and biological replication, and therefore not 
> really appropriate for your analysis.  However, you could average the 
> 2 technical replicates prior to running limma which would give you 
> the right error structure.
> 
> --Naomi
> 
> At 12:04 PM 3/12/2010, Ana Staninska wrote:
> 
> >Dear Bioconductor,
> >I have a simple experiment that I have to analyze in order to find 
> >differentially expressed genes. I have 10 biological replicates, and 
> >each biological replicate has two technical replicates which appear 
> >as dye swapped. So in total I have 20 arrays. Each of the probes are 
> >spotted twice on the array (on the left and on the right hand side). 
> >I use limma to do my analysis. I know at the moment it is not 
> >possible to treat duplicate spots, technical replicates and 
> >biological replicates, but I though if I use the 
> >duplicateCorrelation function on my duplicate spots, and then to use 
> >a contrast matrix to average of all of the Treated vs Non-treated 
> >biological samples, I could address all 3 replications. Am I correct?
> >
> >
> >I am sending a copy of my code, if someone could look at it at tell 
> >me whether I made somewhere a mistake.
> >Thank you very much in advance,
> >Sincerely Ana Staninska
> >
> >
> >  library(limma)> library(statmod)> library(marray)> 
> > library(convert)> library(hexbin)> library(gridBase)> 
> > library(RColorBrewer)> > targets <- 
> > readTargets("Lysi_270705.txt")> > ### Only manually removed ot 
> > absent spots are given 0 weight ###> RGa <- read.maimages(targets, 
> > source="genepix", wt.fun=wtflags(weight=0, 
> > cutoff=-75),  other.columns=c("F635 SD","B635 SD","F532 SD","B532 
> > SD","B532 Mean","B635 Mean","F Pixels","B Pixels"))Read 
> > LYSI270705_1_200905.gpr Read LYSI270705_1dw_200905.gpr Read 
> > LYSI270705_2_200905.gpr Read LYSI270705_2dw_200905.gpr Read 
> > LYSI270705_3_121005.gpr Read LYSI270705_3dw_121005.gpr Read 
> > LYSI270705_4_121005.gpr Read LYSI270705_4dw_121005.gpr Read 
> > LYSI270705_5_121005.gpr Read LYSI270705_5dw__121005.gpr Read 
> > LYSI270705_6_121005.gpr Read LYSI270705_6dw__121005.gpr Read 
> > LYSI270705_7_151001.gpr Read LYSI270705_7dw_151005.gpr Read 
> > LYSI270705_8_151005.gpr Read LYSI270705_8dw_151005.gpr Read 
> > LYSI270705_9_151005.gpr Read LYSI270705_9dw_151005.gpr Read LYSI270705!
> >  _10_151005.gpr Read LYSI270705_10dw_151005.gpr > for(i in 
> > 1:nrow(RGa)){+       for(j in 
> > 1:ncol(RGa)){+         if(RGa$Rb[i,j]+RGa$R[i,j]+ RGa$G[i,j]+ 
> > RGa$Gb[i,j] ==0)+     RGa$weights[i,j]<-0+ }+ }> > 
> > ####################################################> ### 
> > Background Correction = Normexp + offset 25 ####> 
> > ####################################################> > RG 
> > <-backgroundCorrect(RGa, method="normexp", , normexp.method="mle", 
> > offset=25)Green channelCorrected array 1 Corrected array 2 
> > Corrected array 3 Corrected array 4 Corrected array 5 Corrected 
> > array 6 Corrected array 7 Corrected array 8 Corrected array 9 
> > Corrected array 10 Corrected array 11 Corrected array 12 Corrected 
> > array 13 Corrected array 14 Corrected array 15 Corrected array 16 
> > Corrected array 17 Corrected array 18 Corrected array 19 Corrected 
> > array 20 Red channelCorrected array 1 Corrected array 2 Corrected 
> > array 3 Corrected array 4 Corrected array 5 Corrected array 6 
> > Corrected array 7 Corrected array 8 Corrected array !
> >  9 Corrected array 10 Corrected array 11 Corrected array 12 Corrected a
> >rray 13 Corrected array 14 Corrected array 15 Corrected array 16 
> >Corrected array 17 Corrected array 18 Corrected array 19 Corrected 
> >array 20 > ####################################################> 
> >##### normalize Within arrays              #########> 
> >####################################################> > MA 
> ><-normalizeWithinArrays(RG, method="loess")> > 
> >####################################################> ###### 
> >Contrast Matrix                 ############> 
> >####################################################> > 
> >design<-cbind( +  MU1vsWT1=c( 
> >1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),+      MU2vsWT2=c(0,0, 
> >1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),+      MU3vsWT3=c(0,0,0,0, 
> >1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0),+      MU4vsWT4=c(0,0,0,0,0,0, 
> >1,-1,0,0,0,0,0,0,0,0,0,0,0,0),+      MU5vsWT5=c(0,0,0,0,0,0,0,0, 
> >1,-1,0,0,0,0,0,0,0,0,0,0),+      MU6vsWT6=c(0,0,0,0,0,0,0,0,0,0, 
> >1,-1,0,0,0,0,0,0,0,0), 
> >+      MU7vsWT7=c(0,0,0,0,0,0,0,0,0,0!
> >  ,0,0, 
> > 1,-1,0,0,0,0,0,0),+      MU8vsWT8=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0, 
> > 1,-1,0,0,0,0),+      MU9vsWT9=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 
> > 1,-1,0,0),+      MU10vsWT10=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 
> > 1,-1))> > cont.matrix <- 
> > 
> > ####################################################> 
> > ###  Duplicate Correlations on duplicate spots   ####> 
> > ####################################################> > 
> > corfit<-duplicateCorrelation(MA, ndups=2, spacing=192)> > 
> > ####################################################> ##### Linear 
> > Fit Model and Contrasts fit     #######> 
> > ####################################################> > 
> > fit<-lmFit(MA,  design, ndups=2, spacing=192, 
> > cor=corfit$consensus)> > fit<-contrasts.fit(fit, cont.matrix)> > 
> > ####################################################> 
> > #########  eBayes Statistics         ###############> #################!
> >  ###################################> > fit<-eBayes(fit)> > ###########
> >###################################################> ###  Writing 
> >the Results                               ######> 
> >##############################################################> 
> >TTnew<-topTable(fit,coef=1,  number=100, adjust="BH")
> >
> >
> >Ana StaninskaHelmholtz-Zentrum MuenchenDepartment of Scientific 
> >ComputingNeuherberg, Deutschland+49 (0) 89 3187 2656
> >
> >         [[alternative HTML version deleted]]
> >
> >_______________________________________________
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> 
> Naomi S. Altman                                814-865-3791 (voice)
> Associate Professor
> Dept. of Statistics                              814-863-7114 (fax)
> Penn State University                         814-865-1348 (Statistics)
> University Park, PA 16802-2111
> 
 		 	   		  
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