Dear Dr.Smyth, I'm struggling with limma to apply it to two-group microarray data. I thought ebayes() in limma shrinks the variance but not the "sign" of the original t-statistic. However, example code resulted in opposite signs of the moderated T statistic and the mean difference in the gene 1(or the row 1). I tried to follow example code in limma. What seems to be problem here? Thank you in advance. Best, Jihoon Kim -------------------------------------------------------------- library(limma) exprVec <- c( 1.5522, 1.6881, 1.0798, 1.3877, 0.8566, 1.4138, 1.5729 , 1.3380, 2.2301, 1.8566, 2.0400 , 1.6191, -1.6955, -1.2777, -1.4938, -1.7326, -1.5530, -1.7169, -0.1812, 0.0399, 0.0413, -0.3797, -0.4582, -0.6490, -0.3439, -0.2067, -0.0792, 0.0549, -0.1200, 0.4584, -0.2357, -0.5873, -0.0446, -0.1868, 0.2710, -0.2091, -0.5146, -0.1384, -0.1525 , 0.0089, 0.5896, 0.2688, 0.2017, 0.1631, -0.1724, -0.5886, -0.1346, -0.3309 ) Mat <- matrix( exprVec, nrow=4, ncol=12, byrow=TRUE) design.eb <- cbind( time1=c( rep(1, 6), rep(0, 6)), time2=c( rep(0, 6), rep(1, 6)) ) fit <- lm.series(Mat, design=design.eb) eb <- ebayes(fit) modT <- eb$t[,1] meanDiff <- rep(0, 4) for(i in 1:3) { meanDiff[i] <- mean( Mat[i, 1:6] ) - mean( Mat[i, 7:12] ) } cbind(modT, meanDiff) plot(1:12, Mat[1,], type="b")

At 05:16 AM 3/08/2005, Jihoon Kim wrote: >Dear Dr.Smyth, > >I'm struggling with limma to apply it to two-group microarray data. >I thought ebayes() in limma shrinks the variance but not the "sign" >of the original t-statistic. It does. > However, example code resulted in opposite >signs of the moderated T statistic and the mean difference >in the gene 1(or the row 1). > >I tried to follow example code in limma. >What seems to be problem here? Your meanDiff() is not equivalent to fitting a linear model. All your linear model does here is to compute group means. Suggest you talk to a statistician in your dept. Gordon >Thank you in advance. > > >Best, > > >Jihoon Kim > > >-------------------------------------------------------------- > > >library(limma) > > >exprVec <- c( > 1.5522, 1.6881, 1.0798, 1.3877, 0.8566, 1.4138, > 1.5729 , 1.3380, 2.2301, 1.8566, 2.0400 , 1.6191, > -1.6955, -1.2777, -1.4938, -1.7326, -1.5530, -1.7169, > -0.1812, 0.0399, 0.0413, -0.3797, -0.4582, -0.6490, > -0.3439, -0.2067, -0.0792, 0.0549, -0.1200, 0.4584, > -0.2357, -0.5873, -0.0446, -0.1868, 0.2710, -0.2091, > -0.5146, -0.1384, -0.1525 , 0.0089, 0.5896, 0.2688, > 0.2017, 0.1631, -0.1724, -0.5886, -0.1346, -0.3309 ) >Mat <- matrix( exprVec, nrow=4, ncol=12, byrow=TRUE) >design.eb <- cbind( time1=c( rep(1, 6), rep(0, 6)), > time2=c( rep(0, 6), rep(1, 6)) ) >fit <- lm.series(Mat, design=design.eb) >eb <- ebayes(fit) >modT <- eb$t[,1] > >meanDiff <- rep(0, 4) >for(i in 1:3) { > meanDiff[i] <- mean( Mat[i, 1:6] ) - mean( Mat[i, 7:12] ) >} > > >cbind(modT, meanDiff) > >plot(1:12, Mat[1,], type="b")