Hello everyone. =20 I am currently evaluating difference between one-color and two-color hybri= dization to decide which one should be used for upcoming project. For 2-col= or, I found in my self vs self hybs, the gene-specific cy5/cy3 bias (i.e., = reproducible red or green spots on self-self slides) is quite significant. = This will affect the real sample-control experiments especially if the fold= changes of differential _expression is small. A compensate solution is to = divide the ratio of sample-control slides to control-control slides. Althou= gh there is no reference RNA samples, the "Two-Sample Experiments" medel in= limma may fit very well for this purpose. ( I appreciate to hear your opin= iones on this).An alternative is to use single color hyb, one is control an= d another is sample, and this will cut half of the labor and cost. But can = limma handle one-color experiments? If not, is there any good packages for = single color normalization and significance analysis? My guess is that it w= ill have to involve scaling and wonder if it can generate similar results a= s 2-color experiment. =20 Leon [[alternative HTML version deleted]]

At 04:37 PM 20/06/2003, leon ding wrote: >I am currently evaluating difference between one-color and two-color >hybridization to decide which one should be used for upcoming project. For >2-color, I found in my self vs self hybs, the gene-specific cy5/cy3 bias >(i.e., reproducible red or green spots on self-self slides) is quite >significant. This will affect the real sample-control experiments >especially if the fold changes of differential _expression is small. A >compensate solution is to divide the ratio of sample-control slides to >control-control slides. Although there is no reference RNA samples, the >"Two-Sample Experiments" medel in limma may fit very well for this >purpose. ( I appreciate to hear your opiniones on this).An alternative is >to use single color hyb, one is control and another is sample, and this >will cut half of the labor and cost. But can limma handle one-color >experiments? If not, is there any good packages for single color >normalization and significance analysis? My guess is that it will have to >involve scaling and wonder if it can generate similar results as 2-color >experiment. The LIMMA functions will handle one-color experiments just as easily as two-color. One-color experiments are actually the same as Affymetrix data as far as the linear model functions are concerned. Look at Section 10 of the LIMMA User's Guide and then go back to Section 6. Here is an example: Suppose that 'E' is a matrix containing your background-corrected intensities with rows corresponding to genes and columns to arrays. Suppose that the first two arrays are controls and the next three are sample arrays. I would analyse them like this: E <- pmax(E,1) # need to avoid negative intensities if you are to use quantile normalization E <- log(E,2) # convert to log-2 scale E <- normalizeQuantiles(E) # quantile normalization of single- channel intensities design <- cbind(Control=c(1,1,1,1,1),SamplevsControl=c(0,0,1,1,1)) fit <- lm.series(E,design) eb <- ebayes(fit) The second column of eb$t contains moderated t-statistics for differential expression between the sample and control samples. toptable(coef=2,fit=fit,eb=eb) will display the top genes most likely to be differentially expressed. I am very surprised that you find single channel analysis more accurate than two-color, but that is another matter. Gordon >Leon

At 04:37 PM 20/06/2003, leon ding wrote: >I am currently evaluating difference between one-color and two-color >hybridization to decide which one should be used for upcoming project. For >2-color, I found in my self vs self hybs, the gene-specific cy5/cy3 bias >(i.e., reproducible red or green spots on self-self slides) is quite >significant. This will affect the real sample-control experiments >especially if the fold changes of differential _expression is small. A >compensate solution is to divide the ratio of sample-control slides to >control-control slides. Although there is no reference RNA samples, the >"Two-Sample Experiments" medel in limma may fit very well for this >purpose. ( I appreciate to hear your opiniones on this).An alternative is >to use single color hyb, one is control and another is sample, and this >will cut half of the labor and cost. But can limma handle one-color >experiments? If not, is there any good packages for single color >normalization and significance analysis? My guess is that it will have to >involve scaling and wonder if it can generate similar results as 2-color >experiment. The LIMMA functions will handle one-color experiments just as easily as two-color. One-color experiments are actually the same as Affymetrix data as far as the linear model functions are concerned. Look at Section 10 of the LIMMA User's Guide and then go back to Section 6. Here is an example: Suppose that 'E' is a matrix containing your background-corrected intensities with rows corresponding to genes and columns to arrays. Suppose that the first two arrays are controls and the next three are sample arrays. I would analyse them like this: E <- pmax(E,1) # need to avoid negative intensities if you are to use quantile normalization E <- log(E,2) # convert to log-2 scale E <- normalizeQuantiles(E) # quantile normalization of single- channel intensities design <- cbind(Control=c(1,1,1,1,1),SamplevsControl=c(0,0,1,1,1)) fit <- lm.series(E,design) eb <- ebayes(fit) The second column of eb$t contains moderated t-statistics for differential expression between the sample and control samples. toptable(coef=2,fit=fit,eb=eb) will display the top genes most likely to be differentially expressed. I am very surprised that you find single channel analysis more accurate than two-color, but that is another matter. Gordon >Leon