Hi, I read a single channel intensities data set in *read.matrix* function and proceed a normalization with normalizeQuantiles. > y <- normalizeQuantiles(x) In topTable, I get up and down regulated genes. topTable showed a M, t, P.Value and B statistics. But, I get the M value around 400. In my data set there aren't these values. When I read the same data with read.exprSet function and proceed a normalization with normalizeQuantiles, and proceed a topTable execution, I get M, A, t, P.Value and B statistics. The M values are near 400, too. In another test with the same intensities data, I, in excel, transform my intensities datas in log2, set missing values to NA, read it with read.exprSet function and proceed a normalization with normalizeQuantiles. In this analysis, topTable showed M, A, t, P.Value and B statistics. M values is around 3 and P.Value min is 0.0007, but no down regulated genes is showed. These results is similar with normalization with vsn (with out transformation). After these results I and my friend are very confused and we don't know what we to do! For example, why in the first test, when we use matrix, topTable don't return the statistic A and in the next test it returns these values? I know that I am wrong, but I am very curious for to know what are my mistakes. My excuses, in advanced, if this doubt is out of the mail list. Any commentary is very appreciated. Thanks Marcelo -- No virus found in this outgoing message. Checked by AVG Anti-Virus.

Hi, the reason topTable in the limma package didnt provide the statistic A is because you fed in a matrix of *M* values, not an MAList object. Without the A matrix encapsulated within the MAList object, *unweighted* average A values cannot be calculated. e.g. if (length(object@maA)) fit$Amean <- rowMeans(unwrapdups(object@maA, ndups = ndups, spacing = spacing), na.rm = TRUE) Try looking at your channel densities with plotDensities in the limma package. Do the densities look highly right skewed? Usually limma analysis is on log2 transformed data. Marcus >>> Marcelo Luiz de Laia <mlaia@fcav.unesp.br> 17/02/2005 2:08:15 PM >>> Hi, I read a single channel intensities data set in *read.matrix* function and proceed a normalization with normalizeQuantiles. > y <- normalizeQuantiles(x) In topTable, I get up and down regulated genes. topTable showed a M, t, P.Value and B statistics. But, I get the M value around 400. In my data set there aren't these values. When I read the same data with read.exprSet function and proceed a normalization with normalizeQuantiles, and proceed a topTable execution, I get M, A, t, P.Value and B statistics. The M values are near 400, too. In another test with the same intensities data, I, in excel, transform my intensities datas in log2, set missing values to NA, read it with read.exprSet function and proceed a normalization with normalizeQuantiles. In this analysis, topTable showed M, A, t, P.Value and B statistics. M values is around 3 and P.Value min is 0.0007, but no down regulated genes is showed. These results is similar with normalization with vsn (with out transformation). After these results I and my friend are very confused and we don't know what we to do! For example, why in the first test, when we use matrix, topTable don't return the statistic A and in the next test it returns these values? I know that I am wrong, but I am very curious for to know what are my mistakes. My excuses, in advanced, if this doubt is out of the mail list. Any commentary is very appreciated. Thanks Marcelo -- No virus found in this outgoing message. Checked by AVG Anti-Virus. _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor ______________________________________________________ The contents of this e-mail are privileged and/or confidenti...{{dropped}}

I almost had conviction of that topTable must not have the value "A" because I am analyzing single channel intensities. But, I didn't was security. But, when I read my intensities in a exprSet object and normalize it with vsn and use limma to check the DE genes, topTable returns the value "A". This is not a problem for me. I would like to have security that the showed DE genes in topTable are correct, in this case. Thanks Marcelo Marcus Davy escreveu: >Hi, >the reason topTable in the limma package didnt provide the statistic A is because you fed in a matrix of *M* values, not an >MAList object. Without the A matrix encapsulated within the MAList object, *unweighted* average A values cannot be calculated. > >e.g. > if (length(object@maA)) > fit$Amean <- rowMeans(unwrapdups(object@maA, ndups = ndups, > spacing = spacing), na.rm = TRUE) > >Try looking at your channel densities with plotDensities in the limma package. Do the densities look highly right skewed? >Usually limma analysis is on log2 transformed data. > > >Marcus > > > > >>>>Marcelo Luiz de Laia <mlaia@fcav.unesp.br> 17/02/2005 2:08:15 PM >>> >>>> >>>> >Hi, > >I read a single channel intensities data set in *read.matrix* function >and proceed a normalization with normalizeQuantiles. > > > y <- normalizeQuantiles(x) > >In topTable, I get up and down regulated genes. > >topTable showed a M, t, P.Value and B statistics. But, I get the M value >around 400. In my data set there aren't these values. > >When I read the same data with read.exprSet function and proceed a >normalization with normalizeQuantiles, and proceed a topTable execution, >I get M, A, t, P.Value and B statistics. The M values are near 400, too. > >In another test with the same intensities data, I, in excel, transform >my intensities datas in log2, set missing values to NA, read it with >read.exprSet function and proceed a normalization with >normalizeQuantiles. In this analysis, topTable showed M, A, t, P.Value >and B statistics. M values is around 3 and P.Value min is 0.0007, but no >down regulated genes is showed. These results is similar with >normalization with vsn (with out transformation). > >After these results I and my friend are very confused and we don't know >what we to do! For example, why in the first test, when we use matrix, >topTable don't return the statistic A and in the next test it returns >these values? I know that I am wrong, but I am very curious for to know >what are my mistakes. > >My excuses, in advanced, if this doubt is out of the mail list. > >Any commentary is very appreciated. > >Thanks > >Marcelo > > > > -- No virus found in this outgoing message. Checked by AVG Anti-Virus.