Hi I'm looking at the results of an analysis on 4 S. Cerevisiae cDNA arrays, (2 of these have a dye-swap). I followed the example in limma user's guide (that if I'm not wrong is exactly my case). I obtain a topTable of genes of this type Block Row Column ID Name M A t P.Value B 4030 30 11 11 YLR162W :::::1: 1.624122241 6.560234285 4.467743568 0.997257765 -2.0502737 1254 10 4 11 YDR166C SEC5:::::1: -0.56584704 5.143049435 -3.824556839 0.997257765 -2.61592631 1113 9 4 1 YDR409W :::::1: 1.016356426 4.282139218 3.988554403 0.997257765 -2.683464218 2115 16 10 5 YKL209C STE6:::::1: -0.540528098 7.340027942 -3.533312325 0.997257765 -2.686586054 5022 37 16 11 YPR070W :::::1: -0.493446684 6.765075537 -3.415141086 0.997257765 -2.77744221 3642 27 14 3 YOL045W :::::1: -0.678996258 5.185557216 -3.448815561 0.997257765 -2.865258763 1602 12 14 3 YNL253W :::::1: -0.542907069 6.019893924 -3.427152231 0.997257765 -2.880360788 4177 31 13 1 YNL221C POP1:::::1: -0.49777162 6.787712846 -3.274291885 0.997257765 -2.8886384 Where all the p-values are 0.997257765. I read in the topTable help that "if there is no good evidence for differential expression in the experiment, that it is quite possible for all the adjusted p-values to be large, even for all of them to be equal to one." I'm quite astonished ... and now ? This fact implies that is not a good experiment ? Or that data were not well preprocessed ? Or maybe that for that experiment there are no genes significantly differently expressed ? I'm analyzing that data from a Biomolecular lab, and I don't know what to do and how to explain this... I'll be very happy for any help or suggestion ... !!!!! Thanks in advance, Giulio... This

Giulio Di Giovanni wrote: > Hi I'm looking at the results of an analysis on 4 S. Cerevisiae cDNA arrays, > (2 of these have a dye-swap). > I followed the example in limma user's guide (that if I'm not wrong is > exactly my case). > > I obtain a topTable of genes of this type > > Block Row Column ID Name M A t P.Value B > 4030 30 11 11 YLR162W :::::1: 1.624122241 6.560234285 4.467743568 0.997257765 -2.0502737 > 1254 10 4 11 YDR166C SEC5:::::1: -0.56584704 5.143049435 -3.824556839 0.997257765 -2.61592631 > 1113 9 4 1 YDR409W :::::1: 1.016356426 4.282139218 3.988554403 0.997257765 -2.683464218 > 2115 16 10 5 YKL209C STE6:::::1: -0.540528098 7.340027942 -3.533312325 0.997257765 -2.686586054 > 5022 37 16 11 YPR070W :::::1: -0.493446684 6.765075537 -3.415141086 0.997257765 -2.77744221 > 3642 27 14 3 YOL045W :::::1: -0.678996258 5.185557216 -3.448815561 0.997257765 -2.865258763 > 1602 12 14 3 YNL253W :::::1: -0.542907069 6.019893924 -3.427152231 0.997257765 -2.880360788 > 4177 31 13 1 YNL221C POP1:::::1: -0.49777162 6.787712846 -3.274291885 0.997257765 -2.8886384 > > > Where all the p-values are 0.997257765. I read in the topTable help that > "if there is no good evidence for differential > expression in the experiment, that it is quite possible for all > the adjusted p-values to be large, even for all of them to be > equal to one." > > I'm quite astonished ... and now ? > This fact implies that is not a good experiment ? Or that data were not well > preprocessed ? Or maybe that for that experiment there are no genes > significantly differently expressed ? It means pretty much what the help file says, that there is no good evidence for differential expression for any of your genes. Why that might be is a different question with many possibilities. These include 1.) Very small differences - looking at your topTable, there are only two genes with > 2-fold difference. Maybe there really aren't any differences between the samples. 2.) Noisy data - if the variability between the chips is high, then you will need larger differences in order to gain statistical significance. Alternatively, you may just need more data, which will increase your power to detect differences. 3.) Incorrect normalization - if the normalization was not done correctly, you may not be accounting for some non-biological variability, which will result in increased variability. 4.) Coding mistakes - maybe you aren't doing the comparisons you think you are doing. I don't think you will be able to get much help here, because what you really need is for an experienced person to look at your data and code. This is not amenable to a listserv, so your best bet is to find a local statistician who might be able to help you out. Best, Jim > > I'm analyzing that data from a Biomolecular lab, and I don't know what to do > and how to explain this... > > I'll be very happy for any help or suggestion ... !!!!! > > Thanks in advance, > > Giulio... > This > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues.

On 3/23/06 11:14 AM, "Giulio Di Giovanni" <perimessaggini at="" hotmail.com=""> wrote: > > Hi I'm looking at the results of an analysis on 4 S. Cerevisiae cDNA arrays, > (2 of these have a dye-swap). > I followed the example in limma user's guide (that if I'm not wrong is > exactly my case). > > I obtain a topTable of genes of this type > > Block Row Column ID Name M A t P.Value B > 4030 30 11 11 YLR162W :::::1: 1.624122241 6.560234285 4.467743568 0.997257765 > -2.0502737 > 1254 10 4 11 YDR166C SEC5:::::1: -0.56584704 5.143049435 -3.824556839 0.997257 > 765 -2.61592631 > 1113 9 4 1 YDR409W :::::1: 1.016356426 4.282139218 3.988554403 0.997257765 -2. > 683464218 > 2115 16 10 5 YKL209C STE6:::::1: -0.540528098 7.340027942 -3.533312325 0.99725 > 7765 -2.686586054 > 5022 37 16 11 YPR070W :::::1: -0.493446684 6.765075537 -3.415141086 0.99725776 > 5 -2.77744221 > 3642 27 14 3 YOL045W :::::1: -0.678996258 5.185557216 -3.448815561 0.997257765 > -2.865258763 > 1602 12 14 3 YNL253W :::::1: -0.542907069 6.019893924 -3.427152231 0.997257765 > -2.880360788 > 4177 31 13 1 YNL221C POP1:::::1: -0.49777162 6.787712846 -3.274291885 0.997257 > 765 -2.8886384 > > > Where all the p-values are 0.997257765. I read in the topTable help that > "if there is no good evidence for differential > expression in the experiment, that it is quite possible for all > the adjusted p-values to be large, even for all of them to be > equal to one." > > I'm quite astonished ... and now ? > This fact implies that is not a good experiment ? Or that data were not well > preprocessed ? Giulio, These two questions can't be answered by p-values; they should be answered by other means. There are several packages for looking at array quality and for preprocessing. > Or maybe that for that experiment there are no genes > significantly differently expressed ? That is a distinct possibility. If there are not data quality issues and your sample size is large enough, then perhaps there are not detectible differences (although this doesn't mean that there ARE NOT differences, just that you couldn't see them). Sean

Hi Giulio, in my limited and humble understanding it means, that there are no genes which are called significantly changed after adjusting the p-values for multiple testing using Benjamini-Hochberg fdr control. This can have several reasons one of them being bad reproducibility. Did you also do some quality control as detailed in the user's guide? (looking at the background, considering M vs A plots etc. ) With this you might be able to trace down a "bad" array. But explaining is always difficult. Did your partners do some platform validation to see how good the platform is, how good it is in their hands etc. ? Maybe you can even make out some dye effect. You can maybe compare your results with the swirl example for which you can download the data and then compare step by step where you might have problems. For diagnostics only you might also want to pass the parameter adjust.method="none" to toptable. This switches off correcting for multiple testing, and if even then there are not a lot of genes which have low p-values, something is probably very bad. Cheers, Bj?rn >Hi I'm looking at the results of an analysis on 4 S. Cerevisiae cDNA arrays, >(2 of these have a dye-swap). >I followed the example in limma user's guide (that if I'm not wrong is >exactly my case). > >I obtain a topTable of genes of this type > > Block Row Column ID Name M A t P.Value B >4030 30 11 11 YLR162W :::::1: 1.624122241 6.560234285 4.467743568 0.997257765 -2.0502737 >1254 10 4 11 YDR166C SEC5:::::1: -0.56584704 5.143049435 -3.824556839 0.997257765 -2.61592631 >1113 9 4 1 YDR409W :::::1: 1.016356426 4.282139218 3.988554403 0.997257765 -2.683464218 >2115 16 10 5 YKL209C STE6:::::1: -0.540528098 7.340027942 -3.533312325 0.997257765 -2.686586054 >5022 37 16 11 YPR070W :::::1: -0.493446684 6.765075537 -3.415141086 0.997257765 -2.77744221 >3642 27 14 3 YOL045W :::::1: -0.678996258 5.185557216 -3.448815561 0.997257765 -2.865258763 >1602 12 14 3 YNL253W :::::1: -0.542907069 6.019893924 -3.427152231 0.997257765 -2.880360788 >4177 31 13 1 YNL221C POP1:::::1: -0.49777162 6.787712846 -3.274291885 0.997257765 -2.8886384 > > >Where all the p-values are 0.997257765. I read in the topTable help that >"if there is no good evidence for differential > expression in the experiment, that it is quite possible for all > the adjusted p-values to be large, even for all of them to be > equal to one." > >I'm quite astonished ... and now ? >This fact implies that is not a good experiment ? Or that data were not well >preprocessed ? Or maybe that for that experiment there are no genes >significantly differently expressed ? > >I'm analyzing that data from a Biomolecular lab, and I don't know what to do >and how to explain this... > >I'll be very happy for any help or suggestion ... !!!!! > >Thanks in advance, > >Giulio... >This > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > >