I am not sure B>0 means much, since the B statistic depends on a prior knowledge of the % of expected DE genes. At least I see very widely different B values for my top genes in different types of experiments. I prefer to cut by p value (adjusted p value, by Bonferroni-Hochberg -the default- gives you the FDR). I think that's a better starting point. If you have a lot of DE genes that way, do you have a reason to believe that they are too many? If you have too many genes, and you use an FDR of 0.05... you have an instant *estimate* of expected false positives in that list (5%). So when an experiment gives a lot of genes as DE, maybe you can be more strict and reduce your cutoff to 0.01, or even lower... For me, in the end, it depends on how many mistakes can I live with. If I pick up 2000 genes, but I expect 100 to be wrong... depending on the actual experiment I might go for a stricter cutoff where I may only pick 100, but expecting only 1 to be wrong. In other cases a higher rate of error is not a problem. In the end, as Richard said, validation by PCR is necessary, not of every gene, but of a reasonable number across the range. Jose Quoting Richard Friedman <friedman at="" cancercenter.columbia.edu="">: > Dear Mouselly, > > A B>f 0 is a good place to start. > It is advisable to spot check genes by PCR to validate the cutoff. > > Best wishes, > Rich > > On Jul 18, 2007, at 10:06 AM, el mousselly antra wrote: > >> hi, >> i have a question on limma package >> when we have the result of tnis pakage and we have p-vlue and the >> value of B . >> i i want to know on wich value of p we consider that genes are DE , >> we take 0.05 is the threshold or what and i see that when we consider >> 0.05 as threshold wa have a lot of genes DE. >> so, i want some rule (threshold)that will be raisonnable and >> efficient to detect gnes DE >> thank you >> sincerly >> >> >> >> >> --------------------------------- >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> 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 > > ------------------------------------------------------------ > Richard A. Friedman, PhD > Biomedical Informatics Shared Resource > Lecturer > Department of Biomedical Informatics > Educational Coordinator > Center for Computational Biology and Bioinformatics > National Center for Multiscale Analysis of Genomic Networks > Box 95, Room 130BB or P&S 1-420C > Columbia University Medical Center > 630 W. 168th St. > New York, NY 10032 > (212)305-6901 (5-6901) (voice) > friedman at cancercenter.columbia.edu > http://cancercenter.columbia.edu/~friedman/ > > In memoriam, John Stewart Williamson > > _______________________________________________ > 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 > > -- Dr. Jose I. de las Heras Email: J.delasHeras at ed.ac.uk The Wellcome Trust Centre for Cell Biology Phone: +44 (0)131 6513374 Institute for Cell & Molecular Biology Fax: +44 (0)131 6507360 Swann Building, Mayfield Road University of Edinburgh Edinburgh EH9 3JR UK

Hello, in my opinion (qRT)PCR can validate a lot of things (is the microarray probing for the right gene? Is that gene really expressed?...), but it CANNOT validate the cutoff unless you increase the sample size! To understand that just imagine a perfect world in which a) PCR gave you exactly the same results as the microarray b) you have the time and money to do a PCR for each sample and all genes on the array In that case you would simply duplicate your data-set and (if you used limma again) obtain exactly the same p-values and be faced with the same old problem where to place your cut-off. So if you you get a significant finding with the PCR for a gene that was significant on the microarray it still could be a false positive! Re-analysing the same samples with another technology can only remove technical variabiltiy not biological variability! If PCR has less technical variability than the microarray the p-values will tend to be lower for truly differential genes, but they will not be equal to zero, so there will still be uncertainty, that can only be reduced by analysing more samples. Cheers Claus Richard Friedman wrote: > Dear Mouselly, > > A B>f 0 is a good place to start. > It is advisable to spot check genes by PCR to validate the cutoff. > > Best wishes, > Rich > > On Jul 18, 2007, at 10:06 AM, el mousselly antra wrote: > > >> hi, >> i have a question on limma package >> when we have the result of tnis pakage and we have p-vlue and the >> value of B . >> i i want to know on wich value of p we consider that genes are DE , >> we take 0.05 is the threshold or what and i see that when we consider >> 0.05 as threshold wa have a lot of genes DE. >> so, i want some rule (threshold)that will be raisonnable and >> efficient to detect gnes DE >> thank you >> sincerly >> >> >> >> >> --------------------------------- >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> 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 >> > > ------------------------------------------------------------ > Richard A. Friedman, PhD > Biomedical Informatics Shared Resource > Lecturer > Department of Biomedical Informatics > Educational Coordinator > Center for Computational Biology and Bioinformatics > National Center for Multiscale Analysis of Genomic Networks > Box 95, Room 130BB or P&S 1-420C > Columbia University Medical Center > 630 W. 168th St. > New York, NY 10032 > (212)305-6901 (5-6901) (voice) > friedman at cancercenter.columbia.edu > http://cancercenter.columbia.edu/~friedman/ > > In memoriam, John Stewart Williamson > > _______________________________________________ > 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 > > > > > > -- ********************************************************************** ************* Dr Claus-D. Mayer | http://www.bioss.ac.uk Biomathematics & Statistics Scotland | email: claus at bioss.ac.uk Rowett Research Institute | Telephone: +44 (0) 1224 716652 Aberdeen AB21 9SB, Scotland, UK. | Fax: +44 (0) 1224 715349