Sorry I'd been away and missed some posts - it seems limma - fdr is a hot topic at the moment. To link this to an answer already provided by gordon - this is the thread I found earlier - https://www.stat.math.ethz.ch/pipermail/bioconductor/2004-August/00561 6. html This addresses possibilities to fdr correct on the gene and contrast level, however I was wondering if anyone can confirm that the fdr in toptable is on the gene level. If so can the vector of p-values be passed back to classifytests to put the 1 / -1's in for those up / down reg? Also is there any difference in doing contrasts then genes vs. the reverse? I'd also be interested in the discussion of how quantitive limma p-values are (see point 4 in previous mail below). On these lines - from the abstract of Smyth. LM and eBayes methods... "The eBayes approach is equilivent to shrinkage of the estimated sample variances towards a pooled estimate.." I assume (as it works with low #'s of arrays) that the pooled estimate is between genes rather than arrays? If so then what about pooling between arrays - eg: when you have 10 lines exposed to a common treatment and say 3 reps. So 10x2 x3reps. Using the pooled estimate across arrays (30reps control vs. 30 reps treated) to then apply to differences between lines due to the treatment (only 3 vs. 3 arrays)? Obviously the lines would have to be generally similar. But wouldn't this be more biologically relevent than assuming similar expressed genes have similar variance? Thanks in advance, Matt -----Original Message----- From: Matthew Hannah Sent: Donnerstag, 19. August 2004 11:53 To: 'bioconductor@stat.math.ethz.ch' Subject: Limma p-values, fdr and classifyTests Hi, I'm using Limma and have some questions related to p-values and gene selection. Looking in the classifyTests help I noticed "The adjustment for multiple testing is across the contrasts rather than the more usual control across genes." There is also a multiple testing procedure for the topTable function but this appears to give a different result (<sig. genes)="" -="" is="" this="" the="" more="" usual="" control="" across="" genes?="" why="" are="" they="" different?="" is="" it="" possible="" to="" take="" both="" into="" account?="" basically="" i'm="" not="" just="" interested="" in="" the="" top="" 50="" genes,="" i'd="" like="" to="" identify="" all="" 'significant'="" changes.="" i="" thought="" the="" output="" from="" classifytestsp="" (0.01,="" fdr)="" would="" be="" good="" but="" this="" doesn't="" account="" for="" across="" gene="" multiple="" testing.="" is="" there="" an="" easy="" way="" to="" get="" this="" output="" rather="" than="" calling="" toptable="" (if="" the="" fdr="" is="" across="" genes?)="" for="" all="" genes?="" classifytestsf="" could="" be="" useful="" as="" i'm="" looking="" at="" a="" treatment="" effect="" on="" different="" lines.="" however,="" again="" there="" is="" no="" account="" of="" across="" gene="" multiple="" testing.="" is="" there="" any="" possibility="" to="" do="" this?="" also,="" all="" this="" talk="" of="" p-values="" but="" there="" is="" a="" note="" saying="" they="" are="" nominal.="" how="" far="" does="" this="" hold="" true="" -="" do="" you="" always="" have="" to="" select="" a="" cut-off="" based="" on="" some="" criteria="" (eg:control="" genes)="" or="" is="" there="" a="" way="" they="" can="" be="" applied="" quantitatively?="" finally="" is="" it="" ok="" to="" pass="" an="" ebayes="" fit="" to="" toptable?="" what's="" the="" difference="" compared="" to="" toptable?="" fit="" <-="" lmfit(esetgcrma,="" design)="" con.fit="" <-="" contrasts.fit(fit,="" cont.matrix)="" ebfit="" <-="" ebayes(con.fit)="" toptable(ebfit,coef="1,number=50,adjust=" fdr")"="" thanks="" alot,="" matt<="" div="">

At 11:53 PM 23/08/2004, Matthew Hannah wrote: >Sorry I'd been away and missed some posts - it seems limma - fdr is a >hot topic at the moment. > >To link this to an answer already provided by gordon - this is the >thread I found earlier - >https://www.stat.math.ethz.ch/pipermail/bioconductor/2004-August/0056 16. >html > >This addresses possibilities to fdr correct on the gene and contrast >level, however I was wondering if anyone can confirm that the fdr in >toptable is on the gene level. The adjustment is across genes. It can only be across genes because topTable considers only one contrast at a time. > If so can the vector of p-values be >passed back to classifytests to put the 1 / -1's in for those up / down >reg? No > Also is there any difference in doing contrasts then genes vs. the >reverse? Yes. >I'd also be interested in the discussion of how quantitive limma >p-values are (see point 4 in previous mail below). Too big a question for here. The p-values could be believed if the heirarchical model is correct, but no models are correct for microarray data. >On these lines - from the abstract of Smyth. LM and eBayes methods... >"The eBayes approach is equilivent to shrinkage of the estimated sample >variances towards a pooled estimate.." I assume (as it works with low >#'s of arrays) that the pooled estimate is between genes rather than >arrays? If so then what about pooling between arrays - eg: when you have >10 lines exposed to a common treatment and say 3 reps. So 10x2 x3reps. >Using the pooled estimate across arrays (30reps control vs. 30 reps >treated) to then apply to differences between lines due to the treatment >(only 3 vs. 3 arrays)? Obviously the lines would have to be generally >similar. But wouldn't this be more biologically relevent than assuming >similar expressed genes have similar variance? The variances are already pooled across arrays. There is no assumption that "similar expressed genes have similar variances". Gordon >Thanks in advance, >Matt