Yep. You have to remember that log2(this/that) = log2(this) - log2(that), so if you are in the log space you have to subtract to compute what would be division on the natural scale. Best, Jim On 7/6/2012 4:19 PM, john herbert wrote: > OK, I solved it using raw values and see 6.09 is log2 FC. > Thanks. > > John. > > On Fri, Jul 6, 2012 at 9:00 PM, john herbert<arraystruggles at="" gmail.com=""> wrote: >> Thanks a lot James, >> Yes, the raw PAI values are very big and I am feeding Limma log2 and >> normalized values. >> >> So if I have a log2 value of 22.59173 for Tumour and a log2 value for >> wound of 16.50009333 >> subtracting tumour - wound = 6.09 (the same number toptable comes up with) >> >> What is this 6.09 value, is that fold change or log2 fold change? >> >> I would guess fold change as 2 to the power of 6 = 64 fold change but >> topTable labels it as logFC; please explain why? >> >> Thank you, >> >> John. >> >> >> >> >> >>>> Tumour average = 22.59173 >>>> Wound average = 16.50009333 >>>> Log2 Fold change = 0.453320567 >> >> >> On Fri, Jul 6, 2012 at 8:36 PM, James W. MacDonald<jmacdon at="" uw.edu=""> wrote: >>> Hi John, >>> >>> >>> On 7/6/2012 3:12 PM, john herbert wrote: >>>> Dear all, >>>> I have a problem with the log Fold changes calculated in Limma. I am >>>> using protein abundance index of proteomic data >>>> The log2 of this data is normally distributed and after log2, I use >>>> quantile normalization >>>> >>>> This is then the data matrix I use as input to Limma >>>> >>>>> class(norm_ctw) >>>> [1] "matrix" >>>> >>>>> dim(norm_ctw) >>>> [1] 683 9 >>>> design<- model.matrix(~ 0+factor(c(1,1,1,2,2,2,3,3,3))) >>>> colnames(design)<- c("cam", "tumour", "wound") >>>> fit<- lmFit(norm_ctw, design) >>>> >>>> contrast.matrix<- makeContrasts(tumour-wound, tumour-cam, levels=design) >>>> fit2<- contrasts.fit(fit, contrast.matrix) >>>> fit2<- eBayes(fit2) >>>> >>>> topTable(fit2, coef=1, adjust="BH") >>>> >>>> Taking one gene as an example. NAMPT in tumour versus wound and >>>> calculating fold change by hand of normalized data; >>>> >>>>> norm_ctw["NAMPT",] >>>> cam1 cam2 cam3 tumour1 tumour2 tumour3 wound1 >>>> wound2 wound3 >>>> 19.80164 19.46355 19.26075 22.75347 22.62651 22.39521 16.17398 16.60262 >>>> 16.72368 >>>> >>>> In Excel, calculating log2 fold change using Average of Tumour/Average >>>> of wound = >>>> T1 22.75347 T2 22.62651 T3 22.39521 W1 16.17398 W2 >>>> 16.60262 W3 16.72368 >>>> Tumour average = 22.59173 >>>> Wound average = 16.50009333 >>>> Log2 Fold change = 0.453320567 >>> >>> Wait a minute... Are these data logged or not? You say above that you take >>> logs and then normalize, and then you present some data that would be really >>> big if they were log2 variates (but then I have no idea of the scale for >>> protein abundance data). >>> >>> Anyway, you are acting like these data are not logged, whereas limma assumes >>> they are. So you either have to take logs before feeding into limma, or you >>> need to compute the fold change by subtraction (if the data above are >>> already logged). >>> >>> Best, >>> >>> Jim >>> >>> >>> >>>> >>>> However, from TopTable.... >>>>> topTable(fit2,coef=1) >>>> ID logFC AveExpr t P.Value adj.P.Val >>>> B >>>> 431 NAMPT 6.091632 19.53349 20.16810 2.688444e-09 1.750946e-06 >>>> 11.409857 >>>> >>>>> From toptable, NAMPT has an apparent log2 FC of 6 or 64 fold change >>>> but that is impossible right?? >>>> >>>> Please can someone explain if I am using Limma wrong or how the fold >>>> change can be massively different between "by hand" and with Limma. >>>> >>>> Thank you very much for any advice. >>>> >>>> John. >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor at r-project.org >>>> 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 >>> University of Washington >>> Environmental and Occupational Health Sciences >>> 4225 Roosevelt Way NE, # 100 >>> Seattle WA 98105-6099 >>> -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099