Hi Gordon, Thank you so much. Finally, things start working! Best wishes, Wendy On 11 April 2011 03:22, Gordon K Smyth <smyth@wehi.edu.au> wrote: > Hi Wendy, > > It occured to me after sending my last email that the code I gave you will > compute contrasts in the form OtherCell-BCELLA2 rather than > BCELLA2-OtherCell, so you need to use results<0 for positive signature genes > and results>0 for negative, i.e., the other way around to my email. > > Best wishes > Gordon > > > On Mon, 11 Apr 2011, Gordon K Smyth wrote: > > Hi Wendy, >> >> First, let me mention that fit$sigma holds the between-replicate standard >> deviation for each gene, which is probably what you were looking for in your >> original post. >> >> Second, here is a way to compare each cell type with each of the others. >> Suppose you want signature genes for BCELLA2. The following will compare >> all other cell types back to BCELLA2: >> >> f <- factor(samplenames) >> BCELLA2vs <- relevel(f,ref="BCELLA2") >> design <- model.matrix(~BCELLA2vs) >> fit <- eBayes(lmFites.mx,design)) >> >> Now do all the pairwise tests asking for FDR better than 0.1 and fold >> change at least 1.5 (you can choose the settings you want): >> >> results <- decideTests(fit[,-1], p=0.1, lfc=log2(1.5)) >> >> You can find the indices of positive signature genes that are up in all >> comparisons by: >> >> i <- apply(results>0,1,all) >> >> or negative signature genes by >> >> i <- apply(results<0,1,all) >> >> However, you have so many cell types, some of which are probably quite >> similar. You might allow some of these comparisons to be non- significant. >> Suppose you decide to restrict to genes that are up in BCELLA2 vs 20 out of >> the 23 other cell types: >> >> i <- rowSums(results>0) >= 20 >> >> You can see that any variation of this is quite easy. >> >> Best wishes >> Gordon >> >> >> On Sun, 10 Apr 2011, Wendy Qiao wrote >> >> Dear Gordon, >>> >>> Thank you very much for your information. >>> >>> You are right-I am comparing each cell type to the average of all the >>> others. Ideally, I want to compare each cell type to the others pairwisely >>> and find the signature genes as you suggested. I tried this before, but I am >>> afraid that I did not take the full advantages of limma as I am new here. >>> >> > Here is my problem. I am comparing 24 blood cell types (92 arrays in >>> total). Following are the steps that I took. The pairwise comparison take >>> dozens of ligands. Then I used topTable to find overexpressed genes from >>> each comparison, and finally do the 'intersect'. I believe that there is an >>> easy way to do all the pairwise comparisons and use decideTests(). Would you >>> mind giving me some hints on that? >>> >>> Thank you very much. >>> Wendy >>> >>> f<-factor(samplenames) #sampelenames = colnames of 92 arrays with >>> replicates have the same name >>> design<-model.matrix(~0+f) >>> fit<-lmFites.mx,design) >>> fit<-eBayes(fit) >>> >>> contrast.matrix<-makeContrasts(fBASO1-fBCELLA1, fBASO1-fBCELLA2..... >>> >>> >>> >>> fBASO1 fBCELLA1 fBCELLA2 fBCELLA3 ... >>> 1 1 0 0 0 ... >>> 2 1 0 0 0 ... >>> 3 1 0 0 0 ... >>> 4 0 1 0 0 ... >>> ... >>> 92 0 0 0 0 ... >>> >>> >>> On 10 April 2011 18:30, Gordon K Smyth <smyth@wehi.edu.au> wrote: >>> >>> Dear Wendy, >>>> >>>> From your email, I assume that you have found signature genes by >>>> comparing each cell type to all the other cell types treated as one group. >>>> As you have correctly observed, this does not take account of consistency >>>> within the other cell types. Another way to find signature genes, that I >>>> think is superior, is to choose signature genes to be those genes that are >>>> uniquely higher or lower in the relevant cell type with respect to each of >>>> the other cell types individually. In other words, a positive signature >>>> gene is higher in the relevant cell type against every other cell type, not >>>> just against the average of the other cell types. This was the method used >>>> in: >>>> >>>> Lim E, Vaillant F, Wu D, Forrest NC, Pal B, Hart AH, Asselin- Labat ML, >>>> Gyorki DE, Ward T, Partanen A, Feleppa F, Huschtscha LI, Thorne HJ; >>>> kConFab, >>>> Fox SB, Yan M, French JD, Brown MA, Smyth GK, Visvader JE, Lindeman GJ. >>>> Aberrant luminal progenitors as the candidate target population for >>>> basal >>>> tumor development in BRCA1 mutation carriers. Nature Medicine 2009. >>>> >>>> to find stem cell signature genes. If you do it this way, consistency >>>> within the cell types is automatically taken care off, because the t-tests >>>> will only choose genes with consistent behaviour. limma can do all the >>>> relevant pairwise tests for you in a couple of lines, then use decideTests() >>>> to choose the signature genes. >>>> >>>> Best wishes >>>> Gordon >>>> >>>> --------------------------------------------- >>>> Professor Gordon K Smyth, >>>> NHMRC Senior Research Fellow, >>>> Bioinformatics Division, >>>> Walter and Eliza Hall Institute of Medical Research, >>>> 1G Royal Parade, Parkville, Vic 3052, Australia. >>>> Tel: (03) 9345 2326, Fax (03) 9347 0852, >>>> smyth@wehi.edu.au >>>> http://www.wehi.edu.au >>>> http://www.statsci.org/smyth >>>> >>>> >>>> Date: Sat, 9 Apr 2011 19:57:25 -0400 >>>> >>>>> From: Wendy Qiao <wendy2.qiao@gmail.com> >>>>> To: bioconductor@r-project.org >>>>> Subject: [BioC] identifying consistently expressed genes between >>>>> replicates >>>>> >>>>> Hi all, >>>>> >>>>> I am comparing a number of cell types, and am wanting to find the >>>>> signature genes of each cell type. I used the limma package to do this. The >>>>> signature genes of a given cell type are found by the fold different between >>>>> the given cell type and grand mean of all the cell types, as well as the >>>>> BH-adjusted p-values. I want to add another condition to test the >>>>> consistency of expression levels of the selected genes for each cell type. I >>>>> can do this by looking at the standard deviations of gene expressions >>>>> between replicates. I am just wondering if there is any function in limma or >>>>> other BioConductor package to do this. >>>>> >>>>> Thank you in advance, >>>>> Wendy >>>>> >>>> >> > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:10}}