On Mon, Mar 13, 2006 at 10:41:08AM -0500, Naomi Altman wrote: <naomi>Since you used "adjust=fdr", the p-value column of the TopTable are <naomi>the "adjusted p-values" after fdr (which I think of as q-values). I'm not that sure about this. Reading the code of toptable it uses p.adjust(...,adjust.method="fdr"), which strictly is not exactly a q-value. You get the same if p0 = 1, which may not be the case. Using qvalue(toptable$P.Value) will give you a q-value (according to Storey et al.) HIH, Ste <naomi> <naomi>You can either pick some q-value you want to use to select the <naomi>significantly differentially expressing genes, or you can pick some <naomi>number of genes, and report the q-value of the least significant of these. <naomi> <naomi>--Naomi <naomi> <naomi>At 10:17 AM 3/13/2006, Assa Yeroslaviz wrote: <naomi>>Hi, <naomi>> <naomi>>I know this theme is an old one, but I look all over the archives and didn't <naomi>>find any help regarding this subject. <naomi>>Using Affymetrix chips I compared two groups (Control vs compound) with the <naomi>>limma procedure. <naomi>>I made an affybatch Object using ReadAffy(), normalised the data with the <naomi>>RMA algorithm and fitted a linear model with lmFit. <naomi>> <naomi>> >affy <- ReadAffy(filenames=vec) <naomi>> >eset <- rma(affy) <naomi>> >design <- cbind(Control=1,AE0627vsCT=c(rep(0,6),rep(1,4))) <naomi>> <naomi>>my design matrix looks like that (I have 6 control and 4 treated arrays): <naomi>> > design <naomi>> Control AE143vsCT <naomi>> [1,] 1 0 <naomi>> [2,] 1 0 <naomi>> [3,] 1 0 <naomi>> [4,] 1 0 <naomi>> [5,] 1 0 <naomi>> [6,] 1 0 <naomi>> [7,] 1 1 <naomi>> [8,] 1 1 <naomi>> [9,] 1 1 <naomi>>[10,] 1 1 <naomi>> <naomi>>so I don't need any contrast matrix. <naomi>>The list is 22,810 genes long. But not all of them can be significant. I <naomi>>hope!!! <naomi>> <naomi>>I sorted the genes with: <naomi>> >sig_table <- topTable(fit_e, coef=2, number=6000, adjust="fdr", sort.by= <naomi>>"P") <naomi>> <naomi>>I've chosen 6000 as an arbitrary value, but I still don't know how many <naomi>>genes are siginificant. <naomi>> <naomi>>My question(s) is(are): <naomi>> <naomi>>1. How do I find out how many genes are significantly differentially <naomi>>expressed using a define p-value or FDR? <naomi>> Can I use here the decideTests() function although I don't have any <naomi>>contrasts? <naomi>> <naomi>>2. In SAM one can look for the false discovery rates using the different <naomi>>delta-values. <naomi>> Is it possible to set a fixed FDR-Value in Limma? <naomi>> Where Do I find the FDR rates of my significant genes? <naomi>> <naomi>>3. Is there a possibility (like in SAM) to show the results in a graphic ( <naomi>>scatter plot etc.)? <naomi>> <naomi>>Every comment and suggestion would be appreciated! <naomi>> <naomi>>THX <naomi>> <naomi>>Assa <naomi>> <naomi>>-- <naomi>>Assa Yeroslaviz <naomi>>Loetzener Str. 15 <naomi>>51373 Leverkusen <naomi>> <naomi>> [[alternative HTML version deleted]] <naomi>> <naomi>>_______________________________________________ <naomi>>Bioconductor mailing list <naomi>>Bioconductor at stat.math.ethz.ch <naomi>>https://stat.ethz.ch/mailman/listinfo/bioconductor <naomi> <naomi>Naomi S. Altman 814-865-3791 (voice) <naomi>Associate Professor <naomi>Dept. of Statistics 814-863-7114 (fax) <naomi>Penn State University 814-865-1348 (Statistics) <naomi>University Park, PA 16802-2111 <naomi> <naomi>_______________________________________________ <naomi>Bioconductor mailing list <naomi>Bioconductor at stat.math.ethz.ch <naomi>https://stat.ethz.ch/mailman/listinfo/bioconductor -- Stefano Calza, PhD Researcher - Biostatistician Sezione di Statistica Medica e Biometria Dipartimento di Scienze Biomediche e Biotecnologie Universit? degli Studi di Brescia - Italy Viale Europa, 11 25123 Brescia email: calza at med.unibs.it Phone: +390303717653 Fax: +390303717488

Naomi Altman wrote: > Dear Assa, > This one will need to go to the list. I only use topTable. > > --Naomi > > p.s. Please post all questions directly to the listserv. That way > you can get more help than any one person can give, and no-one is > over-burdened. > > At 03:29 AM 3/14/2006, you wrote: > >>Hi, >> >>I was wondering if I can use decideTests()-function in my tests, >>even if i didn't use any contrast matrix. Yes you can. However, depending on how you set up your design matrix you may or may not get the comparisons you are looking for. >> >>I didn't get the difference between decideTests() and classifyTests(). decideTests() is a wrapper function that calls classifyTestsP() or classifyTestsF() if you select method = "heirarchical" or "nestedF", respectively. decideTests() also uses different default p-value cutoffs and multiplicity corrections. Looking at the code for decideTests() may help you understand the differences. HTH, Jim >> >>I would be happy for any help you can give me. >> >>THX >> >>Assa >> >>On 3/13/06, Naomi Altman >><<mailto:naomi at="" stat.psu.edu="">naomi at stat.psu.edu> wrote: >>Since you used "adjust=fdr", the p-value column of the TopTable are >>the "adjusted p-values" after fdr (which I think of as q-values). >> >>You can either pick some q-value you want to use to select the >>significantly differentially expressing genes, or you can pick some >>number of genes, and report the q-value of the least significant of these. >> >>--Naomi >> >>At 10:17 AM 3/13/2006, Assa Yeroslaviz wrote: >> >>>Hi, >>> >>>I know this theme is an old one, but I look all over the archives and didn't >>>find any help regarding this subject. >>>Using Affymetrix chips I compared two groups (Control vs compound) with the >>>limma procedure. >>>I made an affybatch Object using ReadAffy(), normalised the data with the >>>RMA algorithm and fitted a linear model with lmFit. >>> >>> >>>>affy <- ReadAffy(filenames=vec) >>>>eset <- rma(affy) >>>>design <- cbind(Control=1,AE0627vsCT=c(rep(0,6),rep(1,4))) >>> >>>my design matrix looks like that (I have 6 control and 4 treated arrays): >>> >>>>design >>> >>> Control AE143vsCT >>> [1,] 1 0 >>> [2,] 1 0 >>> [3,] 1 0 >>> [4,] 1 0 >>> [5,] 1 0 >>> [6,] 1 0 >>> [7,] 1 1 >>> [8,] 1 1 >>> [9,] 1 1 >>>[10,] 1 1 >>> >>>so I don't need any contrast matrix. >>>The list is 22,810 genes long. But not all of them can be significant. I >>>hope!!! >>> >>>I sorted the genes with: >>> >>>>sig_table <- topTable(fit_e, coef=2, number=6000, adjust="fdr", sort.by= >>> >>>"P") >>> >>>I've chosen 6000 as an arbitrary value, but I still don't know how many >>>genes are siginificant. >>> >>>My question(s) is(are): >>> >>>1. How do I find out how many genes are significantly differentially >>>expressed using a define p-value or FDR? >>> Can I use here the decideTests() function although I don't have any >>>contrasts? >>> >>>2. In SAM one can look for the false discovery rates using the different >>>delta-values. >>> Is it possible to set a fixed FDR-Value in Limma? >>> Where Do I find the FDR rates of my significant genes? >>> >>>3. Is there a possibility (like in SAM) to show the results in a graphic ( >>>scatter plot etc.)? >>> >>>Every comment and suggestion would be appreciated! >>> >>>THX >>> >>>Assa >>> >>>-- >>>Assa Yeroslaviz >>>Loetzener Str. 15 >>>51373 Leverkusen >>> >>> [[alternative HTML version deleted]] >>> >>>_______________________________________________ >>>Bioconductor mailing list >>><mailto:bioconductor at="" stat.math.ethz.ch="">Bioconductor at stat.math.ethz.ch >>>https://stat.ethz.ch/mailman/listinfo/bioconductor >> >>Naomi S. Altman 814-865-3791 (voice) >>Associate Professor >>Dept. of Statistics 814-863-7114 (fax) >>Penn State University 814-865-1348 (Statistics) >>University Park, PA 16802-2111 >> >> >> >> >>-- >>Assa Yeroslaviz >>Loetzener Str. 15 >>51373 Leverkusen > > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 (Statistics) > University Park, PA 16802-2111 > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor -- 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

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