Thanks a lot Sean. I will try this. Best wishes, Suparna. On 10 September 2012 11:57, Sean Davis <sdavis2@mail.nih.gov> wrote: > > > On Mon, Sep 10, 2012 at 1:57 AM, suparna mitra <smitra@liverpool.ac.uk>wrote: > >> Hello group, >> >> Can anybody please you please suggest how I can do the following in BioC >> to >> match a previous set of experiment. >> #Entitylist : A v R Filtered on Error - CV < 300.0 percent >> #Interpretation : A vs R Treatment >> #Experiment: RMA >> #p-value cut-off:0.05 >> #Selected Test : T Test unpaired >> #p-value computation: Asymptotic >> >> Until now I have done rma on direct data and performed t-test. But how can >> I 'Filtered on Error - CV' and select p-value computation as 'Asymptotic'? >> >> This is what I did. >> >> > sessionInfo() >> R version 2.15.1 (2012-06-22) >> Platform: i386-apple-darwin9.8.0/i386 (32-bit) >> >> locale: >> [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8 >> >> attached base packages: >> [1] stats graphics grDevices utils datasets methods base >> >> other attached packages: >> [1] statmod_1.4.15 limma_3.12.1 >> annotate_1.34.1 hugene10stprobeset.db_8.0.1 >> org.Hs.eg.db_2.7.1 >> >> [6] BiocInstaller_1.4.7 affycoretools_1.28.0 KEGG.db_2.7.1 >> GO.db_2.7.1 AnnotationDbi_1.18.1 >> [11] affy_1.34.0 Biobase_2.16.0 >> BiocGenerics_0.2.0 pd.hugene.1.0.st.v1_3.6.0 RSQLite_0.11.1 >> >> [16] DBI_0.2-5 oligo_1.20.4 >> oligoClasses_1.18.0 >> >> >> rmaOligoinvivo = oligo::rma(InVivodat1) >> Background correcting >> Normalizing >> Calculating Expression >> >> > rmaOligoinvitro = oligo::rma(InVitrodat1) >> Background correcting >> Normalizing >> Calculating Expression >> >> > maplot(rmaOligoinvivo) >> > maplot(rmaOligoinvitro) >> > InVivoTargets >> FileName Treatment >> 1 MC1 A >> 2 MC2 A >> 3 MC3 A >> 4 MC4 A >> 5 MC5 A >> 6 MC6 A >> 7 MC7 R >> 8 MC8 R >> 9 MC9 R >> 10 MC10 R >> 11 MC11 R >> 12 MC12 R >> 13 MC13 T >> 14 MC14 T >> 15 MC15 T >> 16 MC16 T >> 17 MC17 T >> 18 MC18 T >> > InVitroTargets >> FileName Treatment Batch CD4 >> 1 MC19 RY 1 High >> 2 MC20 TY 1 Low >> 3 MC21 RY 2 High >> 4 MC22 TY 2 High >> 5 MC23 TY 2 Low >> 6 MC24 RY 2 High >> 7 MC25 TXY 1 Low >> 8 MC26 RXY 1 High >> 9 MC27 RXY 2 Low >> 10 MC28 TXY 2 High11 MC29 RXY 2 High >> 12 MC30 TXY 2 High >> >> f.invivo <- factor(InVivoTargets$Treatment, levels = c("A", "R", "T")) >> >> design.invivo <- model.matrix(~0 + f.invivo) >> >> > >> >> > colnames(design.invivo) <- c("A", "R", "T") >> >> > Just subset the rmaOligoinvivo object by CV. > > rmaOligoinvivo = rmaOligoinvivo[apply(exprs(rmaOligoinvivo)),1,function(x) > {sd(x)/mean(x)})>3,] > > This is untested but, hopefully, you can get the idea and can implement a > solution that works for you. I'm not sure where the cutoff of 3 (300%) > comes from, so you may find that you need to adjust that to your data. > > Sean > > > > fit.invivo <- lmFit(rmaOligoinvivo, design.invivo) >> >> > contrast.matrix.invivo <- makeContrasts(R-A, T-R, T-A,levels = >> design.invivo) >> >> > fit2.invivo <- contrasts.fit(fit.invivo, contrast.matrix.invivo) >> >> > fit2.invivo <-eBayes(fit2.invivo) >> >> > topTable(fit2.invivo, coef = 1, adjust = "fdr") >> >> ID logFC AveExpr t P.Value adj.P.Val >> B >> >> 8819 7943047 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013 >> -2.023533 >> >> 9675 7950951 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013 >> -2.023533 >> >> 18889 8043581 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013 >> -2.023533 >> >> 19899 8053785 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013 >> -2.023533 >> ... >> >> Thanks a lot, >> Mitra >> >> >> > >> > On 7 September 2012 14:55, James W. MacDonald <jmacdon@uw.edu> wrote: >> > >> >> >> >> >> >> On 9/7/2012 7:26 AM, Sean Davis wrote: >> >> >> >>> On Fri, Sep 7, 2012 at 7:14 AM, suparna mitra<smitra@liverpool.ac.uk>> >** >> >> >>> wrote: >> >>> >> >>> Oh thanks.. I missed this point. But can you suggest me one more >> >>>> thing... >> >>>> when I tried adjust = "BH" (Benjamini-Hochberg I suppose) I got the >> same >> >>>> result as adjust = "fdr". for topTable. Is it normal? >> >>>> >> >>>> Yes. They are the same. See the help for p.adjust for details. >> >>> >> >>> >> >>> Further when I tried to do vennDiagram I was surprized to see 0 in >> all >> >>>> circles. Thus I thought I must be doing something wrong. Sorry if my >> >>>> question is silly. >> >>>> >> >>>> Unfortunately, you have no significantly differentially- expressed >> >>> genes. >> >>> Note that all of the adjusted p-values are pretty high. You can >> try to >> >>> filter your genes based on variance before testing to try to reduce >> the >> >>> number of genes entering your test and multiple correction. However, >> >>> having worked with this kind of biological system (patients), you may >> >>> suffering from a problem of a small biological effect in the setting >> of >> >>> large biological variation. A larger sample size may be necessary. >> >>> >> >> >> >> You may also be suffering from large technical variation, which could >> be >> >> helped by applying array weights. See ?arrayWeights for more >> information. >> >> >> >> Best, >> >> >> >> Jim >> >> >> >> >> >> >> >>> Sean >> >>> >> >>> >> >>> Here is what I tried. >> >>>> >> >>>> topTable(fit2.invivo, coef = 1, adjust = "fdr") >> >>>>> >> >>>> ID logFC AveExpr t P.Value adj.P.Val >> >>>> B >> >>>> >> >>>> 8819 7943047 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013 >> >>>> -2.023533 >> >>>> >> >>>> 9675 7950951 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013 >> >>>> -2.023533 >> >>>> >> >>>> 18889 8043581 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013 >> >>>> -2.023533 >> >>>> >> >>>> 19899 8053785 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013 >> >>>> -2.023533 >> >>>> >> >>>> 3713 7896238 0.7731154 2.999029 4.796490 1.434510e-04 0.9552974 >> >>>> -2.323922 >> >>>> >> >>>> 19926 8054075 -0.3816217 4.062936 -4.557543 2.424324e-04 0.9998796 >> >>>> -2.454618 >> >>>> >> >>>> 18660 8041642 -1.0007299 4.220083 -4.290346 4.379518e-04 0.9998796 >> >>>> -2.607991 >> >>>> >> >>>> 3759 7896284 -0.7555604 5.727302 -4.159251 5.861601e-04 0.9998796 >> >>>> -2.685960 >> >>>> >> >>>> 6238 7917530 0.5596335 11.170012 4.117421 6.433789e-04 0.9998796 >> >>>> -2.711203 >> >>>> >> >>>> 15545 8010622 -0.3324189 3.771856 -3.971869 8.899739e-04 0.9998796 >> >>>> -2.800385 >> >>>> >> >>>> topTable(fit2.invivo, coef = 2, adjust = "fdr") >> >>>>> >> >>>> ID logFC AveExpr t P.Value adj.P.Val >> >>>> B >> >>>> >> >>>> 621 7893126 -0.5848178 4.412764 -4.577179 0.0002321630 0.9999684 >> >>>> -2.469821 >> >>>> >> >>>> 6238 7917530 -0.5783362 11.170012 -4.255023 0.0004737013 0.9999684 >> >>>> -2.652426 >> >>>> >> >>>> 26642 8120756 -1.0354557 5.439265 -4.238568 0.0004913467 0.9999684 >> >>>> -2.662042 >> >>>> >> >>>> 1687 7894197 -0.9004303 2.631359 -4.169362 0.0005731153 0.9999684 >> >>>> -2.702782 >> >>>> >> >>>> 2353 7894871 0.8441561 4.815714 4.161413 0.0005833454 0.9999684 >> >>>> -2.707492 >> >>>> >> >>>> 3641 7896166 -0.6206262 7.735431 -4.144225 0.0006060986 0.9999684 >> >>>> -2.717698 >> >>>> >> >>>> 2088 7894602 0.4713716 2.841855 4.115413 0.0006462632 0.9999684 >> >>>> -2.734873 >> >>>> >> >>>> 5638 7911243 -0.7263053 5.676410 -4.053352 0.0007421075 0.9999684 >> >>>> -2.772143 >> >>>> >> >>>> 7851 7933619 0.4194965 8.480778 4.040446 0.0007637691 0.9999684 >> >>>> -2.779941 >> >>>> >> >>>> 20151 8056222 -0.8981049 7.892249 -4.031734 0.0007787485 0.9999684 >> >>>> -2.785214 >> >>>> >> >>>> topTable(fit2.invivo, coef = 3, adjust = "fdr") >> >>>>> >> >>>> ID logFC AveExpr t P.Value adj.P.Val >> >>>> B >> >>>> >> >>>> 2590 7895109 -0.9415442 4.766552 -5.803704 1.670491e-05 0.5562234 >> >>>> -0.6982314 >> >>>> >> >>>> 6210 7917182 -0.2981341 3.273225 -5.028595 8.656989e-05 0.6545102 >> >>>> -1.2472882 >> >>>> >> >>>> 27812 8132245 -0.4595908 5.409405 -4.995303 9.304487e-05 0.6545102 >> >>>> -1.2727646 >> >>>> >> >>>> 867 7893372 1.3251627 3.017891 4.981783 9.581361e-05 0.6545102 >> >>>> -1.2831553 >> >>>> >> >>>> 26802 8122099 -0.4740894 4.548920 -4.828048 1.338927e-04 0.6545102 >> >>>> -1.4031177 >> >>>> >> >>>> 808 7893313 1.0125247 7.938503 4.739949 1.623493e-04 0.6545102 >> >>>> -1.4733549 >> >>>> >> >>>> 26093 8115516 -0.5100673 6.294000 -4.703760 1.757561e-04 0.6545102 >> >>>> -1.5025187 >> >>>> >> >>>> 587 7893092 -0.9608515 6.013864 -4.631511 2.059886e-04 0.6545102 >> >>>> -1.5612836 >> >>>> >> >>>> 22913 8084605 -0.3491973 6.211757 -4.519801 2.634837e-04 0.6545102 >> >>>> -1.6535466 >> >>>> >> >>>> 3828 7896353 0.6239117 4.207636 4.504578 2.724902e-04 0.6545102 >> >>>> -1.6662493 >> >>>> >> >>>> results<- decideTests(fit2.invivo) >> >>>>> vennDiagram(results) >> >>>>> >> >>>> see the plot attached. >> >>>> Thanks, >> >>>> Mitra >> >>>> >> >>>> >> >>>> On 7 September 2012 12:03, Sean Davis<sdavis2@mail.nih.gov> wrote: >> >>>> >> >>>> On Fri, Sep 7, 2012 at 6:57 AM, suparna mitra< >> smitra@liverpool.ac.uk >> >>>>> >> >>>>>> wrote: >> >>>>>> Dear Sean, >> >>>>>> I have been reading Bioconductor and limma user guide and thus >> this >> >>>>>> >> >>>>> is >> >>>> >> >>>>> I >> >>>>> >> >>>>>> tried. >> >>>>>> But just being a novice, wanted to make sure if I am right. >> >>>>>> I know I have perform t-test when I created the contrast, but can >> you >> >>>>>> please help me how can I perform unpaired t-test here. I am >> concerned >> >>>>>> >> >>>>> as >> >>>> >> >>>>> the patients in groups are not same. >> >>>>>> >> >>>>>> >> >>>>> The t-test you performed was unpaired; unpaired is the "default". >> >>>>> >> >>>>> Sean >> >>>>> >> >>>>> >> >>>>> Thanks, >> >>>>>> Mitra >> >>>>>> >> >>>>>> On 7 September 2012 11:41, Sean Davis<sdavis2@mail.nih.gov> >> wrote: >> >>>>>> >> >>>>>> >> >>>>>>> On Fri, Sep 7, 2012 at 5:54 AM, suparna mitra< >> >>>>>>> >> >>>>>> smitra@liverpool.ac.uk >> >>>> >> >>>>> wrote: >> >>>>>>> >> >>>>>>> Hello Group, >> >>>>>>>> I am trying t analyze my affymetrix (HuGene-1_0-st-v1) data using >> >>>>>>>> >> >>>>>>> BiC. >> >>>> >> >>>>> Previously i was using different softwares for this. And this is my >> >>>>>>>> >> >>>>>>> first >> >>>>>> >> >>>>>>> try with Bioconductor for big experiment. So thought to get some >> >>>>>>>> >> >>>>>>> advice >> >>>>> >> >>>>>> in >> >>>>>> >> >>>>>>> the beginning. >> >>>>>>>> I have Three groups of patient: (In-vivo) >> >>>>>>>> A-Acute reaction. Patient taking a drug X develops reaction. >> >>>>>>>> R-recovered (6 weeks after acute reaction-not longer taking >> drug >> >>>>>>>> >> >>>>>>> X). >> >>>> >> >>>>> T-Tolerant. Patient on X and tolerating treatment. >> >>>>>>>> >> >>>>>>>> Now in in-vitro study we used another constant Y >> >>>>>>>> RXY recovered and challenged with X+Y >> >>>>>>>> RY recovered challenged with only Y. RXY vs RY are to exclude >> >>>>>>>> >> >>>>>>> effects >> >>>>> >> >>>>>> by >> >>>>>>>> Y. >> >>>>>>>> TXY tolerant and challenged with X+Y, >> >>>>>>>> TY tolerant challenged with only Y. TXY vs TY are to exclude >> >>>>>>>> >> >>>>>>> effects >> >>>> >> >>>>> by >> >>>>>> >> >>>>>>> Y. >> >>>>>>>> >> >>>>>>>> No I want to check the cross relation and effects A vs R, RvsT >> and >> >>>>>>>> >> >>>>>>> Avs T >> >>>>> >> >>>>>> and differentially expressed genes for each comparison. And the >> >>>>>>>> >> >>>>>>> same >> >>>> >> >>>>> in >> >>>>> >> >>>>>> invitro. There are not same patients in different groups, thus I >> >>>>>>>> >> >>>>>>> think I >> >>>>> >> >>>>>> want to apply unpaired-t test. >> >>>>>>>> >> >>>>>>>> This is what I tried: >> >>>>>>>> >> >>>>>>>>> sessionInfo() >> >>>>>>>>> >> >>>>>>>> R version 2.15.1 (2012-06-22) >> >>>>>>>> Platform: i386-apple-darwin9.8.0/i386 (32-bit) >> >>>>>>>> >> >>>>>>>> locale: >> >>>>>>>> [1] >> en_GB.UTF-8/en_GB.UTF-8/en_GB.**UTF-8/C/en_GB.UTF-8/en_GB.UTF-* >> >> >>>>>>>> *8 >> >>>>>>>> >> >>>>>>>> attached base packages: >> >>>>>>>> [1] stats graphics grDevices utils datasets methods >> base >> >>>>>>>> >> >>>>>>>> other attached packages: >> >>>>>>>> [1] statmod_1.4.15 limma_3.12.1 >> >>>>>>>> annotate_1.34.1 hugene10stprobeset.db_8.0.1 >> >>>>>>>> org.Hs.eg.db_2.7.1 >> >>>>>>>> >> >>>>>>>> [6] BiocInstaller_1.4.7 affycoretools_1.28.0 >> >>>>>>>> >> >>>>>>> KEGG.db_2.7.1 >> >>>>>> >> >>>>>>> GO.db_2.7.1 AnnotationDbi_1.18.1 >> >>>>>>>> [11] affy_1.34.0 Biobase_2.16.0 >> >>>>>>>> BiocGenerics_0.2.0 pd.hugene.1.0.st.v1_3.6.0 >> >>>>>>>> >> >>>>>>> RSQLite_0.11.1 >> >>>>> >> >>>>>> [16] DBI_0.2-5 oligo_1.20.4 >> >>>>>>>> oligoClasses_1.18.0 >> >>>>>>>> >> >>>>>>>> >> >>>>>>>> rmaOligoinvivo = oligo::rma(InVivodat1) >> >>>>>>>> Background correcting >> >>>>>>>> Normalizing >> >>>>>>>> Calculating Expression >> >>>>>>>> >> >>>>>>>> rmaOligoinvitro = oligo::rma(InVitrodat1) >> >>>>>>>>> >> >>>>>>>> Background correcting >> >>>>>>>> Normalizing >> >>>>>>>> Calculating Expression >> >>>>>>>> >> >>>>>>>> maplot(rmaOligoinvivo) >> >>>>>>>>> maplot(rmaOligoinvitro) >> >>>>>>>>> InVivoTargets >> >>>>>>>>> >> >>>>>>>> FileName Treatment >> >>>>>>>> 1 MC1 A >> >>>>>>>> 2 MC2 A >> >>>>>>>> 3 MC3 A >> >>>>>>>> 4 MC4 A >> >>>>>>>> 5 MC5 A >> >>>>>>>> 6 MC6 A >> >>>>>>>> 7 MC7 R >> >>>>>>>> 8 MC8 R >> >>>>>>>> 9 MC9 R >> >>>>>>>> 10 MC10 R >> >>>>>>>> 11 MC11 R >> >>>>>>>> 12 MC12 R >> >>>>>>>> 13 MC13 T >> >>>>>>>> 14 MC14 T >> >>>>>>>> 15 MC15 T >> >>>>>>>> 16 MC16 T >> >>>>>>>> 17 MC17 T >> >>>>>>>> 18 MC18 T >> >>>>>>>> >> >>>>>>>> InVitroTargets=readTargets("~/**Desktop/Recent/Liverpool- work-** >> >>>> related/Micro_RawData/**InVitroTargets.txt") >> >> >>>> >> >>>>> InVitroTargets >> >>>>>>>>> >> >>>>>>>> FileName Treatment Batch CD4 >> >>>>>>>> 1 MC19 RY 1 High >> >>>>>>>> 2 MC20 TY 1 Low >> >>>>>>>> 3 MC21 RY 2 High >> >>>>>>>> 4 MC22 TY 2 High >> >>>>>>>> 5 MC23 TY 2 Low >> >>>>>>>> 6 MC24 RY 2 High >> >>>>>>>> 7 MC25 TXY 1 Low >> >>>>>>>> 8 MC26 RXY 1 High >> >>>>>>>> 9 MC27 RXY 2 Low >> >>>>>>>> 10 MC28 TXY 2 High >> >>>>>>>> 11 MC29 RXY 2 High >> >>>>>>>> 12 MC30 TXY 2 High >> >>>>>>>> >> >>>>>>>> f.invivo<- factor(InVivoTargets$**Treatment, levels = c("A", "R", >> >> >>>>>>>> >> >>>>>>> "T")) >> >>>> >> >>>>> design.invivo<- model.matrix(~0 + f.invivo) >> >>>>>>>> >> >>>>>>>> colnames(design.invivo)<- c("A", "R", "T") >> >>>>>>>>> fit.invivo<- lmFit(rmaOligoinvivo, design.invivo) >> >>>>>>>>> contrast.matrix.invivo<- makeContrasts(R-A, T-R, T-A,levels = >> >>>>>>>>> >> >>>>>>>> design.invivo) >> >>>>>>>> >> >>>>>>>> fit2.invivo<- contrasts.fit(fit.invivo, contrast.matrix.invivo) >> >>>>>>>>> fit2.invivo<-eBayes(fit2.**invivo) >> >> >>>>>>>>> topTable(fit2.invivo, coef = 1, adjust = "fdr") >> >>>>>>>>> >> >>>>>>>> ID logFC AveExpr t P.Value >> adj.P.Val >> >>>>>>>> B >> >>>>>>>> >> >>>>>>>> 8819 7943047 -0.3640702 4.177681 -5.395110 3.942713e-05 >> 0.3282013 >> >>>>>>>> -2.023533 >> >>>>>>>> >> >>>>>>>> 9675 7950951 -0.3640702 4.177681 -5.395110 3.942713e-05 >> 0.3282013 >> >>>>>>>> -2.023533 >> >>>>>>>> >> >>>>>>>> 18889 8043581 -0.3640702 4.177681 -5.395110 3.942713e-05 >> 0.3282013 >> >>>>>>>> -2.023533 >> >>>>>>>> >> >>>>>>>> 19899 8053785 -0.3640702 4.177681 -5.395110 3.942713e-05 >> 0.3282013 >> >>>>>>>> -2.023533 >> >>>>>>>> >> >>>>>>>> 3713 7896238 0.7731154 2.999029 4.796490 1.434510e-04 >> 0.9552974 >> >>>>>>>> -2.323922 >> >>>>>>>> >> >>>>>>>> 19926 8054075 -0.3816217 4.062936 -4.557543 2.424324e-04 >> 0.9998796 >> >>>>>>>> -2.454618 >> >>>>>>>> >> >>>>>>>> 18660 8041642 -1.0007299 4.220083 -4.290346 4.379518e-04 >> 0.9998796 >> >>>>>>>> -2.607991 >> >>>>>>>> >> >>>>>>>> 3759 7896284 -0.7555604 5.727302 -4.159251 5.861601e-04 >> 0.9998796 >> >>>>>>>> -2.685960 >> >>>>>>>> >> >>>>>>>> 6238 7917530 0.5596335 11.170012 4.117421 6.433789e-04 >> 0.9998796 >> >>>>>>>> -2.711203 >> >>>>>>>> >> >>>>>>>> 15545 8010622 -0.3324189 3.771856 -3.971869 8.899739e-04 >> 0.9998796 >> >>>>>>>> -2.800385 >> >>>>>>>> I am progressing in a right way? Further I want to perform >> unpaired >> >>>>>>>> >> >>>>>>> t >> >>>> >> >>>>> test >> >>>>>> >> >>>>>>> for comparing AvsT and so on. Any help will be really great. >> >>>>>>>> >> >>>>>>>> Hi, Mitra. I think that looks about right. You have already >> >>>>>>> >> >>>>>> performed >> >>>> >> >>>>> the unpaired t-test of AvsT (well, actually TvsA, but the p-values >> >>>>>>> >> >>>>>> will >> >>>> >> >>>>> be >> >>>>>> >> >>>>>>> the same) as coefficient 3. >> >>>>>>> >> >>>>>>> Sean >> >>>>>>> >> >>>>>>> >> >>>>>>> >> >>>>>> >> >>>>>> -- >> >>>>>> Dr. Suparna Mitra >> >>>>>> Wolfson Centre for Personalised Medicine >> >>>>>> Department of Molecular and Clinical Pharmacology >> >>>>>> Institute of Translational Medicine University of Liverpool >> >>>>>> Block A: Waterhouse Buildings, L69 3GL Liverpool >> >>>>>> >> >>>>>> Tel. +44 (0)151 795 5394, Internal ext: 55394 >> >>>>>> M: +44 (0) 7511387895 >> >>>>>> Email id: smitra@liverpool.ac.uk >> >>>>>> Alternative Email id: suparna.mitra.sm@gmail.com >> >>>>>> >> >>>>>> [[alternative HTML version deleted]] >> >>>>>> >> >>>>>> ______________________________**_________________ >> >>>>>> Bioconductor mailing list >> >>>>>> Bioconductor@r-project.org >> >>>>>> https://stat.ethz.ch/mailman/**listinfo/bioconductor< >> https://stat.ethz.ch/mailman/listinfo/bioconductor> >> >>>>>> Search the archives: >> >>>>>> >> http://news.gmane.org/gmane.**science.biology.informatics.**conductor< >> http://news.gmane.org/gmane.science.biology.informatics.conductor> >> >>>>>> >> >>>>>> [[alternative HTML version deleted]] >> >>>>> >> >>>>> ______________________________**_________________ >> >>>>> Bioconductor mailing list >> >>>>> Bioconductor@r-project.org >> >>>>> https://stat.ethz.ch/mailman/**listinfo/bioconductor< >> https://stat.ethz.ch/mailman/listinfo/bioconductor> >> >>>>> Search the archives: >> >>>>> >> http://news.gmane.org/gmane.**science.biology.informatics.**conductor< >> http://news.gmane.org/gmane.science.biology.informatics.conductor> >> >> >>>>> >> >>>>> >> >>>> >> >>>> -- >> >>>> Dr. Suparna Mitra >> >>>> Wolfson Centre for Personalised Medicine >> >>>> Department of Molecular and Clinical Pharmacology >> >>>> Institute of Translational Medicine University of Liverpool >> >>>> Block A: Waterhouse Buildings, L69 3GL Liverpool >> >>>> >> >>>> Tel. +44 (0)151 795 5394, Internal ext: 55394 >> >>>> M: +44 (0) 7511387895 >> >>>> Email id: smitra@liverpool.ac.uk >> >>>> Alternative Email id: suparna.mitra.sm@gmail.com >> >>>> >> >>>> ______________________________**_________________ >> >>>> Bioconductor mailing list >> >>>> Bioconductor@r-project.org >> >>>> https://stat.ethz.ch/mailman/**listinfo/bioconductor< >> https://stat.ethz.ch/mailman/listinfo/bioconductor> >> >>>> Search the archives: >> >>>> >> http://news.gmane.org/gmane.**science.biology.informatics.**conductor< >> http://news.gmane.org/gmane.science.biology.informatics.conductor> >> >>>> >> >>>> [[alternative HTML version deleted]] >> >>> >> >>> ______________________________**_________________ >> >>> Bioconductor mailing list >> >>> Bioconductor@r-project.org >> >>> https://stat.ethz.ch/mailman/**listinfo/bioconductor< >> https://stat.ethz.ch/mailman/listinfo/bioconductor> >> >>> Search the archives: http://news.gmane.org/gmane.** >> >>> science.biology.informatics.**conductor< >> 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 >> >> >> >> >> > >> > >> > -- >> > Dr. Suparna Mitra >> > Wolfson Centre for Personalised Medicine >> > Department of Molecular and Clinical Pharmacology >> > Institute of Translational Medicine University of Liverpool >> > Block A: Waterhouse Buildings, L69 3GL Liverpool >> > >> > Tel. +44 (0)151 795 5394, Internal ext: 55394 >> > M: +44 (0) 7511387895 >> > Email id: smitra@liverpool.ac.uk >> > Alternative Email id: suparna.mitra.sm@gmail.com >> > >> > >> >> >> -- >> Dr. Suparna Mitra >> Wolfson Centre for Personalised Medicine >> Department of Molecular and Clinical Pharmacology >> Institute of Translational Medicine University of Liverpool >> Block A: Waterhouse Buildings, L69 3GL Liverpool >> >> Tel. +44 (0)151 795 5394, Internal ext: 55394 >> M: +44 (0) 7511387895 >> Email id: smitra@liverpool.ac.uk >> Alternative Email id: suparna.mitra.sm@gmail.com >> >> >> >> >> -- >> Dr. Suparna Mitra >> Wolfson Centre for Personalised Medicine >> Department of Molecular and Clinical Pharmacology >> Institute of Translational Medicine University of Liverpool >> Block A: Waterhouse Buildings, L69 3GL Liverpool >> >> Tel. +44 (0)151 795 5394, Internal ext: 55394 >> M: +44 (0) 7511387895 >> Email id: smitra@liverpool.ac.uk >> Alternative Email id: suparna.mitra.sm@gmail.com >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > -- Dr. Suparna Mitra Wolfson Centre for Personalised Medicine Department of Molecular and Clinical Pharmacology Institute of Translational Medicine University of Liverpool Block A: Waterhouse Buildings, L69 3GL Liverpool Tel. +44 (0)151 795 5394, Internal ext: 55394 M: +44 (0) 7511387895 Email id: smitra@liverpool.ac.uk Alternative Email id: suparna.mitra.sm@gmail.com [[alternative HTML version deleted]]