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Question: Questions about DecideTest and Several groups
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gravatar for Elodie Chapeaublanc
5.2 years ago by
Elodie Chapeaublanc70 wrote:
Hi, I have got question about several comparisons and results : I have got 4 groups and I want to make several comparisons between my groups (gp1 vs gp2, gp1 vs gp3, ...). I tested decideTest in order to have easy resume output but I don't understand why in a decideTest I obtain results not coording topTable output. fit <- lmFit(data_i,mat_group) comparaison <- makeContrasts(N-Ta,N-T1,N-T2,Ta-T1,Ta-T2,T1-T2,levels=mat_group) fit2 <- contrasts.fit(fit,comparaison) fit2 <- eBayes(fit2) top_table <- topTable(fit2,coef=1,number=Inf,adjust="BH") rownames(top_table)<- top_table[,1] ID logFC AveExpr t P.Value adj.P.Val B IGF1R 0.111104650376149 6.73735754086491 0.58031824032662 0.563347919004554 0.89178337590619 -5.80922017018552 IGF2R -0.418361001891785 7.30781700757716 -1.89591269242418 0.0616198608368053 0.566570014038297 -4.30516165436281 INSR -0.104189972020608 5.88324657993507 -0.705621827997018 0.482494171926321 0.858963516232972 -5.73329527131923 IGF1 0.0164024330700565 5.32379121221856 0.0580159806673495 0.953882062264167 0.993280048337664 -5.96683923009462 IGF2 -2.03692409239562 7.64672458332555 -2.78687079989285 0.00665914505810975 0.255548599553939 -2.46861977043762 INS 1.65128828436572 6.65133927223272 2.04732749311482 0.0439423770026206 0.508760608526091 -4.03635411033303 IGFBP1 0.111163236117028 3.7608601464086 0.990350899711699 0.325019419965225 0.79173927409954 -5.50673419683211 IGFBP2 1.4347995251779 7.54793353816003 3.06683251663792 0.00295982828163497 0.16458256036792 -1.77287225505653 IGFBP3 -0.875527979281101 10.4136969590099 -1.72885747148627 0.0877319854057766 0.615890064783784 -4.57990448008225 IGFBP4 -1.06382888423619 10.6781825579171 -3.59228487330552 0.000567234252702273 0.0858624589183034 -0.332444870299646 IGFBP5 0.0461190965231344 6.75488133496068 0.100191694176698 0.920445389165695 0.988013212218042 -5.96367782236715 IGFBP6 -0.333902382597201 7.18472204506767 -0.793175371995814 0.430046033425277 0.842849113060914 -5.67158345971083 IGFBP7 -2.06010861248481 9.26037314643127 -6.8481256400995 1.44348094893832e-09 2.94975331915545e-06 11.1853303363173 IGF2BP3 -0.0148502449143448 4.54849676079719 -0.031861021350583 0.974663058942624 0.997732729342142 -5.96795306155805 results <- decideTests(fit2,method="nestedF",adjust="BH",p.value=0.05,lfc=0) results[intersect(liste_genes_interest,rownames(results)),] N - Ta N - T1 N - T2 Ta - T1 Ta - T2 T1 - T2 IGF1R 0 0 1 1 1 0 IGF2R 0 -1 0 0 0 1 INSR 0 0 0 0 0 0 IGF1 0 0 0 0 0 0 IGF2 -1 0 0 0 1 0 INS 0 0 0 0 0 0 IGFBP1 0 0 0 0 0 0 IGFBP2 1 1 1 0 1 1 IGFBP3 0 0 0 0 0 0 IGFBP4 -1 0 0 1 1 0 IGFBP5 0 0 0 0 0 0 IGFBP6 0 0 0 0 0 0 IGFBP7 -1 -1 -1 -1 -1 0 IGF2BP3 0 0 0 0 -1 -1 -- -- Elodie Chapeaublanc IE Bioinformatique Équipe Oncologie Moléculaire Institut Curie - UMR 144 - CNRS 26 rue d'Ulm - 75248 Paris Cedex 05 Tel: +33 1 56 24 63 57 Email: elodie.chapeaublanc@curie.fr [[alternative HTML version deleted]]
ADD COMMENTlink modified 5.2 years ago by James W. MacDonald45k • written 5.2 years ago by Elodie Chapeaublanc70
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gravatar for James W. MacDonald
5.2 years ago by
United States
James W. MacDonald45k wrote:
Hi Elodie, On 9/11/2012 4:16 AM, Elodie Chapeaublanc wrote: > Hi, > > I have got question about several comparisons and results : > I have got 4 groups and I want to make several comparisons between my > groups (gp1 vs gp2, gp1 vs gp3, ...). I tested decideTest in order to > have easy resume output but I don't understand why in a decideTest I > obtain results not coording topTable output. The simple answer is because they are two different functions with different outputs (and given that you are using the 'nestedF' argument for decideTests(), there is a different assessment of significance as well). Have you looked at the help pages for these functions? That should clear up any confusion. For now, note that topTable() outputs the Table of Top genes for a given contrast (or alternatively, the top genes by F-statistic if you don't specify a coefficient). In contrast, decideTests() outputs a matrix of 1,0,-1 that indicate significance and direction for each contrast, for each gene. This can be used for many things, but the most common I suppose is to create Venn diagrams. Best, Jim > > > fit<- lmFit(data_i,mat_group) > > comparaison<- > makeContrasts(N-Ta,N-T1,N-T2,Ta-T1,Ta-T2,T1-T2,levels=mat_group) > fit2<- contrasts.fit(fit,comparaison) > fit2<- eBayes(fit2) > top_table<- topTable(fit2,coef=1,number=Inf,adjust="BH") > rownames(top_table)<- top_table[,1] > > > > > ID logFC AveExpr t P.Value adj.P.Val B > IGF1R 0.111104650376149 6.73735754086491 0.58031824032662 > 0.563347919004554 0.89178337590619 -5.80922017018552 > IGF2R -0.418361001891785 7.30781700757716 -1.89591269242418 > 0.0616198608368053 0.566570014038297 -4.30516165436281 > INSR -0.104189972020608 5.88324657993507 -0.705621827997018 > 0.482494171926321 0.858963516232972 -5.73329527131923 > IGF1 0.0164024330700565 5.32379121221856 0.0580159806673495 > 0.953882062264167 0.993280048337664 -5.96683923009462 > IGF2 -2.03692409239562 7.64672458332555 -2.78687079989285 > 0.00665914505810975 0.255548599553939 -2.46861977043762 > INS 1.65128828436572 6.65133927223272 2.04732749311482 > 0.0439423770026206 0.508760608526091 -4.03635411033303 > IGFBP1 0.111163236117028 3.7608601464086 0.990350899711699 > 0.325019419965225 0.79173927409954 -5.50673419683211 > IGFBP2 1.4347995251779 7.54793353816003 3.06683251663792 > 0.00295982828163497 0.16458256036792 -1.77287225505653 > IGFBP3 -0.875527979281101 10.4136969590099 -1.72885747148627 > 0.0877319854057766 0.615890064783784 -4.57990448008225 > IGFBP4 -1.06382888423619 10.6781825579171 -3.59228487330552 > 0.000567234252702273 0.0858624589183034 -0.332444870299646 > IGFBP5 0.0461190965231344 6.75488133496068 0.100191694176698 > 0.920445389165695 0.988013212218042 -5.96367782236715 > IGFBP6 -0.333902382597201 7.18472204506767 -0.793175371995814 > 0.430046033425277 0.842849113060914 -5.67158345971083 > IGFBP7 -2.06010861248481 9.26037314643127 -6.8481256400995 > 1.44348094893832e-09 2.94975331915545e-06 11.1853303363173 > IGF2BP3 -0.0148502449143448 4.54849676079719 -0.031861021350583 > 0.974663058942624 0.997732729342142 -5.96795306155805 > > > > > results<- > decideTests(fit2,method="nestedF",adjust="BH",p.value=0.05,lfc=0) > results[intersect(liste_genes_interest,rownames(results)),] > > N - Ta N - T1 N - T2 Ta - T1 Ta - T2 T1 - T2 > IGF1R 0 0 1 1 1 0 > IGF2R 0 -1 0 0 0 1 > INSR 0 0 0 0 0 0 > IGF1 0 0 0 0 0 0 > IGF2 -1 0 0 0 1 0 > INS 0 0 0 0 0 0 > IGFBP1 0 0 0 0 0 0 > IGFBP2 1 1 1 0 1 1 > IGFBP3 0 0 0 0 0 0 > IGFBP4 -1 0 0 1 1 0 > IGFBP5 0 0 0 0 0 0 > IGFBP6 0 0 0 0 0 0 > IGFBP7 -1 -1 -1 -1 -1 0 > IGF2BP3 0 0 0 0 -1 -1 > > > > > _______________________________________________ > 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
ADD COMMENTlink written 5.2 years ago by James W. MacDonald45k
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