Dear All, I have a small problem that is silimar to the case below, and would really appreciate if someone could give me some ideas. I am doing a pairwise analysis for some samples (say 8 pairs, A1, A2, B1, B2, C1, C2,...H1, H2) using linear models, to find out gene expression difference between before treatment and after treatment. But if the length of the treatment is different for every pair, how can I include the treatment length in my analysis? If one objective of my study is to see if the difference in gene expression correlates with the treatment length, will adjusting the expression differences relative to the highest treatment length introduce a bias? Thanks in advance, Hajja [[alternative HTML version deleted]]

Dear Hajja, Your targets frame (in limma) needs to contain three columns: Pair (taking values A, A, B, B, C, C etc), Treatment, before or after (taking values 1,2,1,2 etc) and Treatment length. Pair <- factor(Pair) Treatment <- factor(Treatment) design <- model.matrix(~Pair+Treatment+Treatment:Length) The last coefficient tests when the differences in expression are correlated with treatment length. Best wishes Gordon > Date: Tue, 12 Jul 2011 03:57:12 -0700 (PDT) > From: khadeeja ismail <hajjja at="" yahoo.com=""> > To: bioconductor at r-project.org > Subject: [BioC] Linear models? > Message-ID: > > Dear All, > ? > I have a small problem that is silimar to the case below, and would really appreciate if someone could give me some ideas. > ? > I am doing a?pairwise analysis for some samples (say 8 pairs, A1, A2, B1, B2, C1, C2,...H1, H2) using linear models, to find out gene?expression difference between?before treatment and after treatment. But if the length of the treatment is different for every pair, how can I include?the treatment length?in my analysis? > ? > If one objective of my study is to see if the difference in gene expression correlates with the treatment length, will adjusting the expression differences relative to the highest treatment length introduce a bias? > ? > Thanks in advance, > Hajja

Gordon K Smyth <smyth at="" ...=""> writes: > > Dear Hajja, > > Your targets frame (in limma) needs to contain three columns: Pair (taking > values A, A, B, B, C, C etc), Treatment, before or after (taking values > 1,2,1,2 etc) and Treatment length. Thanks Gordon, I was actually working on methylation data from 11 pairs. I followed your instructions and created the targets file and design matrix but then it gives me a coefficient for every pair in my data. #####My targets file:###### Pair Tr TLength "T1 1 27.6 "T1 2 21.9 "T2 2 22.2 "T2 1 26.6 ... "T11 2 24.5 "T11 1 28.4 Pair <- factor(Targets$Pair) Treatment <- factor(Targets$Tr) design <- model.matrix(~Pair + Treatment + Targets$TLength) ##OUTPUT############################################################## #### ID X.Intercept. PairT1 PairT2 ... 43676 cg04570604 -4.944408 -0.09987055 0.2324025 ... 49660 cg05227111 3.895301 -0.17053184 -0.2422336 ... ... PairT11 TreatmentL Targets.TLength 43676 -0.23353534 -0.1040837 -0.03335663 49660 0.16514871 0.3153623 0.07148090 ... F P.Value adj.P.Val 43676 1232.021 1.639072e-33 3.387964e-29 49660 1175.197 3.069305e-33 3.387964e-29 ... ###################################################################### ###### Initially I did a one sample t-test on the differences between the pairs (done by hand and submitted to limma), ignoring the differences in treatment length. I'm wondering if I can do the same but taking into account the treatment length. My current objective is to find out the probes that show significant differences in methylation between pairs. Thanks once again for your reply. Best regards, Hajja > > Pair <- factor(Pair) > Treatment <- factor(Treatment) > design <- model.matrix(~Pair+Treatment+Treatment:Length) > > The last coefficient tests when the differences in expression are > correlated with treatment length. > > Best wishes > Gordon > > > Date: Tue, 12 Jul 2011 03:57:12 -0700 (PDT) > > From: khadeeja ismail <hajjja at="" ...=""> > > To: bioconductor at ... > > Subject: [BioC] Linear models? > > Message-ID: > > > > Dear All, > > ? > > I have a small problem that is silimar to the case below, and would really appreciate if someone could give > me some ideas. > > ? > > I am doing a?pairwise analysis for some samples (say 8 pairs, A1, A2, B1, B2, C1, C2,...H1, H2) using linear > models, to find out gene?expression difference between?before treatment and after treatment. But if > the length of the treatment is different for every pair, how can I include?the treatment length?in my analysis? > > ? > > If one objective of my study is to see if the difference in gene expression correlates with the treatment > length, will adjusting the expression differences relative to the highest treatment length introduce a bias? > > ? > > Thanks in advance, > > Hajja > > > _______________________________________________ > Bioconductor mailing list > Bioconductor at ... > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor