Hi all, I am trying to use Limma for multiple regression. The design matrix is like this : EffectA EffectB 1 25 0 35 0 28 1 32 The Effect A is categorical, The Effect B is continuous. I can use contrast.matrix to find the differential expressed genes between 0 and 1 (EffectA). But I don't know how can I find genes correlated with EffectB. Thanks, Jack [[alternative HTML version deleted]]

Hi Jack I would fit my design matrix like this: > design<-model.matrix(~A+B) and because there are only two levels for A it is treated as a factor, but B is treated as a continuous variable. > design (Intercept) A B 1 1 1 25 2 1 0 35 3 1 0 28 4 1 1 32 Then proceed as usual: > fit<-lmFit(data,design) > fit<-eBayes(fit) > summary(decideTests(fit)) # For significant genes for effect A: > topTable(fit,coef=2) # For significant genes for effect B: > topTable(fit,coef=3) Cheers, Belinda -----Original Message----- From: bioconductor-bounces@r-project.org [mailto:bioconductor-bounces at r-project.org] On Behalf Of Yao Chen Sent: Friday, 13 July 2012 7:08 AM To: bioconductor at r-project.org Subject: [BioC] [LIMMA]lmFit with continuous predictor Hi all, I am trying to use Limma for multiple regression. The design matrix is like this : EffectA EffectB 1 25 0 35 0 28 1 32 The Effect A is categorical, The Effect B is continuous. I can use contrast.matrix to find the differential expressed genes between 0 and 1 (EffectA). But I don't know how can I find genes correlated with EffectB. Thanks, Jack [[alternative HTML version deleted]] _______________________________________________ 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 ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}