I have done this in the past, although I did not have much faith in it, maybe because of the way I create the design matrix. For example I do it this way: Treat <- factor(paste(targets$Condition,targets$Time,sep=".")) design <- model.matrix(~0+Treat) colnames(design) <- levels(Treat) colnames(design) [1] "Case.Time2" "Case.Time1" "Control.Time2" "Control.Time1" #introduce continuous variable, add to design CES <- factor(targets$CES) design2 <- cbind(design, CES) colnames(design2) [1] "Case.Time2" "Case.Time1" "Control.Time2" "Control.Time1" "CES" I go on to duplicate correlation and lmfit then make these contrasts to substract away the effect of the continuous variable from our observations. cm <- makeContrasts( CaseEffect = Case.Time2-Case.Time1, ControlEffect = Control.Time2-Control.Time1, ContrastEffect = (Case.Time2-Case.Time1)-(Control.Time2-Control.Time1), CaseEffectNoCov = (Case.Time2-Case.Time1)-(CES), ControlEffectNoCov = (Case.Time2-Case.Time1)-(CES), ContrastEffectNoCov = (Case.Time2-Case.Time1)-(CES) - (Case.Time2-Time1)-(CES), levels=designnew) etc... Our results are 'different' so it would be useful to have another opinion regarding this set-up. Michael On Tue, Oct 22, 2013 at 11:03 PM, Ryan wrote: > Including continuous covariates in design matrices is R is just as easy as > including categorical ones. Instead of creating a column for each degree of > freedom in the categorical variable, you just end up with a single column > that simply contains the values of the continuous variable. Try using the > model.matrix function with a combination of your categorical variables and > continuous ones to see what it does. > > -Ryan > > > On Tue Oct 22 14:36:38 2013, Richard Friedman wrote: > >> >> On Oct 22, 2013, at 5:30 PM, Michael Breen wrote: >> >> Hi all, >>> >>> Our lab analyzes gene-expression from microarray and RNAseq platforms. >>> Currently, I am looking for a package to test differential expression >>> (DE) >>> while considering continuous variables that may alter gene-expression >>> profiles. In other words, an ANCOVA type tool. I am quite familiar with >>> Limma (ANOVA) but including continuous variables is not very well >>> described. >>> >>> Specifically, we have a project were two groups can be modeled over the >>> same 2 time points. One group starts healthy and ends in a disease state. >>> The other group starts healthy and remains healthy. >>> >>> We are interested in identifying genes uniquely responding within one >>> group >>> and not in the other. Thus, we have implemented a longitudinal contrast >>> with linear modeling through Limma. However, we are also interested in >>> adding one or two continuous variables (tumor size, time spent >>> meditating, >>> the amount of drinks one consumes etc..) to check if gene expression >>> differences or similarities may be due to these factors instead of due to >>> belonging to a certain class. Limma seems to test categorical variables, >>> but I don't think it is capable of either correlating gene-expression to >>> continuous variables. >>> >>> If not, can someone recommend a tool which may be appropriate for such a >>> situation? >>> >>> Yours, >>> >>> Michael >>> >>> [[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 >>> >> Dear Michael and list, >> >> I think that you write the design and contrast matrices >> exactly as you would for an ANCOVA in R only you do the >> fit and Bayesian correction in Limma. >> >> Perhaps someone who has had experience doing this >> kind of analysis can comment. >> >> Best wishes, >> Rich >> >> Richard A. Friedman, PhD >> Associate Research Scientist, >> Biomedical Informatics Shared Resource >> Herbert Irving Comprehensive Cancer Center (HICCC) >> Lecturer, >> Department of Biomedical Informatics (DBMI) >> Educational Coordinator, >> Center for Computational Biology and Bioinformatics (C2B2)/ >> National Center for Multiscale Analysis of Genomic Networks (MAGNet)/ >> Columbia Department of Systems Biology >> Room 824 >> Irving Cancer Research Center >> Columbia University >> 1130 St. Nicholas Ave >> New York, NY 10032 >> (212)851-4765 (voice) >> friedman@cancercenter.**columbia.edu >> http://cancercenter.columbia.**edu/~friedman/ >> >> In memoriam, Frederik Pohl >> >> ______________________________**_________________ >> 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 >> > [[alternative HTML version deleted]]