Dear Som, I certainly do not recommend Welch's t-test. Your limma analysis is already full adjusting for patient variability, and Welch's test has nothing to do with patient to patient variability anyway. Best wishes Gordon --------------------------------------------- Professor Gordon K Smyth, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Vic 3052, Australia. Tel: (03) 9345 2326, Fax (03) 9347 0852, http://www.statsci.org/smyth On Fri, 6 Jul 2012, somnath bandyopadhyay wrote: > > > > Hi > Gordon, > > Thanks for your suggestion. That helped a lot! > > > > I had one more question: if the patient to patient variability is too large, > would you recommend doing a Welch's t-test? Is there a way to do it in limma > using the same linear model (~patient + dis + dis:tx)? > > > > Thanks, > > Som. > > > >> Date: Wed, 4 Jul 2012 10:27:18 +1000 >> From: smyth at wehi.EDU.AU >> To: genome1976 at hotmail.com >> CC: bioconductor at r-project.org; maintainer at bioconductor.org >> Subject: Re: LIMMA paired T-test >> >> Your design matrix is not sufficient to answer questions 2 and 3. Your >> questions presume an interaction between treatment and disease, i.e., >> distinct effects for treatment for disease and healthy, whereas your model >> formula assumes no interaction. >> >> You need: >> >> design <- model.matrix(~patient + dis + dis:tx) >> >> Then last two coefficients answer questions 2 and 3. >> >> Gordon >> >> --------------------------------------------- >> Professor Gordon K Smyth, >> Bioinformatics Division, >> Walter and Eliza Hall Institute of Medical Research, >> 1G Royal Parade, Parkville, Vic 3052, Australia. >> http://www.wehi.edu.au >> http://www.statsci.org/smyth >> >> On Tue, 3 Jul 2012, somnath bandyopadhyay wrote: >> >>> >>> Hi Gordon and LIMMA users, >>> >>> I am sure this question has been answered before and I tried looking into the archives for some answer but did n't have any success there. >>> >>> My experimental design has diseased and healthy volunteers blood treated with a drug. I have gene expression data for both before and after treatment. So, I have disease, treatment and patient_ID (before vs. after treatment) as covariates. What I am interested in are as follows: >>> >>> 1. What genes change in untreated disease vs. untreated healthy volunteers? >>> 2. What genes change in treated disease vs. untreated disease blood samples? >>> 3. What genes change in treated healthy volunteers vs. untreated healthy volunteers blood samples? >>> >>> Design of the experiment: >>> design <- model.matrix(~ dis + tx + patient) >>> >>> Based on the above design I am able to answer question 1. I was >>> wondering how I would answer question 2 and 3 in a paired T -test (to >>> account for before vs. after treatment). Do I need to do some contrasts >>> because I have been trying to work off the lmfit. >>> >>> Any help would be greatly apreciated. >>> >>> Thanks, >>> Som. >>> >>> >>> >>> >>> >> >> ______________________________________________________________________ >> The information in this email is confidential and intended solely for the addressee. >> You must not disclose, forward, print or use it without the permission of the sender. >> ______________________________________________________________________ > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}