Hi, I am trying to set up my linear model design matrix and contrast matrix and I am having some difficulty. I am using Agilent's one-color platform, so I have a matrix of normalized intensity values as my input object for lmfit. I have 3 populations, 12 samples from each population, and a treatment that was conducted on 6 individuals from each population (36 arrays total). I am interested in population differences, treatment differences, and any population x treatment differences. I also have individuals of both sexes among the samples, so I would like to correct for any sex affects in the model. I am new to linear models, but I thought I understood how to do this until I decided to add the sex information to the model. I would greatly appreciate any suggestions. Thanks, Erica -- <<<<<<<<<< Erica Leder Division of Genetics and Physiology Dept of Biology (Vesilinnantie 5) 20014 University of Turku Finland mobile +358 509313035 tel. +358 23337086 fax. +358 23336680 erica.leder at utu.fi

Hi Erica, How about something like this: > pop <- factor(rep(1:3, each=12)) > trt <- factor(rep(1:2, each=6, times=3)) > sex <- factor(sample(c("male","female"), 36, TRUE)) > design <- model.matrix(~0+pop+trt+pop:trt+sex) Then the population comps are computed using a contrasts matrix, the treatment comp is captured by the trt2 parameter, and the intersections are the pop2:trt2 and pop3:trt2 parameters, all of which are adjusted for sex. Best, Jim Erica Leder wrote: > Hi, > > I am trying to set up my linear model design matrix and contrast matrix > and I am having some difficulty. I am using Agilent's one-color > platform, so I have a matrix of normalized intensity values as my input > object for lmfit. > > I have 3 populations, 12 samples from each population, and a treatment > that was conducted on 6 individuals from each population (36 arrays > total). I am interested in population differences, treatment > differences, and any population x treatment differences. I also have > individuals of both sexes among the samples, so I would like to correct > for any sex affects in the model. > I am new to linear models, but I thought I understood how to do this > until I decided to add the sex information to the model. I would > greatly appreciate any suggestions. > > Thanks, > > Erica > > > -- James W. MacDonald, M.S. Biostatistician Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623