Hello, I have a question regarding the correct use of glm for logistic regression. I am trying to analyse microarray data of 30 cases with 600 affymetrix probes. The 30 cases have clinical parameters on dead/alive and drug treatment/no treatment. I enclose subset example of how the table is for 6 affymetrix probes on 17 cases : probe 1 probe 2 probe 3 probe 4 probe 5 probe 6 Dead/Alive Drug/NoDrug 5.9 6.6 6.5 6.8 5.2 7.1 0 1 8.6 7.9 7.0 7.1 6.9 7.0 1 0 4.9 5.5 4.6 4.3 4.4 4.3 1 1 4.3 5.5 4.9 5.9 6.8 4.2 0 1 7.1 5.3 6.1 6.7 7.4 7.0 0 1 9.0 10.8 4.5 4.5 5.5 5.7 0 0 3.2 2.3 2.8 2.3 5.3 2.3 0 1 3.6 2.7 2.7 2.9 3.2 2.9 1 1 6.4 4.0 2.4 5.6 2.5 3.0 0 0 5.4 2.3 2.3 3.2 2.3 2.3 1 0 3.3 3.7 3.0 3.6 3.7 4.9 0 1 5.1 4.5 3.2 3.6 5.5 3.8 0 0 My question is can I use glm to see whether dead/alive or drug/nodrug is significant in the model in multivariate way. Meaning can I take probes 1 to 6 and compare those as matrix against Dead/Alive and Drug/NoDrug in multivariate analysis? so in the above example of the data.frame is called glmana : dead/alive <- glmana[,7] drug/nodrug <- glmanap[,8] summary(glm(glmana[,1:6] ~ dead/alive + drug/nodrug)) Is this correct and if so could I put all 600 probes and compare that as a matrix against dead/alive and drug/nodrug, if not is there anyway to determine the maximum number of probes to use in the model? Many thanks, Ruppert [[alternative HTML version deleted]]