Hi, I have generated some data using rt-pct; From 50 samples (divided into three groups healthy,chronic,disease) i have tested all samples for th expression of 50 genes. I want to find the most suitable combination of genes that will help to discriminate three groups healthy, chronic and disease. For that i have started by running an anova, followed by TukeyHSD test and have identified gene1 to 10 as differentially expressed in group disease from both chronic and healthy. I have evaluated the ROC plots for each of these genes and noticed that a three of those can help to better discriminate group chronic from disease. So the conclusion would be that gene1+gene2+gene3 are my best markers between group chronic and disease. Everything is ok until here but i want to statistically show that three genes are the best combination markers. Should i run individual LDA (between pair genes) or is there any multi-class LDA test that i could use to show that this is the best combination of markers to differentiate chronic from disease in ly set of 10 genes ? thanks for your help and tips david