Dear list. I am looking at a dataset comprised of Affy images from disease- affected tissue samples that I am trying to cluster. The problem is that we have 2+ biopsies per study subject, and I am not sure how to best account for their dependency. In contrast to cancer samples, these biopsies differ to a certain extent in their disease severity. I first tried to just cluster all available biopsies using ConsensusClusterPlus. However, this produced clusters of biopsies according to their disease severity - often with different samples from the same patient assigned to different clusters - and that´s not what I want. I am trying to identify different classes between subjects, not biopsies. For the diff exp analyses, we dealt with this issue by adding the patient as a random effect to the model. Could I do something similar using model-based clustering, perhaps also adding a variable for disease severity? As an alternative, I have explored aggregating all available samples per subject into one expression profile, and cluster the pattients using these aggregates. I am, however, not convinced that this is right, since this approach creates 'artificial' data. Does anyone have an idea? Many thanks, Moritz [[alternative HTML version deleted]]