Hi, I am working with time-series mice serum lipidomics data, and would like to make sure I have the right design for my purpose. The experiments like: Using 5 different concentrations A in the whole exposure experiment, only one intra-tracheal instillation exposure was performed on mice, and the mice were sacrificed to collect serum samples at 2、4、6 and 8 days respectively after exposure. I read some articles about soft clustering of analysis of time series data. Many articles have been written about genes, and I wanted to see if the mouse serum data could be processed in this way.The data of different mice in the same group was vary greatly, whether it is reasonable to calculate the average of 8 parallel samples in the same group directly.(some papers point out that Note that the clustering is based soley on the exprs matrix and no information is used from the phenoData. In particular, the ordering of samples (arrays) is the same as the ordering of the columns in the exprs matrix. Also, replicated arrays in the exprs matrix are treated as independent by the mfuzz function i.e. They should be averagered prior to clustering or placed into different distinct ExpressionSet objects.) thank you so much!