Hi there, I have a problem with my hierarchical clustering method and I appreciate if anyone could help me in advance. Let me start from the first step, in order to identify differentially expressed genes in some microarray studies (each study consist of 3 individual dataset, collectively I have 15 dataset) I use limma package from bioconductor, R. Then I filtered out those genes with adj. P-value less than 0.05. After that, I extracted a set of genes which involved in the cell cycle for example. Finally, this set of genes with there expression base on log fold change were used for hierarchical clustering. As I read before for log-transformed data Euclidean distance measurement method with complete linkage is the best for my data but the problem is when I clustered 15 dataset, surprisingly data from the same study stand close together in one cluster. What can I do for this mistaken view? Would it possible to use only one control for all treatment data from a different study in R? Or another approach would be taken?
Many thanks in advance