  In this paper, we first reviewed several biclustering methods that are used
to identify the most significant clusters in gene expression data. Here we
mainly focused on the SSVD(sparse SVD) method and tried a new sparse penalty
named "Prenet penalty" which has been used only in factor analysis to gain
sparsity. Then in the simulation study, we tried different types of generated
datasets (with different sparsity and dimension) and tried 1-layer
approximation then for k-layers which shows the mixed Prenet penalty is very
effective for non-overlapped data. Finally, we used some real gene expression
data to show the behavior of our methods.
