Biclustering Methods via Sparse Penalty
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Group-sparse SVD Models and Their Applications in Biological Data
Sparse Singular Value Decomposition (SVD) models have been proposed for biclustering high dimensional gene expression data to identify block patterns with similar expressions. However, these models do not take into accou…
Variable SelectionBiclustering Using Message Passing
Biclustering is the analog of clustering on a bipartite graph. Existent methods infer biclusters through local search strategies that find one cluster at a time; a common technique is to update the row memberships based …
Clusteringglobal-optimizationSparse Convex Biclustering
Biclustering is an essential unsupervised machine learning technique for simultaneously clustering rows and columns of a data matrix, with widespread applications in genomics, transcriptomics, and other high-dimensional …
Differential gene co-expression networks via Bayesian biclustering models
Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relation…
L0-norm Sparse Graph-regularized SVD for Biclustering
Learning the "blocking" structure is a central challenge for high dimensional data (e.g., gene expression data). Recently, a sparse singular value decomposition (SVD) has been used as a biclustering tool to achieve this …
Blocking