Integrative and sparse singular value decomposition method for biclustering analysis in multi-sources dataset

Multi-sources dataset analysis has been researched for a few years and it aims at improving the potential performance for discoveries by combining data from different sources. Meanwhile, biclustering analysis, widely applied for biology, chemistry, and so on, is a powerful tool that allows the clustering of rows and columns simultaneously. This paper proposes an integrative and sparse singular value decomposition (ISSVD) method for biclustering analysis in multi-sources datasets, which seeks the common left and individual right singular vectors, and finds the interpretable row-column associations. Simulation studies demonstrate that our model can handle with the different scales between the multi-source data, and obtain better performance than other popular biclustering methods. Two real-world data examples are also presented.

Automated summary

The research proposes an ISSVD method for biclustering analysis in multi-sources datasets, which can handle data of different scales and achieve better performance, suitable for applications in biology, chemistry, and other fields.