期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 211, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.chemolab.2021.104281
关键词
Multi-sources dataset; Sparse singular value decomposition; Biclustering analysis; Integrative analysis
类别
资金
- Innovation Program of Central South University [502221807]
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.
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.
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