A sparse integrative cluster analysis for understanding soybean phenotypes

Soybean is one of the most important crops for food, feed and bio-energy world-wide. The study of soybean phenotypic variation at different geographical locations can help the understanding of soybean domestication, population structure of soybean, and the conservation of soybean biodiversity. We investigate if soybean varieties can be identified that they differ from other varieties on multiple traits even when growing at different geographical locations. When a collection of traits are observed for the same soybean type at different locations (different views), joint analysis of the multiple-view data is required in order to identify the same soybean clusters based on data from different locations. We employ a new multi-view singular value decomposition approach that simultaneously decomposes the data matrix gathered at each location into sparse singular vectors. This approach is able to group soybean samples consistently across the different locations and simultaneously identify the phenotypes at each location on which the soybean samples within a cluster are the most similar. Comparison with several latest multi-view co-clustering methods demonstrates the superior performance of the proposed approach.