Multi-view clustering by CPS-merge analysis with application to multimodal single-cell data.

ABSTRACT: Multi-view data can be generated from diverse sources, by different technologies, and in multiple modalities. In various fields, integrating information from multi-view data has pushed the frontier of discovery. In this paper, we develop a new approach for multi-view clustering, which overcomes the limitations of existing methods such as the need of pooling data across views, restrictions on the clustering algorithms allowed within each view, and the disregard for complementary information between views. Our new method, called CPS-merge analysis, merges clusters formed by the Cartesian product of single-view cluster labels, guided by the principle of maximizing clustering stability as evaluated by CPS analysis. In addition, we introduce measures to quantify the contribution of each view to

SUBMITTER: Zhang L 

PROVIDER: S-EPMC10138214 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature