Data integration via analysis of subspaces (DIVAS)

Jack Prothero, Meilei Jiang, Jan Hannig, Quoc Tran-Dinh, Andrew Ackerman and J. S. Marron ()
Additional contact information
Jack Prothero: National Institute of Standards and Technology
Meilei Jiang: Menlo Park
Jan Hannig: UNC-Chapel Hill: The University of North Carolina at Chapel Hill
Quoc Tran-Dinh: UNC-Chapel Hill: The University of North Carolina at Chapel Hill
Andrew Ackerman: UNC-Chapel Hill: The University of North Carolina at Chapel Hill
J. S. Marron: UNC-Chapel Hill: The University of North Carolina at Chapel Hill

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2024, vol. 33, issue 3, No 1, 633-674

Abstract: Abstract Modern data collection in many data paradigms, including bioinformatics, often incorporates multiple traits derived from different data types (i.e., platforms). We call this data multi-block, multi-view, or multi-omics data. The emergent field of data integration develops and applies new methods for studying multi-block data and identifying how different data types relate and differ. One major frontier in contemporary data integration research is methodology that can identify partially shared structure between sub-collections of data types. This work presents a new approach: Data Integration Via Analysis of Subspaces (DIVAS). DIVAS combines new insights in angular subspace perturbation theory with recent developments in matrix signal processing and convex–concave optimization into one algorithm for exploring partially shared structure. Based on principal angles between subspaces, DIVAS provides built-in inference on the results of the analysis, and is effective even in high-dimension-low-sample-size (HDLSS) situations.

Keywords: Data integration; Matrix decomposition; Rotational bootstrap; Principal angle analysis; 62H20 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11749-024-00923-z

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