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Rejoinder on: Data integration via analysis of subspaces (DIVAS)

Author

Listed:
  • Jack Prothero

    (National Institute of Standards and Technology)

  • Meilei Jiang

    (Meta)

  • 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)

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.

Suggested Citation

  • Jack Prothero & Meilei Jiang & Jan Hannig & Quoc Tran-Dinh & Andrew Ackerman & J. S. Marron, 2024. "Rejoinder on: Data integration via analysis of subspaces (DIVAS)," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(3), pages 693-696, September.
    • Handle: RePEc:spr:testjl:v:33:y:2024:i:3:d:10.1007_s11749-024-00950-w
    DOI: 10.1007/s11749-024-00950-w
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    References listed on IDEAS

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    1. N. Locantore & J. Marron & D. Simpson & N. Tripoli & J. Zhang & K. Cohen & Graciela Boente & Ricardo Fraiman & Babette Brumback & Christophe Croux & Jianqing Fan & Alois Kneip & John Marden & Daniel P, 1999. "Robust principal component analysis for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(1), pages 1-73, June.
    2. Yang, Xi & Hoadley, Katherine A. & Hannig, Jan & Marron, J.S., 2023. "Jackstraw inference for AJIVE data integration," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
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