As a spin-off from the Mini-Arctic Conference, Dr. Frans van der Kloet was visiting Nofima in December 2015. Frans is a Post-doc in the Biosystem Data Analysis group at the University of Amsterdam, working on integration and interpretation of -omics data.
During his stay he established a collaboration on the use of data fusion methods for separating common and distinct variation across datasets. Together With Dr. Ingrid Måge he will investigate the practical use and added value of methods such as DISCO, JIVE, OnPLS and PO-PCA.
Schouteden, M., Van Deun, K., Wilderjans, T. F., & Van Mechelen, I. (2014). Performing DISCO-SCA to search for distinctive and common information in linked data. Behavior Research Methods, 46(2), 576–87. doi:10.3758/s13428-013-0374-6
Van Deun, K., Smilde, a. K. K., Thorrez, L., Kiers, H. a. L. a L., & Van Mechelen, I. (2013). Identifying common and distinctive processes underlying multiset data. Chemometrics and Intelligent Laboratory Systems. doi:10.1016/j.chemolab.2013.07.005
Lock, E. F., Hoadley, K. a., Marron, J. S., & Nobel, A. B. (2013). Joint and individual variation explained (JIVE) for integrated analysis of multiple data types. Annals of Applied Statistics, 7(1), 523–542. doi:10.1214/12-AOAS597
Löfstedt, T., Hoffman, D., & Trygg, J. (2013). Global, local and unique decompositions in OnPLS for multiblock data analysis. Analytica Chimica Acta, 791(June 2012), 13–24. doi:10.1016/j.aca.2013.06.026
Måge, I., Menichelli, E., & Næs, T. (2012). Preference mapping by PO-PLS: Separating common and unique information in several data blocks. Food Quality and Preference, 24(1), 8–16.
Næs, T., Tomic, O., Afseth, N. K., Segtnan, V., & Måge, I. (2013). Multi-block regression based on combinations of orthogonalisation, PLS-regression and canonical correlation analysis. Chemometrics and Intelligent Laboratory Systems, 124, 32–42.