TY - JOUR AU - Grellmann, Claudia AU - Neumann, Jane AU - Bitzer, Sebastian AU - Kovacs, Peter AU - Tönjes, Anke AU - Westlye, Lars T. AU - Andreassen, Ole A. AU - Stumvoll, Michael AU - Villringer, Arno AU - Horstmann, Annette PY - 2016 M3 - Original Research TI - Random Projection for Fast and Efficient Multivariate Correlation Analysis of High-Dimensional Data: A New Approach JO - Frontiers in Genetics UR - https://www.frontiersin.org/articles/10.3389/fgene.2016.00102 VL - 7 SN - 1664-8021 N2 - In recent years, the advent of great technological advances has produced a wealth of very high-dimensional data, and combining high-dimensional information from multiple sources is becoming increasingly important in an extending range of scientific disciplines. Partial Least Squares Correlation (PLSC) is a frequently used method for multivariate multimodal data integration. It is, however, computationally expensive in applications involving large numbers of variables, as required, for example, in genetic neuroimaging. To handle high-dimensional problems, dimension reduction might be implemented as pre-processing step. We propose a new approach that incorporates Random Projection (RP) for dimensionality reduction into PLSC to efficiently solve high-dimensional multimodal problems like genotype-phenotype associations. We name our new method PLSC-RP. Using simulated and experimental data sets containing whole genome SNP measures as genotypes and whole brain neuroimaging measures as phenotypes, we demonstrate that PLSC-RP is drastically faster than traditional PLSC while providing statistically equivalent results. We also provide evidence that dimensionality reduction using RP is data type independent. Therefore, PLSC-RP opens up a wide range of possible applications. It can be used for any integrative analysis that combines information from multiple sources. ER -