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Random projection for fast and efficient multivariate correlation analysis of high-dimensional data: A new approach

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons37454

Grellmann,  Claudia
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Germany;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons19887

Neumann,  Jane
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Germany;
Collaborative Research Center Obesity Mechanisms, Institute of Biochemistry, University of Leipzig, Germany;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons19554

Bitzer,  Sebastian
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Psychology, TU Dresden, Germany;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons20065

Villringer,  Arno
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Germany;
Clinic for Cognitive Neurology, University of Leipzig, Germany;
Berlin School of Mind and Brain, Humboldt University Berlin, Germany;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons19734

Horstmann,  Annette
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Germany;
Collaborative Research Center Obesity Mechanisms, Institute of Biochemistry, University of Leipzig, Germany;

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Zitation

Grellmann, C., Neumann, J., Bitzer, S., Kovacs, P., Tönjes, A., Westlye, L. T., et al. (2016). Random projection for fast and efficient multivariate correlation analysis of high-dimensional data: A new approach. Frontiers in Genetics, 7: 102. doi:10.3389/fgene.2016.00102.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002A-6393-F
Zusammenfassung
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.