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

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-6393-F Version Permalink: http://hdl.handle.net/21.11116/0000-0003-1EC8-B
Genre: Journal Article

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 Creators:
Grellmann, Claudia1, 2, Author              
Neumann, Jane1, 2, 3, Author              
Bitzer, Sebastian1, 4, Author              
Kovacs, Peter2, Author
Tönjes, Anke5, Author
Westlye, Lars Tjelta6, 7, Author
Andreassen, Ole Andreas6, Author
Stumvoll, Michael2, 5, Author
Villringer, Arno1, 2, 8, 9, Author              
Horstmann, Annette1, 2, 3, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Germany, ou_persistent22              
3Collaborative Research Center Obesity Mechanisms, Institute of Biochemistry, University of Leipzig, Germany, ou_persistent22              
4Department of Psychology, TU Dresden, Germany, ou_persistent22              
5Clinic for Endocrinology and Nephrology, University Hospital Leipzig, Germany, ou_persistent22              
6NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Norway, ou_persistent22              
7Department of Psychology, University of Oslo, Norway, ou_persistent22              
8Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              
9Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              

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Free keywords: Multivariate multimodal data integration; Partial Least Squares Correlation; Dimensionality reduction; Genome-wide association; Genetic neuroimaging
 Abstract: 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.

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Language(s): eng - English
 Dates: 2016-01-192016-05-232016-06-07
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.3389/fgene.2016.00102
PMID: 27375677
PMC: PMC4894907
Other: eCollection 2016
 Degree: -

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Title: Frontiers in Genetics
Source Genre: Journal
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Publ. Info: Lausanne : Frontiers Media
Pages: - Volume / Issue: 7 Sequence Number: 102 Start / End Page: - Identifier: Other: 1664-8021
CoNE: https://pure.mpg.de/cone/journals/resource/1664-8021