English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

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

MPS-Authors
/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;

/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;

/persons/resource/persons19554

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

/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;

/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;

External Resource

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4894907/pdf/fgene-07-00102.pdf
(Any fulltext)

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

Grellmann_2016.pdf
(Publisher version), 6MB

Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002A-6393-F
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.