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Dimension reduction and regularisation combined with Partial Least Squares in high dimensional imaging-genetics studies

Abstract : In the imaging genetics field, the classical univariate approach ignores the potential joint effects between genes or the potential covariations between brain regions. In this paper, we propose instead to investigate exploratory multivariate methods, namely partial least squares regression or canonical correlation analysis, in order to identify a set of genetic polymorphisms covarying with a set of neuroimaging phenotypes. However, in high-dimensional settings, such multivariate methods may encounter overfitting issues. Thus, we investigate the use of different strategies of regularization and dimension reduction, combined with PLS or CCA, to face the very high dimensionality of imaging genetics studies. We propose a comparison study of the different strategies on a simulated dataset. We estimate the generalisability of the multivariate association with a cross-validation scheme and assess the capacity of good detection. Univariate selection seems necessary to reduce the dimensionality. However, the best results are obtained by combining univariate filtering and L-1-regularized PLS, which suggests that discovering meaningful genetic associations calls for a multivariate approach.
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https://hal-supelec.archives-ouvertes.fr/hal-00752233
Contributor : Alexandra Siebert Connect in order to contact the contributor
Submitted on : Thursday, November 15, 2012 - 10:59:00 AM
Last modification on : Wednesday, June 16, 2021 - 3:46:41 AM

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  • HAL Id : hal-00752233, version 1
  • PRODINRA : 316747

Citation

Edith Le Floch, Laura Trinchera, Arthur Tenenhaus, Jean-Baptiste Poline, Vincent Frouin. Dimension reduction and regularisation combined with Partial Least Squares in high dimensional imaging-genetics studies. PLS'12, May 2012, Houston, United States. CD-ROM Proceedings (8 p.). ⟨hal-00752233⟩

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