Abstract
Multivariate methods have the potential to better capture complex relationships that may exist between different biological levels. Multiple Factor Analysis (MFA) is one of the most popular methods to obtain factor scores and measures of discrepancy between data sets. However, singular value decomposition in MFA is based on PCA, which is adequate only if the data is normally distributed, linear or stationary. In addition, including strongly correlated variables can overemphasize the contribution of the estimated components. In this work, we introduced a novel method referred as Independent Multifactorial Analysis (ICA-MFA) to derive relevant features from multiscale data. This method is an extended implementation of MFA, where the component value decomposition is based on Independent Component Analysis. In addition, ICA-MFA incorporates a predictive step based on an Independent Component Regression. We evaluated and compared the performance of ICA-MFA with both, the MFA method and traditional univariate analyses, in a simulation study. We showed how ICA-MFA explained up to 10-fold more variance than MFA and univariate methods. We applied the proposed algorithm in a study of 4057 individuals belonging to the population-based Rotterdam Study with available genetic and neuroimaging data, as well as information about executive cognitive functioning. Specifically, we used ICA-MFA to detect relevant genetic features related to structural brain regions, which in turn were involved, in the mechanisms of executive cognitive function. The proposed strategy makes it possible to determine the degree to which the whole set of genetic and/or neuroimaging markers contribute to the variability of the symptomatology jointly, rather than individually. While univariate results and MFA combinations only explained a limited proportion of variance (less than 2%), our method increased the explained variance (10%) and allowed the identification of significant components that maximize the variance explained in the model. The potential application of the ICA-MFA algorithm constitutes an important aspect of integrating multivariate multiscale data, specifically in the field of Neurogenetics.
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Acknowledgements
Natalia Vilor-Tejedor is funded by a pre-doctoral grant from the Agència de Gestió d’Ajuts Universitaris i de Recerca (2017 FI_B 00636), Generalitat de Catalunya – Fons Social Europeu. This work has been partially supported by a STSM Grant from EU COST Action 15120 Open Multiscale Systems Medicine (OpenMultiMed) and Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP). Further support was obtained through the Ministerio de Economía e Innovación (Spain), grant MTM2015-68140-R. ISGlobal is a member of the CERCA Programme, Generalitat de Catalunya.
Silvia Alemany thanks the Institute of Health Carlos III for her Sara Borrell postdoctoral grant (CD14/00214).
The generation and management of GWAS genotype data for the Rotterdam Study are supported by the Netherlands Organization of Scientific Research NWO Investments (no. 175.010.2005.011, 911-03-012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) project no. 050-060-810. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. This research is supported by the Dutch Technology Foundation STW (12723), which is part of the NWO, and which is partly funded by the Ministry of Economic Affairs. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (project: ORACLE, grant agreement No: 678543).
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Vilor-Tejedor, N., Ikram, M.A., Roshchupkin, G.V. et al. Independent Multiple Factor Association Analysis for Multiblock Data in Imaging Genetics. Neuroinform 17, 583–592 (2019). https://doi.org/10.1007/s12021-019-09416-z
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DOI: https://doi.org/10.1007/s12021-019-09416-z