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A generalization of partial least squares regression and correspondence analysis for categorical and mixed data: An application with the ADNI data

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Abstract

The present and future of large scale studies of human brain and behaviorin typical and disease populationsis mutli-omics, deep-phenotyping, or other types of multi-source and multi-domain data collection initiatives. These massive studies rely on highly interdisciplinary teams that collect extremely diverse types of data across numerous systems and scales of measurement (e.g., genetics, brain structure, behavior, and demographics). Such large, complex, and heterogeneous data requires relatively simple methods that allow for exibility in analyses without the loss of the inherent properties of various data types. Here we introduce a method designed * Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimag-ing Initiative (ADNI) database (http://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at
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hal-02471325 , version 1 (07-02-2020)

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Derek Beaton, Gilbert Saporta, Hervé Abdi. A generalization of partial least squares regression and correspondence analysis for categorical and mixed data: An application with the ADNI data. 2020. ⟨hal-02471325⟩
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