PT - JOURNAL ARTICLE
AU - Beaton, Derek
AU - ,
AU - Saporta, Gilbert
AU - Abdi, Hervé
TI - A generalization of partial least squares regression and correspondence analysis for categorical and mixed data: An application with the ADNI data
AID - 10.1101/598888
DP - 2020 Jan 01
TA - bioRxiv
PG - 598888
4099 - http://biorxiv.org/content/early/2020/07/16/598888.short
4100 - http://biorxiv.org/content/early/2020/07/16/598888.full
AB - Current large scale studies of brain and behavior typically involve multiple populations, diverse types of data (e.g., genetics, brain structure, behavior, demographics, or “mutli-omics,” and “deep-phenotyping”) measured on various scales of measurement. To analyze these heterogeneous data sets we need simple but flexible methods able to integrate the inherent properties of these complex data sets. Here we introduce partial least squares-correspondence analysis-regression (PLS-CA-R) a method designed to address these constraints. PLS-CA-R generalizes PLS regression to most data types (e.g., continuous, ordinal, categorical, non-negative values). We also show that PLS-CA-R generalizes many “two-table” multivariate techniques and their respective algorithms, such as various PLS approaches, canonical correlation analysis, and redundancy analysis (a.k.a. reduced rank regression).Competing Interest StatementThe authors have declared no competing interest.