Two-way analysis of high-dimensional collinear data

Ilkka Huopaniemi, Tommi Suvitaival, Janne Nikkilä, Matej Orešič, Samuel Kaski
2009 Data mining and knowledge discovery  
We present a Bayesian model for two-way ANOVA-type analysis of highdimensional, small sample-size datasets with highly correlated groups of variables. Modern cellular measurement methods are a main application area; typically the task is differential analysis between diseased and healthy samples, complicated by additional covariates requiring a multi-way analysis. The main complication is the combination of high dimensionality and low sample size, which renders classical multivariate techniques
more » ... useless. We introduce a hierarchical model which does dimensionality reduction by assuming that the input variables come in similarly-behaving groups, and performs an ANOVA-type decomposition for the set of reduced-dimensional latent variables. 123 262 I. Huopaniemi et al. We apply the methods to study lipidomic profiles of a recent large-cohort human diabetes study.
doi:10.1007/s10618-009-0142-5 fatcat:d62mndvfifbfhkjzfhf2zjbjca