Abstract
We present a Bayesian model for two-way ANOVA-type analysis of high-dimensional, 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 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. We apply the methods to study lipidomic profiles of a recent large-cohort human diabetes study.
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Responsible editors: Aleksander Kołcz, Wray Buntine, Marko Grobelnik, Dunja Mladenic, and John Shawe-Taylor.
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Huopaniemi, I., Suvitaival, T., Nikkilä, J. et al. Two-way analysis of high-dimensional collinear data. Data Min Knowl Disc 19, 261–276 (2009). https://doi.org/10.1007/s10618-009-0142-5
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DOI: https://doi.org/10.1007/s10618-009-0142-5