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- W1648000161 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." @default.
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- W1648000161 date "2009-01-01" @default.
- W1648000161 modified "2023-10-02" @default.
- W1648000161 title "Two-Way Analysis of High-Dimensional Collinear Data" @default.
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- W1648000161 doi "https://doi.org/10.1007/978-3-642-04180-8_18" @default.
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