Two-Way Analysis of High-Dimensional Collinear Data
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.
Cite
Text
Huopaniemi et al. "Two-Way Analysis of High-Dimensional Collinear Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04180-8_18Markdown
[Huopaniemi et al. "Two-Way Analysis of High-Dimensional Collinear Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/huopaniemi2009ecmlpkdd-twoway/) doi:10.1007/978-3-642-04180-8_18BibTeX
@inproceedings{huopaniemi2009ecmlpkdd-twoway,
title = {{Two-Way Analysis of High-Dimensional Collinear Data}},
author = {Huopaniemi, Ilkka and Suvitaival, Tommi and Nikkilä, Janne and Oresic, Matej and Kaski, Samuel},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2009},
pages = {33},
doi = {10.1007/978-3-642-04180-8_18},
url = {https://mlanthology.org/ecmlpkdd/2009/huopaniemi2009ecmlpkdd-twoway/}
}