@article{
    Huopaniemi09,
    author = {Huopaniemi, Ilkka and Suvitaival, Tommi and Nikkil{\"{a}}, Janne and Ore{\v{s}}i{\v{c}}, Matej and Kaski, Samuel},
    responsibleauthor = "Kaski, Samuel",
    doi = "10.1007/s10618-009-0142-5",
    title = "Two-Way Analysis of High-Dimensional Collinear Data",
    url = "http://dx.doi.org/10.1007/s10618-009-0142-5",
    journal = "Data Mining and Knowledge Discovery",
    corerank = "A",
    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.",
    volume = "19",
    flags = "AIRC HIIT public",
    year = "2009",
    pdf = "huopaniemi09.pdf",
    impactfactor = "A",
    pages = "261-276"
}