Two-way analysis of high-dimensional collinear data

Ilkka Huopaniemi (Corresponding Author), Tommi Suvitaival, Janne Nikkilä, Matej Orešič, Samuel Kaski

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)261-276
JournalData Mining and Knowledge Discovery
Issue number2
Publication statusPublished - 2009
MoE publication typeA1 Journal article-refereed


  • factor analysis
  • hierarchical model
  • metabolomics
  • multi-way analysis
  • small sample-size


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