Name
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.
|
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s10618-009-0142-5 | Data Min. Knowl. Discov. |
Keywords | Field | DocType |
ANOVA,Factor analysis,Hierarchical model,Metabolomics,Multi-way analysis,Small sample-size | Data mining,Covariate,Bayesian inference,Dimensionality reduction,Pattern recognition,Multivariate statistics,Computer science,Latent variable,Curse of dimensionality,Artificial intelligence,Hierarchical database model,Sample size determination | Journal |
Volume | Issue | ISSN |
19 | 2 | 0302-9743 |
Citations | PageRank | References |
9 | 0.76 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ilkka Huopaniemi | 1 | 26 | 2.41 |
| Tommi Suvitaival | 2 | 33 | 3.21 |
| Janne Nikkilä | 3 | 200 | 16.65 |
| Matej Oresic | 4 | 323 | 34.23 |
| Samuel Kaski | 5 | 2755 | 245.52 |