Paper Info
Title
Multivariate multi-way analysis of multi-source data.
Abstract
Analysis of variance (ANOVA)-type methods are the default tool for the analysis of data with multiple covariates. These tools have been generalized to the multivariate analysis of high-throughput biological datasets, where the main challenge is the problem of small sample size and high dimensionality. However, the existing multi-way analysis methods are not designed for the currently increasingly important experiments where data is obtained from multiple sources. Common examples of such settings include integrated analysis of metabolic and gene expression profiles, or metabolic profiles from several tissues in our case, in a controlled multi-way experimental setup where disease status, medical treatment, gender and time-series are usual covariates.We extend the applicability area of multivariate, multi-way ANOVA-type methods to multi-source cases by introducing a novel Bayesian model. The method is capable of finding covariate-related dependencies between the sources. It assumes the measurements consist of groups of similarly behaving variables, and estimates the multivariate covariate effects and their interaction effects for the discovered groups of variables. In particular, the method partitions the effects to those shared between the sources and to source-specific ones. The method is specifically designed for datasets with small sample sizes and high dimensionality. We apply the method to a lipidomics dataset from a lung cancer study with two-way experimental setup, where measurements from several tissues with mostly distinct lipids have been taken. The method is also directly applicable to gene expression and proteomics.An R-implementation is available at http://www.cis.hut.fi/projects/mi/software/multiWayCCA/.
Year
DOI
Venue
2010
10.1093/bioinformatics/btq174
Bioinformatics [ISMB]
Keywords
Field
DocType
small sample size,existing multi-way analysis method,gene expression,integrated analysis,controlled multi-way experimental setup,multivariate analysis,type method,multivariate covariate effect,high dimensionality,multivariate multi-way analysis,multi-source data,multi-way anova-type method,analysis of variance,algorithms,data collection,gene expression profiling
Data collection,Data mining,Covariate,Bayesian inference,Data analysis,Computer science,Multivariate statistics,Curse of dimensionality,Bioinformatics,Multivariate analysis,Sample size determination
Journal
Volume
Issue
ISSN
26
12
1367-4811
Citations 
PageRank 
References 
16
0.94
11
Authors
5
Name
Order
Citations
PageRank
Ilkka Huopaniemi1262.41
Tommi Suvitaival2333.21
Janne Nikkilä320016.65
Matej Oresic432334.23
Samuel Kaski52755245.52