Separating common from distinctive variation.
BACKGROUND
Joint and individual variation explained (JIVE), distinct and common simultaneous component analysis (DISCO) and O2-PLS, a two-block (X-Y) latent variable regression method with an integral OSC filter can all be used for the integrated analysis of multiple data sets and decompose them in three terms: a low(er)-rank approximation capturing common variation across data sets, low(er)-rank approximations for structured variation distinctive for each data set, and residual noise. In this paper these three methods are compared with respect to their mathematical properties and their respective ways of defining common and distinctive variation.


RESULTS
The methods are all applied on simulated data and mRNA and miRNA data-sets from GlioBlastoma Multiform (GBM) brain tumors to examine their overlap and differences. When the common variation is abundant, all methods are able to find the correct solution. With real data however, complexities in the data are treated differently by the three methods.


CONCLUSIONS
All three methods have their own approach to estimate common and distinctive variation with their specific strength and weaknesses. Due to their orthogonality properties and their used algorithms their view on the data is slightly different. By assuming orthogonality between common and distinctive, true natural or biological phenomena that may not be orthogonal at all might be misinterpreted.
Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098, XH, Amsterdam, The Netherlands.
Frans M
FM
van der Kloet
Computational Genomics Program, Centro de Investigaciones Príncipe Felipe, Valencia, Spain.
Patricia
P
Sebastián-León
Computational Genomics Program, Centro de Investigaciones Príncipe Felipe, Valencia, Spain.
Ana
A
Conesa
Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098, XH, Amsterdam, The Netherlands.
Age K
AK
Smilde
Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098, XH, Amsterdam, The Netherlands. j.a.westerhuis@uva.nl.
Johan A
JA
Westerhuis
D000465:Algorithms; D001932:Brain Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D005909:Glioblastoma / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D006801:Humans; D035683:MicroRNAs / Q000378:metabolism; D025341:Principal Component Analysis; D012333:RNA, Messenger / Q000378:metabolism
D035683:MicroRNAs; D012333:RNA, Messenger
DISCO; Integrated analysis; JIVE; Multiple data-sets; O2-PLS