Abstract
BackgroundJoint 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.ResultsThe 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.ConclusionsAll 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1037-2) contains supplementary material, which is available to authorized users.
Highlights
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
Subsequent statistical data analysis on these data should reveal the relevant information to that process
In functional genomics research it becomes more and more common that multiple platforms are used to explore the variation in samples for a given study. This leads to multiple sets of data with the same objects but different features
Summary
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. Subsequent statistical data analysis on these data should reveal the relevant information to that process For hypothesis testing such an approach of theory and measuring can be relatively straightforward especially if the analytical instruments are designed for that purpose. In lack of such hypotheses and using generic but readily available analytical instruments, obvious data structures are rarely observed and extensive data analysis and interpretation are necessary A new van der Kloet et al BMC Bioinformatics 2016, 17(Suppl 5):195 group of low level data fusion methods has recently been introduced that are able to separate the variation in all data-sets
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