title: Principal components analysis and the reported low intrinsic dimensionality of gene expression microarray data.
abstract: Principal components analysis (PCA) is a common unsupervised method for the analysis of gene expression microarray data, providing information on the overall structure of the analyzed dataset. In the recent years, it has been applied to very large datasets involving many different tissues and cell types, in order to create a low dimensional global map of human gene expression. Here, we reevaluate this approach and show that the linear intrinsic dimensionality of this global map is higher than previously reported. Furthermore, we analyze in which cases PCA fails to detect biologically relevant information and point the reader to methods that overcome these limitations. Our results refine the current understanding of the overall structure of gene expression spaces and show that PCA critically depends on the effect size of the biological signal as well as on the fraction of samples containing this signal.
authors.affiliation: Joint Research Center for Computational Biomedicine, RWTH Aachen University, 52062 Aachen, Germany.|Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, 52062 Aachen, Germany.|Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, Netherlands.
authors.forename: Michael
authors.initials: M
authors.lastname: Lenz
authors.affiliation: Zentrum für Integrative Psychiatrie, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
authors.forename: Franz-Josef
authors.initials: FJ
authors.lastname: Müller
authors.affiliation: Institute for Biomedical Engineering, Department of Cell Biology, RWTH Aachen University Medical School, 52074 Aachen, Germany.|Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Zenke
authors.affiliation: Joint Research Center for Computational Biomedicine, RWTH Aachen University, 52062 Aachen, Germany.|Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, 52062 Aachen, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Schuppert
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D058977:Molecular Sequence Annotation / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods*