title: How to extract marker genes from microarray data sets.
abstract: In this study we focus on classification tasks and apply matrix factorization techniques like principal component analysis (PCA), independent component analysis (ICA) and non-negative matrix factorization (NMF) to a microarray data set. The latter monitors the gene expression levels (GEL) of mononcytes and macrophages during and after differentiation. We show that these tools are able to identify relevant signatures in the deduced matrices and extract marker genes from these gene expression profiles (GEPs) without the need for extensive data bank search for appropriate functional annotations. With these marker genes corresponding test data sets can then easily be classified into related diagnostic categories.
authors.affiliation: Institute for Biophysics, Computational Intelligence Group, University of Regensburg, Regensburg, Germany.
authors.forename: R
authors.initials: R
authors.lastname: Schachtner
authors.forename: D
authors.initials: D
authors.lastname: Lutter
authors.forename: F J Theis
authors.initials: FJ
authors.lastname: Theis
authors.forename: E W
authors.initials: EW
authors.lastname: Lang
authors.forename: G
authors.initials: G
authors.lastname: Schmitz
authors.forename: A M
authors.initials: AM
authors.lastname: Tomé
authors.forename: P Gómez
authors.initials: PG
authors.lastname: Vilda
mesh_terms: D002454:Cell Differentiation / Q000502:physiology; D020869:Gene Expression Profiling / Q000379:methods*; D005819:Genetic Markers; D006801:Humans; D008264:Macrophages / Q000166:cytology / Q000378:metabolism; D009000:Monocytes / Q000166:cytology / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*
chemical_list: D005819:Genetic Markers