title: Gene expression data analysis using closed item set mining for labeled data.
abstract: This article presents an approach to microarray data analysis using discretised expression values in combination with a methodology of closed item set mining for class labeled data (RelSets). A statistical 2 x 2 factorial design analysis was run in parallel. The approach was validated on two independent sets of two-color microarray experiments using potato plants. Our results demonstrate that the two different analytical procedures, applied on the same data, are adequate for solving two different biological questions being asked. Statistical analysis is appropriate if an overview of the consequences of treatments and their interaction terms on the studied system is needed. If, on the other hand, a list of genes whose expression (upregulation or downregulation) differentiates between classes of data is required, the use of the RelSets algorithm is preferred. The used algorithms are freely available upon request to the authors.
authors.affiliation: National Institute of Biology, Department of Biotechnology and Systems Biology, Ljubljana, Slovenia. ana.rotter@nib.si
authors.forename: Ana
authors.initials: A
authors.lastname: Rotter
authors.forename: Petra Kralj
authors.initials: PK
authors.lastname: Novak
authors.forename: Spela
authors.initials: S
authors.lastname: Baebler
authors.forename: Natasa
authors.initials: N
authors.lastname: Toplak
authors.forename: Andrej
authors.initials: A
authors.lastname: Blejec
authors.forename: Nada
authors.initials: N
authors.lastname: Lavrac
authors.forename: Kristina
authors.initials: K
authors.lastname: Gruden
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*