Gene expression data analysis using closed item set mining for labeled data.
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.
Rotter
Ana
A
National Institute of Biology, Department of Biotechnology and Systems Biology, Ljubljana, Slovenia. ana.rotter@nib.si
Novak
Petra Kralj
PK
Baebler
Spela
S
Toplak
Natasa
N
Blejec
Andrej
A
Lavrac
Nada
N
Gruden
Kristina
K
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*