Gene expression data analysis using closed item set mining for labeled data.
OMICS
; 14(2): 177-86, 2010 Apr.
Article
en En
| MEDLINE
| ID: mdl-20210654
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.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Análisis de Secuencia por Matrices de Oligonucleótidos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
OMICS
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2010
Tipo del documento:
Article
País de afiliación:
Eslovenia