Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach.
BMC Genomics
; 10: 32, 2009 Jan 20.
Article
en En
| MEDLINE
| ID: mdl-19154582
ABSTRACT
BACKGROUND:
Genomic analysis will greatly benefit from considering in a global way various sources of molecular data with the related biological knowledge. It is thus of great importance to provide useful integrative approaches dedicated to ease the interpretation of microarray data.RESULTS:
Here, we introduce a data-mining approach, Multiple Factor Analysis (MFA), to combine multiple data sets and to add formalized knowledge. MFA is used to jointly analyse the structure emerging from genomic and transcriptomic data sets. The common structures are underlined and graphical outputs are provided such that biological meaning becomes easily retrievable. Gene Ontology terms are used to build gene modules that are superimposed on the experimentally interpreted plots. Functional interpretations are then supported by a step-by-step sequence of graphical representations.CONCLUSION:
When applied to genomic and transcriptomic data and associated Gene Ontology annotations, our method prioritize the biological processes linked to the experimental settings. Furthermore, it reduces the time and effort to analyze large amounts of 'Omics' data.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Perfilación de la Expresión Génica
/
Genómica
Tipo de estudio:
Prognostic_studies
Límite:
Animals
/
Humans
Idioma:
En
Año:
2009
Tipo del documento:
Article