Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach.
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.


CONCLUSIONS
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.
de Tayrac
Marie
M
CNRS UMR 6061, Université de Rennes 1, IFR 140, Faculté de Médecine, CS 34317, 35043 Rennes, France. marie.de-tayrac@univ-rennes1.fr
Lê
Sébastien
S
Aubry
Marc
M
Mosser
Jean
J
Husson
François
F
D000818:Animals; D055028:Comparative Genomic Hybridization; D005163:Factor Analysis, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics / Q000379:methods*; D005910:Glioma / Q000235:genetics; D006801:Humans; D051379:Mice; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods