MVDA: a multi-view genomic data integration methodology.
BACKGROUND
Multiple high-throughput molecular profiling by omics technologies can be collected for the same individuals. Combining these data, rather than exploiting them separately, can significantly increase the power of clinically relevant patients subclassifications.


RESULTS
We propose a multi-view approach in which the information from different data layers (views) is integrated at the levels of the results of each single view clustering iterations. It works by factorizing the membership matrices in a late integration manner. We evaluated the effectiveness and the performance of our method on six multi-view cancer datasets. In all the cases, we found patient sub-classes with statistical significance, identifying novel sub-groups previously not emphasized in literature. Our method performed better as compared to other multi-view clustering algorithms and, unlike other existing methods, it is able to quantify the contribution of single views on the final results.


CONCLUSIONS
Our observations suggest that integration of prior information with genomic features in the subtyping analysis is an effective strategy in identifying disease subgroups. The methodology is implemented in R and the source code is available online at http://neuronelab.unisa.it/a-multi-view-genomic-data-integration-methodology/ .
NeuRoNe Lab, Department of Computer Science, University of Salerno, Fisciano, Italy. aserra@unisa.it.
Angela
A
Serra
Department of Medical, Surgical, Neurological, Metabolic and Ageing Sciences, Second University of Napoli, Napoli, Italy. michele.fratello@unina2.it.
Michele
M
Fratello
Unit of Systems Toxicology and Nanosafety Research Centre, Finnish Institute of Occupational Health, FIOH, Helsinki, Finland. vittorio.fortino@ttl.fi.
Vittorio
V
Fortino
NeuRoNe Lab, Department of Computer Science, University of Salerno, Fisciano, Italy. gianni@unisa.it.
Giancarlo
G
Raiconi
NeuRoNe Lab, Department of Computer Science, University of Salerno, Fisciano, Italy. rtagliaferri@unisa.it.
Roberto
R
Tagliaferri
Unit of Systems Toxicology and Nanosafety Research Centre, Finnish Institute of Occupational Health, FIOH, Helsinki, Finland. dario.greco@ttl.fi.
Dario
D
Greco
D000465:Algorithms; D016000:Cluster Analysis; D023281:Genomics / Q000379:methods*; D035683:MicroRNAs / Q000235:genetics / Q000378:metabolism; D017423:Sequence Analysis, RNA
D035683:MicroRNAs