Skip to Main content Skip to Navigation
Journal articles

Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration

Abstract : Recent advances in NGS sequencing, microarrays and mass spectrometry for omics data production have enabled the generation and collection of different modalities of high-dimensional molecular data. The integration of multiple omics datasets is a statistical challenge, due to the limited number of individuals, the high number of variables and the heterogeneity of the datasets to integrate. Recently, a lot of tools have been developed to solve the problem of integrating omics data including canonical correlation analysis, matrix factorization and SM. These commonly used techniques aim to analyze simultaneously two or more types of omics. In this article, we compare a panel of 13 unsupervised methods based on these different approaches to integrate various types of multi-omics datasets: iClusterPlus, regularized generalized canonical correlation analysis, sparse generalized canonical correlation analysis, multiple co-inertia analysis (MCIA), integrative-NMF (intNMF), SNF, MoCluster, mixKernel, CIMLR, LRAcluster, ConsensusClustering, PINSPlus and multi-omics factor analysis (MOFA). We evaluate the ability of the methods to recover the subgroups and the variables that drive the clustering on eight benchmarks of simulation. MOFA does not provide any results on these benchmarks. For clustering, SNF, MoCluster, CIMLR, LRAcluster, ConsensusClustering and intNMF provide the best results. For variable selection, MoCluster outperforms the others. However, the performance of the methods seems to depend on the heterogeneity of the datasets (especially for MCIA, intNMF and iClusterPlus). Finally, we apply the methods on three real studies with heterogeneous data and various phenotypes. We conclude that MoCluster is the best method to analyze these omics data. Availability: An R package named CrIMMix is available on GitHub at https://github.com/CNRGH/crimmix to reproduce all the results of this article.
Complete list of metadatas

Cited literature [64 references]  Display  Hide  Download

https://hal-cea.archives-ouvertes.fr/cea-02393847
Contributor : Morgane Pierre-Jean <>
Submitted on : Wednesday, December 4, 2019 - 3:18:39 PM
Last modification on : Wednesday, October 14, 2020 - 4:21:40 AM
Long-term archiving on: : Thursday, March 5, 2020 - 8:23:06 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2020-12-04

Please log in to resquest access to the document

Identifiers

Citation

Morgane Pierre-Jean, Jean-François Deleuze, Edith Le Floch, Florence Mauger. Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration. Briefings in Bioinformatics, Oxford University Press (OUP), 2019, ⟨10.1093/bib/bbz138⟩. ⟨cea-02393847⟩

Share

Metrics

Record views

317