Efficient and Distributed Generalized Canonical Correlation Analysis for Big Multiview Data

2019 ◽  
Vol 31 (12) ◽  
pp. 2304-2318 ◽  
Author(s):  
Xiao Fu ◽  
Kejun Huang ◽  
Evangelos E. Papalexakis ◽  
Hyun Ah Song ◽  
Partha Talukdar ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Generalized Canonical Correlation Analysis

scholarly journals Variable selection for generalized canonical correlation analysis

Biostatistics ◽  
2014 ◽  
Vol 15 (3) ◽  
pp. 569-583 ◽  
Author(s):  
A. Tenenhaus ◽  
C. Philippe ◽  
V. Guillemot ◽  
K.-A. Le Cao ◽  
J. Grill ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Variable Selection ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Generalized Canonical Correlation Analysis ◽  
Selection For

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

2019 ◽  
Vol 21 (6) ◽  
pp. 2011-2030 ◽  
Author(s):  
Morgane Pierre-Jean ◽  
Jean-François Deleuze ◽  
Edith Le Floch ◽  
Florence Mauger
Keyword(s):  
Correlation Analysis ◽  
Variable Selection ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Molecular Data ◽  
Heterogeneous Data ◽  
Data Availability ◽  
Omics Data ◽  
Generalized Canonical Correlation Analysis ◽  
Unsupervised Methods

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.

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