IntMultiOmics: Integration Multi-omics
In CNRGH/crimmix: Multi-omics Integration

Description Usage Arguments Details References Examples

Integration Multi-omics

1	IntMultiOmics(data, method, ...)

`data`	List of matrices.
`method`	A character value specifying the method to run (See Details).
`...`	Additionnal parameters for each methods

We run the method according to the value of argument 'method':

If method=="SGCCA", Variable selection for generalized canonical correlation analysis If method=="RGCCA", Regularized generalized canonical correlation analysis. If method=="iCluster", Integrative subtype discovery If method=="intNMF",I ntegrative clustering methods using Non-Negative Matrix Factorization If method=="Mocluster", Identifying joint patterns across multiple omics data sets. If method=="MOFA" Multi-omics factor analysis If method=="MixKernel", Unsupervised multiple kernel learning If method=="SNF" Run Similarity network fusion If method=="MCIA" Run Multiple Co-inertia Aalysis

Tenenhaus, A., Philippe, C., Guillemot, V., et al. Variable selection for generalized canonical correlation analysis. Biostatistics, 15(3):569–583, 2014.

Tenenhaus, A. and Tenenhaus, M. Regularized generalized canonical correlation analysis. Psychometrika, 76(2):257–284, 2011.

Shen, R., Mo, Q., Schultz, N., et al. Integrative subtype discovery in glioblastoma using icluster. PloS one, 7(4):e35236, 2012.

Chalise, P., Koestler, D. C., Bimali, M., et al. Integrative clustering methods for high-dimensional molecular data. Translational cancer research, 3(3):202, 2014.

Meng, C., Helm, D., Frejno, M., et al. mocluster: Identifying joint patterns across multiple omics data sets. Journal of proteome research, 15(3):755–765,2015.

Argelaguet, R., Velten, B., Arnol, D., et al. Multi-omics factor analysis - a framework for unsupervised integration of multi-omics data sets. Molecular systems biology, 14(6):e8124, 2018.

Mariette, J. and Villa-Vialaneix, N. Unsupervised multiple kernel learning for heterogeneous data integration. Bioinformatics, 34(6):1009–1015, 2017.

Wang, B., Mezlini, A. M., Demir, F., et al. Similarity network fusion for aggregating data types on a genomic scale. Nature methods, 11(3):333, 2014.

Meng, C., Kuster, B., Culhane, A. C., & Gholami, A. M. (2014). A multivariate approach to the integration of multi-omics datasets. BMC bioinformatics, 15(1), 162

Nguyen H, Shrestha S, Draghici S, Nguyen T. PINSPlus: A tool for tumor subtype discovery in integrated genomic data. Bioinformatics, 2018

Wilkerson, M.D., Hayes, D.N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking Bioinformatics, 2010 Jun 15;26(12):1572-3.

Wu, D., Wang, D., Zhang, M. Q., & Gu, J. Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification. BMC genomics, 16(1), 1022.

Ramazzotti, D., Lal, A., Wang, B., Batzoglou, S., & Sidow, A. (2018). Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival. Nature communications, 9(1), 4453.

set.seed(34)
c_1 <- simulateY(J=100, prop=0.1, noise=1)
c_2 <- simulateY(J=200, prop=0.1, noise=0.1)
data <- list(c_1$data, c_2$data)
res <- CrIMMix::IntMultiOmics(data, K=4, method="RGCCA") # ok
res <- CrIMMix::IntMultiOmics(data, K=4, method="SGCCA") # ok
## Not run: res <- CrIMMix::IntMultiOmics(data, K=4, method="MOFA") # ok
res <- CrIMMix::IntMultiOmics(data, K=4, method="SNF", K_n=10, sigma=0.5) # ok
res <- CrIMMix::IntMultiOmics(data, K=4, method="intNMF") # ok
res <- CrIMMix::IntMultiOmics(data, K=4, method="MixKernel") # ok
res <- CrIMMix::IntMultiOmics(data, K=4, method="Mocluster") # ok
res <- CrIMMix::IntMultiOmics(data, K=4, method="iCluster") # ok
res <- CrIMMix::IntMultiOmics(data, K=4, method="MCIA") # ok