getMOIC: Get subtypes from multi-omics integrative clustering

In xlucpu/MOVICS: Multi-Omics integration and VIsualization in Cancer Subtyping

Description

Using 'getMOIC()', users can choose one out of the ten algorithms embedded in 'MOVICS'. Users can implement multi-omics clustering in a simplest way of which the only requirement is to specify and at least specify a list of matrices (argument of 'data'), a number of cluster (argument of 'N.clust'), and clustering method (argument of 'methodslist') in 'getMOIC()'. It is possible to pass various arguments that are specific to each method. Of course, users can also directly call different algorithms by using functions start with 'get' and end with the name of the algorithm (e.g., 'getSNF'; please refer to '?get

Usage

getMOIC(
  data = NULL,
  methodslist = list("SNF", "CIMLR", "PINSPlus", "NEMO", "COCA", "MoCluster",
    "LRAcluster", "ConsensusClustering", "IntNMF", "iClusterBayes"),
  N.clust = NULL,
  type = rep("gaussian", length(data)),
  ...
)

Arguments

data	List of matrices (Maximum number of matrices is 6).
methodslist	A string list specifying one or multiple methods to run (See Details).
N.clust	Number of clusters.
type	Data type corresponding to the list of matrics, which can be gaussian, binomial or possion.
...	Additionnal parameters for each method (only works when only one method chosen)

Details

Method for integrative clustering will be chosed according to the value of argument 'methodslist':

If methodslist == "IntNMF", Integrative clustering methods using Non-Negative Matrix Factorization

If methodslist == "SNF", Similarity network fusion.

If methodslist == "LRAcluster", Integrated cancer omics data analysis by low rank approximation.

If methodslist == "PINSPlus", Perturbation Clustering for data integration and disease subtyping

If methodslist == "ConsensusClustering", Consensus clustering

If methodslist == "NEMO", Neighborhood based multi-omics clustering

If methodslist == "COCA", Cluster Of Clusters Analysis

If methodslist == "CIMLR", Cancer Integration via Multikernel Learning (Support Feature Selection)

If methodslist == "MoCluster", Identifying joint patterns across multiple omics data sets (Support Feature Selection)

If methodslist == "iClusterBayes", Integrative clustering of multiple genomic data by fitting a Bayesian latent variable model (Support Feature Selection)

Value

A list of results returned by each specified algorithms.

References

Pierre-Jean M, Deleuze J F, Le Floch E, et al. Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration[J]. Briefings in Bioinformatics, 2019.

intNMF: Chalise P, Fridley BL. Integrative clustering of multi-level omic data based on non-negative matrix factorization algorithm. PLoS One. 2017;12(5):e0176278.

iClusterBayes: Mo Q, Shen R, Guo C, Vannucci M, Chan KS, Hilsenbeck SG. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics. 2018;19(1):71-86.

SNF: Wang B, Mezlini AM, Demir F, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014;11(3):333-337.

Mocluster: Meng C, Helm D, Frejno M, Kuster B. moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets. J Proteome Res. 2016;15(3):755-765.

LRAcluster: Wu D, Wang D, Zhang MQ, Gu J. Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification. BMC Genomics. 2015;16:1022.

CIMLR: Ramazzotti D, Lal A, Wang B, Batzoglou S, Sidow A. Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival. Nat Commun. 2018;9(1):4453.

PINSPlus: Nguyen H, Shrestha S, Draghici S, Nguyen T. PINSPlus: a tool for tumor subtype discovery in integrated genomic data. Bioinformatics. 2019;35(16):2843-2846.

ConsensusClustering: Monti S, Tamayo P, Mesirov J, et al. Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Machine Learning. 2003;52:91-118.

NEMO: Rappoport N, Shamir R. NEMO: cancer subtyping by integration of partial multi-omic data. Bioinformatics. 2019;35(18):3348-3356.

COCA: Hoadley KA, Yau C, Wolf DM, et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell. 2014;158(4):929-944.

Examples

# There is no example and please refer to vignette.

xlucpu/MOVICS documentation built on July 24, 2021, 9:23 p.m.