iClusterBayes: Integrative clustering of multiple genomic data types
In iClusterPlus: Integrative clustering of multi-type genomic data

Description Usage Arguments Value Author(s) References See Also Examples

Given multiple genomic data types (e.g., copy number, gene expression, DNA methylation) measured in the same set of samples, iClusterBayes fits a Bayesian latent variable model that generates an integrated cluster assignment based on joint inference across data types and identifies genomic features that contribute to the clusters.

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iClusterBayes(dt1,dt2=NULL,dt3=NULL,dt4=NULL,dt5=NULL,dt6=NULL,
	type = c("gaussian","binomial","poisson"),K=2,n.burnin=1000,n.draw=1200,
	prior.gamma=rep(0.1,6),sdev=0.5,beta.var.scale=1,thin=1,pp.cutoff=0.5)

`dt1`	Data set 1 - a matrix with rows and columns representing samples and genomic features, respectively.
`dt2`	Data set 2 - a matrix with rows and columns representing samples and genomic features, respectively.
`dt3`	Data set 3 - a matrix with rows and columns representing samples and genomic features, respectively.
`dt4`	Data set 4 - a matrix with rows and columns representing samples and genomic features, respectively.
`dt5`	Data set 5 - a matrix with rows and columns representing samples and genomic features, respectively.
`dt6`	Data set 6 - a matrix with rows and columns representing samples and genomic features, respectively.
`type`	Data type corresponding to dt1-6, which can be gaussian, binomial, or poisson.
`K`	The number of eigen features. Given K, the number of cluster is K+1.
`n.burnin`	Number of MCMC burnin.
`n.draw`	Number of MCMC draw.
`prior.gamma`	Prior probability for the indicator variable gamma of each data set.
`sdev`	Standard deviation of random walk proposal for the latent variable.
`beta.var.scale`	A positive value to control the scale of covariance matrix of the proposed beta.
`thin`	A parameter to thin the MCMC chain in order to reduce autocorrelation. Discard all but every 'thin'th sampling values. When thin=1, all sampling values are kept.
`pp.cutoff`	Posterior probability cutoff for the indicator variable gamma. The BIC and deviance ratio will be calculated by setting parameter beta to zero when the posterior probability of gamma <= cutoff.

A list with the following elements.

`alpha`	Intercept parameter.
`beta`	Information parameter.
`beta.pp`	Posterior probability of beta. The higher the beta.pp, the more likely the beta should be included in the model.
`gamma.ar`	Acceptance ratio for the parameter gamma.
`beta.ar`	Acceptance ratio for the parameter beta.
`Z.ar`	Acceptance ratio for the latent variable.
`clusters`	Cluster assignment.
`centers`	Cluster center.
`meanZ`	The latent variable.
`BIC`	Bayesian information criterion.
`dev.ratio`	see dev.ratio defined in glmnet package.

Qianxing Mo qianxing.mo@moffitt.org

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

tune.iClusterBayes,plotHMBayes,iClusterPlus,tune.iClusterPlus,plotHeatmap