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

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

In order to determining the appropriate number of clusters, tune.iClusterBayes calls iClusterBayes function and performs parallel computation for K=1,2,....

tune.iClusterBayes(cpus=6,dt1,dt2=NULL,dt3=NULL,dt4=NULL,dt5=NULL,dt6=NULL,
	type=c("gaussian","binomial","poisson"),
   	K=1:6,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)

`cpus`	Number of CPU used for parallel computation. If possible, let it be equal to the number of Ks.
`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, poisson.
`K`	A vector. Each element is 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 named 'fit'. fit[[i]] is an object return by iClusterBayes, corresponding to the ith element in K. Each component of fit has 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 parameter gamma.
`beta.ar`	Acceptance ratio for parameter beta.
`Z.ar`	Acceptance ratio for the latent variable.
`clusters`	Cluster assignment.
`centers`	Cluster center.
`meanZ`	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.

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