iClusterBayes {iClusterPlus} | R Documentation

## Integrative clustering of multiple genomic data types

### Description

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.

### Usage

    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)

### Arguments

`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.

### Value

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.

### Author(s)

Qianxing Mo qianxing.mo@moffitt.org

### References

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.

### See Also

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

### Examples

    # see iManual.pdf

[Package iClusterPlus version 1.23.0 Index]