runMDI

C++ function to perform MCMC sampling for MDI.

Integrative clustering and semi-supervised prediction.
This package includes a 'C++' implementation of both semi-supervised and
unsupervised Multiple Dataset Integration (MDI; Kirk et al., 2012
<https://doi-org.ezp.lib.cam.ac.uk/10.1093/bioinformatics/bts595>), an
extension of Bayesian mixture models to the integrative (multiple dataset or
multi-modal) setting such as multi-omics analysis. The package also includes
Bayesian mixture models. Densities allowed within MDI and the mixture model
are Gaussian with full and diagonal covariance matrices, categorical, Gaussian
with a Gaussian process (GP) prior on the mean parameter. The GP model
implemented canonly handle data with common time points of equal separation.
The Gaussian and GP models can be augmented with a global multivariate t
distribution to handle outliers.

Stephen Coleman

mdir

Bayesian Model-Based Clustering of Multi-Modal Data

Paul Kirk

Chris Wallace

Oliver Crook

runMDI function

<dl><dt>R</dt>
<dd>The number of iterations to run for.</dd>
<dt>thin</dt>
<dd>thinning factor for samples recorded.</dd>
<dt>Y</dt>
<dd>The list of data matrices to perform integrative clustering upon 
with items to cluster in rows.</dd>
<dt>K</dt>
<dd>Vector of the number of components to model in each view. This is the upper 
limit on the number of clusters that can be found.</dd>
<dt>mixture_types</dt>
<dd>Character vector of densities used in each view</dd>
<dt>outlier_types</dt>
<dd>Character vector of outlier components used in each 
view ('MVT' or 'None').</dd>
<dt>labels</dt>
<dd>Matrix item labels to initialise from. Rows correspond to the
items being clustered, columns to views.</dd>
<dt>fixed</dt>
<dd>Binary matrix of the items that are fixed in their initial
label.</dd>
<dt>proposal_windows</dt>
<dd>List/field of vectors</dd></dl>

Arguments

Call Multiple Dataset Integration — runMDI

<dl>

<dt>R</dt>
<dd>The number of iterations to run for.</dd>


<dt>thin</dt>
<dd>thinning factor for samples recorded.</dd>


<dt>Y</dt>
<dd>The list of data matrices to perform integrative clustering upon 
with items to cluster in rows.</dd>


<dt>K</dt>
<dd>Vector of the number of components to model in each view. This is the upper 
limit on the number of clusters that can be found.</dd>


<dt>mixture_types</dt>
<dd>Character vector of densities used in each view</dd>


<dt>outlier_types</dt>
<dd>Character vector of outlier components used in each 
view ('MVT' or 'None').</dd>


<dt>labels</dt>
<dd>Matrix item labels to initialise from. Rows correspond to the
items being clustered, columns to views.</dd>


<dt>fixed</dt>
<dd>Binary matrix of the items that are fixed in their initial
label.</dd>


<dt>proposal_windows</dt>
<dd>List/field of vectors</dd>

</dl>

runMDI: Call Multiple Dataset Integration

Description

Usage

Value

Arguments