Description Usage Arguments Value References Examples
This function wraps the MoCluster (Multiple omics data integrative clustering) algorithm and provides standard output for 'getMoHeatmap()' and 'getConsensusMOIC()'.
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| data | List of matrices. | 
| N.clust | Number of clusters. | 
| type | Data type corresponding to the list of matrics, which can be gaussian, binomial or possion. | 
| ncomp | An integer value to indicate the number of components to calculate. To calculate more components requires longer computational time. | 
| method | A string value can be one of CPCA, GCCA and MCIA; CPCA by default. | 
| option | A string value could be one of c('lambda1', 'inertia', 'uniform') to indicate how the different matrices should be normalized. | 
| k | A numeric value to indicate the absolute number (if k >= 1) or the proportion (if 0 < k < 1) of non-zero coefficients for the variable loading vectors. It could be a single value or a vector has the same length as x so the sparsity of individual matrix could be different. | 
| center | A logical value to indicate if the variables should be centered. TRUE by default. | 
| scale | A logical value to indicate if the variables should be scaled. TRUE by default. | 
| clusterAlg | A string value to indicate the cluster algorithm for distance. | 
A list with the following components:
fit        an object returned by mbpca.
clust.res  a data.frame storing sample ID and corresponding clusters.
feat.res   the results of features selection process.
clust.dend a dendrogram of sample clustering.
mo.method  a string value indicating the method used for multi-omics integrative clustering.
Meng C, Helm D, Frejno M, Kuster B (2016). moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets. J Proteome Res, 15(3):755-765.
| 1 | # There is no example and please refer to vignette.
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