exprmclust {TSCAN} | R Documentation

## exprmclust

### Description

Perform model-based clustering on expression values

### Usage

    exprmclust(data, clusternum = 2:9, modelNames = "VVV", reduce = T)

### Arguments

`data` | The raw single_cell data, which is a numeric matrix or data.frame. Rows represent genes/features and columns represent single cells.
`clusternum` | An integer vector specifying all possible cluster numbers. The best cluster number will be picked using BIC. The minimum value should be two other
`modelNames` | model to be used in model-based clustering. By default "ellipsoidal, varying volume, shape, and orientation" is used.
`reduce` | Whether to perform the PCA on the expression data.

### Details

By default, this function first uses principal component analysis (PCA) to reduce dimensionality of original data. It then performs model-based clustering on the transformed expression values. A minimum-spanning-tree is constructed to link the cluster centers. The clustering results will be used for TSCAN ordering.

### Value

if more than one cluster detected, a list containing

- pcareduceres Numeric matrix containing the transformed expression values after PCA.

- MSTtree igraph object which is the result of constructing MST.

- clusterid A named vector specifying which cluster the cells belong to.

- clucenter Numeric matrix of the cluster centers.

if only one cluster detected, a list containing

- pcareduceres Numeric matrix containing the transformed expression values after PCA.

### Author(s)

Zhicheng Ji, Hongkai Ji <zji4@zji4.edu>

### References

Fraley, C., & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611-631.

### Examples

    data(lpsdata)
    procdata <- preprocess(lpsdata)
    exprmclust(procdata)

[Package TSCAN version 1.18.0 Index]