ExtractTopFeatures {CountClust} | R Documentation

## Extracting top driving genes of GoM clusters

### Description

This function uses relative gene expression profile of the GoM clusters and applies a KL-divergence based method to obtain a list of top features that drive each of the clusters.

### Usage

    ExtractTopFeatures(theta, top_features = 10, method = c("poisson",
      "bernoulli"), options = c("min", "max"), shared = FALSE)

### Arguments

`theta` | \boldsymbol{theta} matrix, the relative gene expression profile of the GoM clusters (cluster probability distributions) from the GoM model fitting (a G x K matrix where G is number of features, K number of topics).
`top_features` | The top features in each cluster k that are selected based on the feature's ability to distinguish cluster k from cluster 1, …, K for all cluster k \ne l. Default: 10.
`method` | The underlying model assumed for KL divergence measurement. Two choices considered are "bernoulli" and "poisson". Default: poisson.
`options` | if "min", for each cluster k, we select features that maximize the minimum KL divergence of cluster k against all other clusters for each feature. If "max", we select features that maximize the maximum KL divergence of cluster k against all other clusters for each feature.
`shared` | if TRUE, then we report genes that can be highly expressed in more than one cluster. Else, we stick to only those genes that are highest expressed only in a specific cluster.

### Value

A matrix (K x top_features) which tabulates in k-th row the top feature indices driving the cluster k.

### Examples

    data("MouseDeng2014.FitGoM")
    theta_mat <- MouseDeng2014.FitGoM$clust_6$theta;
    top_features <- ExtractTopFeatures(theta_mat, top_features=100, method="poisson", options="min");
    top_features$indices
    top_features$scores

[Package CountClust version 1.10.1 Index]