Description Usage Arguments Value Examples
View source: R/ExtractTopFeatures.R
This function uses relative gene expression profile of the GoM clusters and applies a KLdivergence based method to obtain a list of top features that drive each of the clusters.
1 2  ExtractTopFeatures(theta, top_features = 10, method = c("poisson",
"bernoulli"), options = c("min", "max"))

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. 
A matrix (K x top_features) which tabulates in kth row the top feature indices driving the cluster k.
1 2 3 4  data("MouseDeng2014.FitGoM")
theta_mat < MouseDeng2014.FitGoM$clust_6$theta;
top_features < ExtractTopFeatures(theta_mat, top_features=100,
method="poisson", options="min");

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