ExtractTopFeatures: Extracting top driving genes of GoM clusters

Description Usage Arguments Value Examples

View source: R/ExtractTopFeatures.R

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

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

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.

Value

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

Examples

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

Bioconductor-mirror/CountClust documentation built on May 29, 2017, 2:15 a.m.