ExtractHighCorFeatures: Extracting most highly correlated genes with GoM...

In kkdey/CountClust: Clustering and Visualizing RNA-Seq Expression Data using Grade of Membership Models

Description

This function compares grades of membership profile for each cluster in GoM model fit with the data expression profile to identify genes that are mostly strongly associated with each topic.

This function compares grades of membership profile for each cluster in GoM model fit with the data expression profile to identify genes that are mostly strongly associated with each topic.

Usage

ExtractHighCorFeatures(omega, data, num_genes = 100)

ExtractHighCorFeatures(omega, data, num_genes = 100)

Arguments

omega	\boldsymbol{omega} matrix, the relative grades of memberships from the GoM model fitting (a N x K matrix where N is number of samples, K number of topics).
data	G x N matrix of the expression profile of genes across samples, where G is the number of features and N number of samples
num_genes	The number of top associated genes with each cluster. Defaults to 100
omega	\boldsymbol{omega} matrix, the relative grades of memberships from the GoM model fitting (a N x K matrix where N is number of samples, K number of topics).
data	G x N matrix of the expression profile of genes across samples, where G is the number of features and N number of samples
num_genes	The number of top associated genes with each cluster. Defaults to 100

Value

A list containing two items - a K x num_genes matrix of the top strongly associated/correlated indices/features for K clusters, and another K x num_genes matrix of the absolute values of the correlations.

A list containing two items - a K x num_genes matrix of the top strongly associated/correlated indices/features for K clusters, and another K x num_genes matrix of the absolute values of the correlations.

Examples

data("MouseDeng2014.FitGoM")
omega_mat <- MouseDeng2014.FitGoM$clust_6$omega;
read.data1 = function() {
    x = tempfile()
   download.file('https://cdn.rawgit.com/kkdey/singleCellRNASeqMouseDeng2014/master/data/Deng2014MouseEsc.rda', destfile=x, quiet=TRUE)
   z = get(load((x)))
   return(z)
}
Deng2014MouseESC <- read.data1()
deng.counts <- Biobase::exprs(Deng2014MouseESC)
out <- ExtractHighCorFeatures(omega_mat, deng.counts, num_genes=10)

kkdey/CountClust documentation built on Jan. 17, 2021, 5:32 p.m.