Clustering and Visualizing RNA-Seq Expression Data using Grade of Membership Models

Documentation for package ‘CountClust’ version 1.25.0

AbundanceGoM	GoM model fit for abundance data
BatchCorrectedCounts	Obtain Batch effect Corrected counts
compare_omega	Re-ordering cluster membership proportion matrices and Information calculation
compGoM	compGoM: compare GoM model fits across K or across different runs through log-likelihood, BIC and null loglikelihood
ex.counts	counts data for GTEx V6 Brain data for 200 genes
ExtractHighCorFeatures	Extracting most highly correlated genes with GoM topics/clusters
ExtractTopFeatures	Extracting top driving genes of GoM clusters
FitGoM	Run Grade of Membership (GoM) model with multiple starting points !
GTExV6Brain.FitGoM	GoM model fit for GTEx V6 Brain bulk-RNA data
handleNA	Deal with NAs in the dataset!
MouseDeng2014.FitGoM	GoM model fit for Deng et al 2014 single cell RNA-seq data on mouse
MouseJaitinSpleen.FitGoM	GoM model fit for Jaitin et al 2014 single cell RNA-seq data on mouse
nullmodel_GoM	Null models for Grade of Membership (GoM) cluster validation
RemoveSparseFeatures	Removes features with a lot of 0 counts
StructureGGplot	Struture plot using ggplot2