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

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

## Documentation for package ‘CountClust’ version 1.25.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

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