"celda" stands for "CEllular Latent Dirichlet Allocation", which is a suite of Bayesian hierarchical models and supporting functions to perform gene and cell clustering for count data generated by single cell RNA-seq platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications. Celda has advantages over other clustering frameworks:

1. Celda can simultaneously cluster genes into transcriptional states and cells into subpopulations
2. Celda uses count-based Dirichlet-multinomial distributions so no additional normalization is required for 3' DGE single cell RNA-seq
3. These types of models have shown good performance with sparse data.

To install the beta release of celda via devtools:

library(devtools)
install_github("compbiomed/[email protected]")

Vignettes are available in the package.

An analysis example using celda with RNASeq via vignette('celda-analysis')

The v0.1 release of celda represents a useable implementation of the various celda clustering models. Please submit any usability issues or bugs to the issue tracker at https://github.com/compbiomed/celda