"celda" stands for "CEllular Latent Dirichlet Allocation". It 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. This package also includes a method called DecontX which can be used to estimate and remove contamination in single cell genomic data.
To install the latest stable release of celda from Bioconductor (requires R version >= 3.6):
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("celda")
To install the development version (R >= 3.6) of celda from GitHub using devtools:
library(devtools)
install_github("campbio/celda")
NOTE For MAC OSX users, devtools::install_github() requires installation of libgit2. This can be installed via homebrew:
brew install libgit2
Also, if you receive installation errors when Rcpp is being installed and compiled, try following the steps outlined here to solve the issue:
https://thecoatlessprofessor.com/programming/cpp/r-compiler-tools-for-rcpp-on-macos/
NOTE If you are trying to install celda using Rstudio and get this error: could not find tools necessary to compile a package, you can try this:
options(buildtools.check = function(action) TRUE)
To build the vignettes for Celda and DecontX during installation from GitHub, use the following command:
library(devtools)
install_github("campbio/celda", build_vignettes = TRUE)
Note that installation may take an extra 5-10 minutes for building of the vignettes. The Celda and DecontX vignettes can then be accessed via the following commands:
vignette("celda")
vignette("decontX")
Check out our Wiki for developer's guide if you want to contribute!