"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
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:
If you are running R 4.0.0 or later version on MacOS Catalina and you see error
'wchar.h' file not found, you can try the method in this link:
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:
Check out our Wiki for developer's guide if you want to contribute! - Celda Development Coding Style Guide - Celda Development Robust and Efficient Code - Celda Development Rstudio configuration - FAQ on how to use celda - FAQ on package development
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