cellassign automatically assigns single-cell RNA-seq data to known cell types across thousands of cells accounting for patient and batch specific effects. Information about a priori known markers cell types is provided as input to the model in the form of a (binary) marker gene by cell-type matrix.
cellassign then probabilistically assigns each cell to a cell type, removing subjective biases from typical unsupervised clustering workflows.
Installing from GitHub
cellassign is built using Google's Tensorflow, and as such requires installation of the R package
install.packages("tensorflow") tensorflow::install_tensorflow(extra_packages='tensorflow-probability', version = "2.1.0")
Please ensure this installs version 2 of tensorflow. You can check this by calling
TensorFlow v2.1.0 (/usr/local/lib/python3.7/site-packages/tensorflow)
cellassign can then be installed from github:
install.packages("devtools") # If not already installed devtools::install_github("Irrationone/cellassign")
Installing from conda
With conda, install the current release version of
cellassign as follows:
conda install -c conda-forge -c bioconda r-cellassign
Package documentation can be found here. This includes the following vignettes:
cellassign requires the following inputs:
exprs_obj: Cell-by-gene matrix of raw counts (or SingleCellExperiment with
marker_gene_info: Binary gene-by-celltype marker gene matrix or list relating cell types to marker genes
s: Size factors
X: Design matrix for any patient/batch specific effects
The model can be run as follows:
cas <- cellassign(exprs_obj = gene_expression_data, marker_gene_info = marker_gene_info, s = s, X = X)
An example set of markers for the human tumour microenvironment can be loaded by calling
Please see the package vignette for details and caveats.
Code of Conduct
Please note that the 'cellassign' project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Allen W Zhang, University of British Columbia
Kieran R Campbell, University of British Columbia