clonealign assigns single-cell RNA-seq expression to cancer clones by probabilistically mapping RNA-seq to clone-specific copy number profiles using reparametrization gradient variational inference. This is particularly useful when clones have been inferred using ultra-shallow single-cell DNA-seq meaning SNV analysis is not possible.
clonealign is built using Google's Tensorflow so requires installation of the R package
install.packages("tensorflow") tensorflow::install_tensorflow(extra_packages ="tensorflow-probability", version="1.12.0")
clonealign uses the Tensorflow probability library, requiring
>= 1.12.0, which can be installed using the above.
clonealign can then be installed from github:
install.packages("devtools") # If not already installed install_github("kieranrcampbell/clonealign")
clonealign accepts either a cell-by-gene matrix of raw counts or a SingleCellExperiment with a
counts assay as gene expression input. It also requires a gene-by-clone matrix or
data.frame corresponding to the copy number of each gene in each clone. The cells are then assigned to their clones by calling
cal <- clonealign(gene_expression_data, # matrix or SingleCellExperiment copy_number_data) # matrix or data.frame print(cal)
A clonealign_fit for 200 cells, 100 genes, and 3 clones To access clone assignments, call x$clone To access ML parameter estimates, call x$ml_params
 "B" "C" "C" "B" "C" "B"
Kieran R Campbell, University of British Columbia