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.
See the website for more details as well as the introductory vignette.
clonealign
is built using Google’s Tensorflow so requires installation of the R package tensorflow
:
install.packages("tensorflow")
tensorflow::install_tensorflow()
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
print(head(cal$clone))
[1] "B" "C" "C" "B" "C" "B"