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# clonealign

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.

## Getting started

### Installation

clonealign is built using Google’s Tensorflow so requires installation of the R package tensorflow:

install.packages("tensorflow")
tensorflow::install_tensorflow(extra_packages ="tensorflow-probability", version="1.12.0")


Note that clonealign uses the Tensorflow probability library, requiring Tensorflow version >= 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")


### Usage

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"


## Paper

https://www.biorxiv.org/content/early/2018/06/11/344309

## Authors

Kieran R Campbell, University of British Columbia