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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.
- Introduction to clonealign Overview of
clonealign including data preparation, model fitting, plotting results, and advanced inference control
- Preparing copy number data for input to clonealign Instructions for taking region/range specific copy number profiles and converting them to gene and clone specific copy numbers for input to clonealign
clonealign is built using Google’s Tensorflow so requires installation of the R package
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
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
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"
clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers, Genome Biology 2019
Kieran R Campbell, University of British Columbia