An R toolkit for accurate and efficient estimation of cell composition (‘decomposition’) from bulk expression data with single-cell information.
Bisque provides two modes of operation:
Reference-based decomposition
This method utilizes single-cell data to decompose bulk expression.
We assume that both single-cell and bulk counts are measured from the same tissue.
Specifically, the cell composition of the labeled single-cell data should match the expected physiological composition.
While we don’t explicitly require matched samples, we expect having samples with both single-cell and bulk expression measured will provide more accurate results.
Marker-based decomposition
This method utilizes marker genes alone to decompose bulk expression when a reference profile is not available.
Single-cell data is not explicitly required but can be used to identify these marker genes.
This method captures relative abundances of a cell type across individuals. Note that these abundances are not proportions, so they cannot be compared between different cell types.
Installation
The Bisque R package is available on CRAN
install.packages("BisqueRNA")
as well as Bioconda
conda install r-bisquerna
The package can also be installed from the GitHub repository
devtools::install_github("cozygene/bisque")
Getting Started
You can load Bisque as follows:
library(BisqueRNA)
The two modes of operation described above are called as follows:
res <- BisqueRNA::ReferenceBasedDecomposition(bulk.eset, sc.eset, markers)
res <- BisqueRNA::MarkerBasedDecomposition(bulk.eset, markers)
Each method returns a list of results with estimated cell proportions/abundances stored in res$bulk.props.
To see examples of these methods on simulated data, check out the vignette:
Bisque
An R toolkit for accurate and efficient estimation of cell composition (‘decomposition’) from bulk expression data with single-cell information.
Bisque provides two modes of operation:
Reference-based decomposition
This method utilizes single-cell data to decompose bulk expression. We assume that both single-cell and bulk counts are measured from the same tissue. Specifically, the cell composition of the labeled single-cell data should match the expected physiological composition. While we don’t explicitly require matched samples, we expect having samples with both single-cell and bulk expression measured will provide more accurate results.
Marker-based decomposition
This method utilizes marker genes alone to decompose bulk expression when a reference profile is not available. Single-cell data is not explicitly required but can be used to identify these marker genes. This method captures relative abundances of a cell type across individuals. Note that these abundances are not proportions, so they cannot be compared between different cell types.
Installation
The Bisque R package is available on CRAN
as well as Bioconda
The package can also be installed from the GitHub repository
Getting Started
You can load Bisque as follows:
The two modes of operation described above are called as follows:
Each method returns a list of results with estimated cell proportions/abundances stored in
res$bulk.props.To see examples of these methods on simulated data, check out the vignette: