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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.


The Bisque R package is available on CRAN


as well as Bioconda

conda install r-bisquerna

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:

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: