https://github.com/cozygene/bisque
An R toolkit for estimation of cell composition from bulk expression data
https://github.com/cozygene/bisque
Last synced: 15 days ago
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An R toolkit for estimation of cell composition from bulk expression data
- Host: GitHub
- URL: https://github.com/cozygene/bisque
- Owner: cozygene
- Created: 2019-03-19T21:34:54.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-08-11T13:10:05.000Z (over 3 years ago)
- Last Synced: 2024-11-09T10:38:30.442Z (6 months ago)
- Language: R
- Homepage:
- Size: 118 KB
- Stars: 68
- Watchers: 5
- Forks: 20
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-deconvolution - Bisque - cell information ([Jew et al 2020](https://www.nature.com/articles/s41467-020-15816-6); tags: single-cell; transcriptomics; rna-seq; bulk). (Methods)
README
# Bisque
[](https://travis-ci.org/cozygene/bisque)
[](https://codecov.io/gh/cozygene/bisque)
[](http://bioconda.github.io/recipes/r-bisquerna/README.html)
[](https://CRAN.R-project.org/package=BisqueRNA)
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
```r
install.packages("BisqueRNA")
```
as well as Bioconda
```
conda install r-bisquerna
```
The package can also be installed from the GitHub repository
```r
devtools::install_github("cozygene/bisque")
```
## Getting Started
You can load Bisque as follows:
```r
library(BisqueRNA)
```
The two modes of operation described above are called as follows:
```r
res <- BisqueRNA::ReferenceBasedDecomposition(bulk.eset, sc.eset, markers)
```
```r
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:
```r
browseVignettes("BisqueRNA")
```