The goal of omnideconv is to unify second generation cell-type deconvolution methods in an R package.
There are two ways to install omnideconv:
- The minimal installation installs only the dependencies required for the basic functionalities. All deconvolution methods need to be installed on-demand.
- The complete installation installs all dependencies including all deconvolution methods. This may take a considerable time.
Since not all dependencies are on CRAN or Bioconductor, omnideconv is available from GitHub only.
We recommend installing it through the pak package manager:
# install the `pak` package manager
install.packages("pak")
# minimal installation
pak::pkg_install("omnideconv/omnideconv")
# complete installation, including Python dependencies
pak::pkg_install("omnideconv/omnideconv", dependencies = TRUE)
omnideconv::install_all_python()The main functions of this package are to build a signature matrix and deconvolute bulk RNA-seq data based on this signature matrix.
The basic workflow (in this example using the method “dwls”) is the following:
omnideconv::build_model(single_cell_data_1, cell_type_annotations_1,
method = "dwls")In this method, single_cell_data is a matrix of single cell RNA-seq data with genes in rows and cells in columns, cell_type_annotations a vector that contains an annotation for each cell in your single_cell_data, and the possible methods are:
- AutoGeneS (“autogenes”)
- Bisque (“bisque”)
- BSeq-sc (“bseqsc”)
- CDSeq (“cdseq”)
- CIBERSORTx (“cibersortx”)
- CPM (“cpm”)
- DWLS (“dwls”)
- MOMF (“momf”)
- MuSiC (“music”)
- Scaden (“scaden”)
- SCDC (“scdc”)
omnideconv::deconvolute(bulk, signature_matrix, method = "dwls")Here, bulk is your bulk RNA-seq data as a matrix with genes in rows and samples in column, signature_matrix is the signature matrix you created in the previous step and the method can again be one of the four methods listed above.
This is, what the cell type properties in your bulk RNA-seq data computed in the deconvolution step could look like:
| B | CD4 T | CD8 T | DC | Mono | NK | |
|---|---|---|---|---|---|---|
| HD30_PBMC_0 | 0.087 | 0.444 | 0.318 | 0.047 | 0.037 | 0.066 |
| HD30_PBMC_1 | 0.086 | 0.439 | 0.311 | 0.052 | 0.039 | 0.073 |
| HD30_PBMC_3 | 0.085 | 0.540 | 0.244 | 0.044 | 0.032 | 0.055 |
| HD30_PBMC_7 | 0.091 | 0.472 | 0.295 | 0.048 | 0.028 | 0.067 |
| HD31_PBMC_0 | 0.080 | 0.617 | 0.167 | 0.041 | 0.045 | 0.049 |
| HD31_PBMC_1 | 0.081 | 0.566 | 0.214 | 0.041 | 0.042 | 0.056 |
| HD31_PBMC_3 | 0.080 | 0.525 | 0.243 | 0.043 | 0.044 | 0.065 |
| HD31_PBMC_7 | 0.053 | 0.851 | 0.000 | 0.000 | 0.059 | 0.037 |
For more information and an example workflow see the vignette of this package.
Most methods do not require additional software/tokens, but there are a few exceptions:
- A working version of Docker is required for CIBERSORTx
- A token for CIBERSORTx is required from this website: https://cibersortx.stanford.edu/
- The CIBERSORT source code is required for BSeq-sc (see tutorial in ?omnideconv::bseqsc_config)
Note that, while omnideconv itself is free (GPL 3.0), you may need to obtain a license to use the individual methods. See the table below for more information. If you use this package in your work, please cite both our package and the method(s) you are using.
CITATION
| method | license | citation |
|---|---|---|
| AutoGeneS | free (MIT) | Aliee, H., & Theis, F. (2021). AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution. https://doi.org/10.1101/2020.02.21.940650 |
| Bisque | free (GPL 3.0) | Jew, B., Alvarez, M., Rahmani, E., Miao, Z., Ko, A., Garske, K. M., Sul, J. H., Pietiläinen, K. H., Pajukanta, P., & Halperin, E. (2020). Publisher Correction: Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nature Communications, 11(1), 2891. https://doi.org/10.1038/s41467-020-16607-9 |
| BSeq-sc | free (GPL 2.0) | Baron, M., Veres, A., Wolock, S. L., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S. S., Klein, A. M., Melton, D. A., & Yanai, I. (2016). A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. In Cell Systems (Vol. 3, Issue 4, pp. 346–360.e4). https://doi.org/10.1016/j.cels.2016.08.011 |
| CDSeq | free (GPL 3.0) | Kang, K., Huang, C., Li, Y. et al. CDSeqR: fast complete deconvolution for gene expression data from bulk tissues. BMC Bioinformatics 22, 262 (2021). https://doi.org/10.1186/s12859-021-04186-5 |
| CIBERSORTx | free for non-commerical use only | Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature Methods, 12(5), 453–457. https://doi.org/10.1038/nmeth.3337 |
| CPM | free (GPL 2.0) | Frishberg, A., Peshes-Yaloz, N., Cohn, O., Rosentul, D., Steuerman, Y., Valadarsky, L., Yankovitz, G., Mandelboim, M., Iraqi, F. A., Amit, I., Mayo, L., Bacharach, E., & Gat-Viks, I. (2019). Cell composition analysis of bulk genomics using single-cell data. Nature Methods, 16(4), 327–332. https://doi.org/10.1038/s41592-019-0355-5 |
| DWLS | free (GPL) | Tsoucas, D., Dong, R., Chen, H., Zhu, Q., Guo, G., & Yuan, G.-C. (2019). Accurate estimation of cell-type composition from gene expression data. Nature Communications, 10(1), 2975. https://doi.org/10.1038/s41467-019-10802-z |
| MOMF | free (GPL 3.0) | Xifang Sun, Shiquan Sun, and Sheng Yang. An efficient and flexible method for deconvoluting bulk RNAseq data with single-cell RNAseq data, 2019, DIO: 10.5281/zenodo.3373980 |
| MuSiC | free (GPL 3.0) | Wang, X., Park, J., Susztak, K., Zhang, N. R., & Li, M. (2019). Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nature Communications, 10(1), 380. https://doi.org/10.1038/s41467-018-08023-x |
| Scaden | free (MIT) | Menden, K., Marouf, M., Oller, S., Dalmia, A., Kloiber, K., Heutink, P., & Bonn, S. (n.d.). Deep-learning-based cell composition analysis from tissue expression profiles. https://doi.org/10.1101/659227 |
| SCDC | (MIT) | Dong, M., Thennavan, A., Urrutia, E., Li, Y., Perou, C. M., Zou, F., & Jiang, Y. (2020). SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbz166 |