omnideconv

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The goal of omnideconv is to unify second generation cell-type deconvolution methods in an R package.

Installation

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)

Upon the first loading, miniconda will be installed if not already present. A dedicated conda environment will be created to host the python-based methods.

Available methods

The methods currently implemented in omnideconv are:

  • AutoGeneS (“autogenes”)
  • Bisque (“bisque”)
  • BayesPrism (“bayesprism”)
  • BSeq-sc (“bseqsc”)
  • CDSeq (“cdseq”)
  • CIBERSORTx (“cibersortx”)
  • CPM (“cpm”)
  • DWLS (“dwls”)
  • MOMF (“momf”)
  • MuSiC (“music”)
  • Scaden (“scaden”)
  • SCDC (“scdc”)

General usage

All the deconvolution methods included in omnideconv can be run in one step, trough the function deconvolute, which takes in input the matrix of bulk RNAseq to be deconvolved (bulk_gene_expression), along with the training single cell expression matrix (single_cell_object) with the cell type annotations and sample information.

deconvolution <- omnideconv::deconvolute(bulk_gene_expression, method,
                                         single_cell_object, cell_type_annotations, batch_ids)

Signature matrix/model building

The methods AutoGeneS, BSeq-Sc, DWLS, CIBERSORTx, MOMF and Scaden first optimize their internal model, for example building a signature matrix, and then use this model to perform deconvolution. For these methods, the build_model function can be used. The obtained model can then be given in input to the deconvolute function, omitting the single cell data.

signature <- omnideconv::build_model(single_cell_object, cell_type_annotations,
                                     batch_ids, method, bulk_gene_expression)

deconvolution <- omnideconv::deconvolute(bulk_gene_expression, signature)

The deconvolute function returns a sample x cell type matrix with the estimated cell fractions

Input data

Different methods have different requirements in terms of input data. This list has been compiled considering the methods documentation, described data procssing or authors recommendation

Method Single cell normalization Bulk normalization
AutogeneS CPM TPM
BayesPrism Counts Counts
Bisque Counts Counts
Bseq-Sc Counts TPM
CDseqR Counts Counts
CIBERSORTx CPM TPM
CPM Counts Counts
DWLS Counts TPM
MOMF Counts Counts
MuSiC Counts TPM
Scaden Counts TPM
SCDC Counts TPM

Learn More

For more information and an example workflow see the vignette of this package.

Requirements

Most methods do not require additional software/tokens, but there are a few exceptions:

  • A working version of Docker or Singularity 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)

Available methods, Licenses, Citations

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.

Benchmarking second-generation methods for cell-type deconvolution of transcriptomic data. Dietrich, Alexander and Merotto, Lorenzo and Pelz, Konstantin and Eder, Bernhard and Zackl, Constantin and Reinisch, Katharina and Edenhofer, Frank and Marini, Federico and Sturm, Gregor and List, Markus and Finotello, Francesca. (2024) https://doi.org/10.1101/2024.06.10.598226

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
BayesPrism free (GPL 3.0) Chu, T., Wang, Z., Pe’er, D. et al. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat Cancer 3, 505–517 (2022). https://doi.org/10.1038/s43018-022-00356-3
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