spacedeconv is a unified interface to 31 deconvolution tools with focus on spatial transcriptomics datasets. The package is able to directly estimate celltype proportions of immune cells and can deconvolute any celltype if an annotated single-cell reference dataset is available.

⬇️ Installation

spacedeconv is available from GitHub only. We recommend installing trough the pak package manager:

# install the pak package manager
install.packages("pak")

# recommended installation, deconvolution tools are installed on-demand
pak::pkg_install("omnideconv/spacedeconv")

# full installation including all deconvolution tools
pak::pkg_install("omnideconv/spacedeconv", dependencies=TRUE)

✨ Features

unified access to 31 deconvolution tools
direct deconvolution of immune cells
compute custom reference signatures to deconvolute any celltype
flexible visualization functions
resource optimization
Pathway and Transcription Factor analysis (decoupleR integration)
Ligand-Receptor analysis
easy integration into spatial transcriptomics workflows

💾 Data requirements

Spatial transcriptomics data: SpatialExperiment

Single-cell data with cell-type annotation: SingleCellExperiment (recommended), anndata or Seurat

🧑‍💻 Usage

The main workflow consists of:

Reference signature computation using annotated single-cell data
Deconvolution
Visualization

1. Build a Signature Matrix

Build a cell type specific signature matrix from annotated single-cell reference data.

signature <- spacedeconv::build_model(
  single_cell_object,
  cell_type_col = "celltype_major",
  method = "spotlight",
  assay_sc="cpm"
)

2. Deconvolution

While some methods are able to directly estimate immune cell abundances other tools require a custom reference signature computed in step 1).

result <- spacedeconv::deconvolute(
  spatial_object,
  signature,
  method = "spotlight"
)

3. Visualization

spacedeconv includes highly-flexible visualization functions. A full explanation of all visualization options can be found in the visualization vignette.

plot_celltype(spe, cell_type="spotlight_B.cells")

💡 Additional Requirements

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)
SpatialExperiment requires magick to be installed: sudo apt-get install libmagick++-dev

Available methods, Licenses, Citations

Note that, while spacedeconv 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.

Constantin Zackl, Maria Zopoglou, Reto Stauffer, Markus Ausserhofer, Marieke E. Ijsselsteijn, Gregor Sturm, Noel Filipe da Cunha Carvalho de Miranda, Francesca Finotello. spacedeconv: deconvolution of tissue architecture from spatial transcriptomics, PREPRINT available at Research Square https://doi.org/10.21203/rs.3.rs-5102166/v1

First-gen (immunedeconv)	First-gen mouse (immunedeconv)	Second-gen (omnideconv + spatial Methods)

References

Method	signature	licence	citation
omnideconv	✔️	GPL-3	Citation will follow
immunedeconv	❌	BSD	Sturm, G., Finotello, F., Petitprez, F., Zhang, J. D., Baumbach, J., Fridman, W. H., …, List, M., Aneichyk, T. (2019). Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology. Bioinformatics, 35(14), i436-i445.
spatialDWLS	✔️	MIT	Dong R, Yuan GC. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 2021 May 10;22(1):145. doi: 10.1186/s13059-021-02362-7
cell2location	✔️	Apache-2.0	Kleshchevnikov, V., Shmatko, A., Dann, E. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol (2022). https://doi.org/10.1038/s41587-021-01139-4
SPOTlight	✔️	GPL 3.0	Elosua-Bayes M, Nieto P, Mereu E, Gut I, Heyn H (2021): SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res 49(9):e50. doi:10.1093/nar/gkab043.
RCTD	✔️	GPL 3.0	Cable, D.M., Murray, E., Zou, L.S. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol 40, 517–526 (2022). https://doi.org/10.1038/s41587-021-00830-w
CARD	✔️	GPL-3.0	Ying Ma, and Xiang Zhou (2022). Spatially informed cell type deconvolution for spatial transcriptomics. https://doi.org/10.1038/s41587-022-01273-7

🚀 spacedeconv