spDDB

A Comprehensive Benchmarking of Spatial Deconvolution and Domain Detection Methods across Diverse Tissues and Spatial Transcriptomic Technologies.

Paper: https://doi.org/10.64898/2026.05.11.724248

Installation

Clone the repository at https://github.com/Zafar-Lab/spDDB_datasets.github.io and create the required conda environments using the environment files provided in ./Environments/.

We recommend using the stable main branch.

git clone https://github.com/Zafar-Lab/spDDB.git
cd spDDB/Environments

conda env create -f SynthST.yml
conda activate SynthST

conda env create -f method_name.yml
conda activate method_name

What Computational Tasks Can spDDB Be Used For?

spDDB provides a comprehensive framework for:

Benchmarking spatial deconvolution methods.
Benchmarking spatial domain detection methods.
Evaluating spatial transcriptomics methods using a rich collection of metrics, including:
- Bivariate spatial metrics
- Cell-type shape characterization metrics
- Rare cell-type metrics
Simulating synthetic spatial transcriptomics datasets and cell-type proportions using SynthST.
Accessing a diverse repository of spatial transcriptomics datasets spanning multiple tissues, species, and technologies.

spDDB Dataset Repository

Synthetic datasets and benchmarking resources are available at:

https://zafar-lab.github.io/spDDB_datasets.github.io/

Contributing

We welcome bug reports, enhancement requests, and general questions through GitHub Issues.

For substantial contributions:

Fork the repository.
Create a feature branch.
Commit your changes with clear commit messages.
Submit a pull request for review.

Citation

If you use spDDB in your research, please cite:

Ajita Shree, Aditya V*, Tanush Kumar*, and Hamim Zafar.

A Comprehensive Benchmarking of Spatial Deconvolution and Domain Detection Methods across Diverse Tissues and Spatial Transcriptomic Technologies.

* Equal contribution.

https://doi.org/10.64898/2026.05.11.724248