Welcome to Spateo documentation

Cells do not live in a vacuum, but in a milieu defined by cell–cell communication that can be quantified via recent advances in spatial transcriptomics.

Here we present spateo, a open source framework that welcomes community contributions for quantitative spatiotemporal modeling of spatial transcriptomics. Leveraging the ultra-high spatial-resolution, large field of view and high RNA capture sensitivity of Stereo-seq, spateo enables single cell resolution spatial transcriptomics via nuclei-staining and RNA signal based cell segmentation. Spateo also delivers novel methods for spatially constrained clustering to identify continuous tissue domains, spatial aware differential analyses to reveal spatial gene expression hotspots and modules, as well as the intricate ligand-receptor interactions.

Importantly, spateo is equipped with sophisticated methods for building whole-body 3D models of embryogenesis by leveraging serial profilings of drosophila embryos across different stages. Spateo thus enables us to evolve from the reductionism of single cells to the holisticism of tissues and organs, heralding a paradigm shift in moving toward studying the ecology of tissue and organ while still offering us the opportunity to reveal associated molecular mechanisms.


Spateo is currently in beta. Developers are hard at work implementing new features, fixing bugs, and improving the overall user experience. If you have a feature request and/or would like to report a bug, please feel free to open an issue on Github.

Spateo will not be uploaded to Pypi until its first stable release. In the meantime, spateo may be installed directly from GitHub using these instructions.


Click here to view a brief spateo installation guide.


End-to-end tutorials showcasing key features in the package.


Technical information on algorithms and tools provided by spateo.

API reference

Detailed descriptions of spateo API and internals.


Ask questions, report bugs, and contribute to spateo at our GitHub repository.

This documentation was heavily inspired and adapted from the scvi-tools documentation. Go check them out!