Baysor

Bayesian segmentation of imaging-based spatial transcriptomics data. Baysor finds cells from molecule positions and gene composition, with or without a prior segmentation. This is the native C++ release cpp-0.9.0.

Quick start

Linux x86-64 — download the release binary and run on your molecule table:

curl -fLO https://github.com/kharchenkolab/Baysor/releases/download/cpp-0.9.0/baysor-0.9.0-linux-x86_64.tar.gz
tar -xzf baysor-0.9.0-linux-x86_64.tar.gz
./baysor-0.9.0-linux-x86_64/bin/baysor run -m 30 -s 8 molecules.csv

Docker — from the directory containing molecules.csv:

docker run --rm -v "$PWD:/data" ghcr.io/kharchenkolab/baysor:0.9.0 run -m 30 -s 8 molecules.csv

The table needs x, y and gene columns (optional z for 3D). 30 molecules and radius 8 are examples, not universal settings.

Setting	What to choose
-m	Minimum molecules expected in a real cell; choose for your protocol.
-s / --scale	Approximate cell radius in coordinate units. Alternatively, pass a prior as the second input and set --prior-segmentation-confidence (default 0.2).
-c	TOML config file; explicit CLI flags override it.
-o	Output directory (default segmentation).
--threads	Worker threads; physical CPU cores by default.

Documentation

• Installation — macOS / Windows binaries, requirements, Docker and source builds
• Cell segmentation — choosing parameters, using a prior and inspecting results
• Xenium workflow and examples
• Performance — run time, memory and segmentation examples

The documentation is versioned per release. For the old Julia implementation, see the migration guide and archived v0.7.1 docs.

Citation

If you use Baysor in a publication, please cite:

Petukhov V, Xu RJ, Soldatov RA, Cadinu P, Khodosevich K, Moffitt JR & Kharchenko PV.
Cell segmentation in imaging-based spatial transcriptomics.
Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-01044-w