senoquant

napari plugin for spatial quantification of senescence markers in tissue imaging

SenoQuant Contributors

SenoQuant is a versatile napari plugin designed for comprehensive, accurate, and unbiased spatial quantification and prediction of senescence markers across diverse tissue contexts.

Video tutorial

Watch the tutorial for a guided walkthrough of the main workflow:

Features

Read microscopy formats via BioIO, including OME-TIFF, OME-ZARR, ND2, LIF, CZI, and more.
Segment nuclei and cytoplasm with built-in models, including StarDist, Cellpose SAM, and morphological operations.
Detect punctate spots with built-in detectors.
Run prediction models for senescence-associated feature maps in a dedicated Prediction tab (includes demo_model placeholder).
Discover and download compatible SenNet datasets from the SenNet Portal tab.
Quantify marker intensity, morphology, spot counts, and spot colocalization.
Generate visualization outputs from quantification tables (Spatial Plot, UMAP, Double Expression, and Neighborhood Enrichment).
Run batch workflows across folders with multi-scene support.
Save/load reusable Segmentation, Spots, and Batch settings for reproducibility.

Installation

Installer (recommended - please also use Google Chrome to download)

Windows

Download the Windows installer (.exe) from the latest release under "Assets."

macOS

Download the macOS installer (.pkg) from the latest release under "Assets."

Linux

Installer support for Linux is under construction.

Note 1: The installer may trigger security warnings on macOS and Windows (especially when using Microsoft Edge). This is expected for open-source software distributed outside of official app stores. Follow the system prompts to allow installation. On Windows, you may need to click "More info" and then "Run anyway" on the warning popup. On macOS, when you see the warning that "Apple could not verify...," click "Done" to dismiss, then go to System Settings > Privacy & Security and click "Open Anyway" for the SenoQuant installer.

Note 2: In some corporate environments, security policies may block the installer's access to folders or the Internet. If you encounter issues, try running the installer with administrator privileges (right-click > "Run as administrator" on Windows) and ensure that your firewall allows the installer to access the Internet to download dependencies.

Manual installation

For conda/pip/uv setup, see the developer installation guide.

Quick start

Use the documentation workflow for the most up-to-date instructions.

Start with the installation guide.
Follow the quick start guide.
Then use tab-specific guides for SenNet Portal, segmentation, spots, prediction, quantification, visualization, batch, and settings.

Documentation

Full documentation is available at https://haamsree.github.io/senoquant/.

Development

See the contributing guide for development setup instructions.

How to cite

If you use SenoQuant in your research, please cite it using the metadata in CITATION.cff.

On GitHub, open the repository page and click Cite this repository in the right sidebar to copy a formatted citation.

Acknowledgements

SenoQuant builds on and integrates excellent open-source projects.

Version:

1.0.0b11.post5

Last updated:

2026-04-14

First released:

2026-01-31

License:

BSD-3-Clause

Supported data:

Information not submitted

Plugin type:

Open extension:

Save extension:

Python versions supported:

3.11

Operating system:

Information not submitted

Requirements:

bioio>=3.2.0
bioio-bioformats>=1.3.0
bioio-czi>=2.4.2
bioio-dv>=1.2.0
bioio-imageio>=1.3.0
bioio-lif>=1.4.0
bioio-nd2>=1.6.0
bioio-ome-tiff>=1.4.0
bioio-ome-zarr>=3.2.1
bioio-sldy>=1.4.0
bioio-tifffile>=1.3.0
scyjava>=1.12.0
numpy<=1.26.4,>=1.23
pandas>=2.0
cellpose==4.0.8
onnx>=1.16
onnxruntime>=1.21.0; platform_system == "Darwin"
onnxruntime-gpu>=1.21.0; platform_system != "Darwin"
openpyxl>=3.1
huggingface_hub>=0.23.0
scikit-image<0.25,>=0.22
PyWavelets>=1.5
scipy>=1.8
senoquant-stardist-ext>=0.1.1
dask[array]>=2024.4
dask[distributed]>=2024.4
fsspec>=2024.4
smbprotocol>=1.13
matplotlib>=3.8
umap-learn>=0.5
igraph>=0.11
leidenalg>=0.10
jsonschema>=3.2
pathvalidate>=3.2
zarr>=3
globus-cli>=3.1.4
atlas-consortia-clt>=1.0.4
napari[all]; extra == "all"
bioio>=3.2.0; extra == "all"
bioio-bioformats>=1.3.0; extra == "all"
bioio-czi>=2.4.2; extra == "all"
bioio-dv>=1.2.0; extra == "all"
bioio-imageio>=1.3.0; extra == "all"
bioio-lif>=1.4.0; extra == "all"
bioio-nd2>=1.6.0; extra == "all"
bioio-ome-tiff>=1.4.0; extra == "all"
bioio-ome-zarr>=3.2.1; extra == "all"
bioio-sldy>=1.4.0; extra == "all"
bioio-tifffile>=1.3.0; extra == "all"
scyjava>=1.12.0; extra == "all"
numpy<=1.26.4,>=1.23; extra == "all"
pandas>=2.0; extra == "all"
cellpose==4.0.8; extra == "all"
onnx>=1.16; extra == "all"
onnxruntime>=1.21.0; platform_system == "Darwin" and extra == "all"
onnxruntime-gpu>=1.21.0; platform_system != "Darwin" and extra == "all"
openpyxl>=3.1; extra == "all"
huggingface_hub>=0.23.0; extra == "all"
scikit-image<0.25,>=0.22; extra == "all"
PyWavelets>=1.5; extra == "all"
scipy>=1.8; extra == "all"
senoquant-stardist-ext>=0.1.1; extra == "all"
dask[array]>=2024.4; extra == "all"
dask[distributed]>=2024.4; extra == "all"
fsspec>=2024.4; extra == "all"
smbprotocol>=1.13; extra == "all"
matplotlib>=3.8; extra == "all"
umap-learn>=0.5; extra == "all"
igraph>=0.11; extra == "all"
leidenalg>=0.10; extra == "all"
jsonschema>=3.2; extra == "all"
pathvalidate>=3.2; extra == "all"
zarr>=3; extra == "all"
globus-cli>=3.1.4; extra == "all"
atlas-consortia-clt>=1.0.4; extra == "all"