The abbott-features Task Collection is intented to be used in combination with the Fractal Analytics Platform maintained by the BioVisionCenter Zurich (co-founded by the Friedrich Miescher Institute and the University of Zurich).
The tasks in abbott-features are focused on extending Fractal's capabilities to extract features from (multiplexed) 3D image data.
| Task | Description | Passing |
|---|---|---|
| Calculate Cycle Registration Quality | Calculates image-based registration quality across multiplexed OME-Zarr datasets. | ✓ |
| Measure Features | Calculates morphology, intensity, distance, and colocalization features for objects in a 3D label image. | ✓ |
| Get Cellvoyager Time Decay | Calculates time-decay correction factors per ROI, channel and acquisition to correct for acquisition bias dependent intensity decay (aka imaging snake). | ✓ |
| Get Z Decay Models | Calculates z-decay correction models per channel label to correct intensity decay across z. | ✓ |
To install this task package on a Fractal server, get the whl in the Github release and use the local task collection. To install this package locally:
git clone https://github.com/pelkmanslab/abbott-features
cd abbott
pip install -e .
For development:
git clone https://github.com/pelkmanslab/abbott-features
cd abbott
pip install -e ".[dev]"
pre-commit install
to update manifest:
fractal-manifest create --package abbott_features --fractal-server-2-13
The code is based on zfish originally developed by Maks Hess and adapted to Fractal & maintained by Ruth Hornbachner.