AliGater is intended as a rapid application development environment for high-throughput gating using pattern recognition functions and libraries. It provides a framework with basic gating functionality and then lets you build your own mathematical and pattern recognition functions in your strategies.
Typically you can explore and gate your data in a notebook environment then run thousands of gates using the same strategy. See sample notebooks (to be made) for examples.
Jupyter templates for evaluating QC objects (downsampled image views) using clustering will be made available.
Check out the documentation at https://aligater.readthedocs.io/en/latest/
* Several pre-built functions to make gating easier, from simple thresholding and fixed quadgates to 1-2d mixed gaussian modelling, dijkstras shortest path implementations and principal components.
* Support for crunching through folder hierarchies with sample files and linking folder names to your experiment.
* Straight-forward requirements to build your own pattern-recognition methods into the workflow.
* Easily integrates with methods of libraries like scikit-learn & scipy.
Download AliGater by running below:
git clone https://github.com/LudvigEk/aligater
cd aligater
After downloading, if you do not have cython installed in your environment it's recommended to pre-install it.
pip install cython
You should then be able to install AliGater by running:
pip install -e .
If you installed AliGater in a local environment such as conda, a few extra steps might be necessary to set up a jupyter kernel with the environment containing aligater. Below commands should work for a conda environment.
conda install -c anaconda ipykernel
python -m ipykernel install --user --name=aligater
Where --name=aligater is the name of the local conda environment where aligater was installed.
- Issue Tracker: github.com/LudvigEk/Aligater/issues
- Source Code: github.com/LudvigEk/Aligater
Lead dev; [email protected]
AliGater: a framework for the development of bioinformatic pipelines for large-scale, high-dimensional cytometry data
Bioinformatics Advances, Volume 3, Issue 1, 2023, vbad103, https://doi.org/10.1093/bioadv/vbad103
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Copyright (c) 2023 Ludvig Ekdahl
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