This repository contains an implementation of automated gating algorithms with Mondrian processes for flow cytometry data described in our paper: Bayesian Trees for Automated Cytometry Data Analysis (Disi Ji, Eric Nalisnick , Yu Qian, Richard H. Scheuermann, and Padhraic Smyth)
We develop a novel Bayesian approach for automated gating that classifies cells into different types by combining cell-level marker measurements with an informative prior. The Bayesian approach allows for the incorporation of biologically-meaningful prior information that captures the domain expertise of human experts. The inference algorithm results in a hierarchically-structured classification of individual cells in a manner that mimics the tree-structured recursive process of manual gating, making the results readily interpretable.
If you use this repository in your research, please cite the following paper:
"Bayesian Trees for Automated Cytometry Data Analysis" (PDF).
@inproceedings{ji2018bayesian,
title={Bayesian Trees for Automated Cytometry Data Analysis},
author={Ji, Disi and Nalisnick, Eric and Qian, Yu and Scheuermann, Richard H and Smyth, Padhraic},
booktitle={Machine Learning for Healthcare Conference},
pages={465--483},
year={2018}
}
This work is released under the MIT License. Please submit an issue to report bugs or request changes. Contact Disi Ji ✉️ for any questions or comments.
This work was supported in part by the National Center For Advancing Translational Sciences of the National Institutes of Health [U01TR001801]; and by the National Science Foundation [IIS1320527]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Science Foundation.