Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction

Fatemeh Nassajian Mojarrad, Lorenzo Bini, Thomas Matthes, Stephane Marchand-Maillet

Keywords: Flow cytometry, Hierarchical classification, Graph neural network

TL;DR: This study enhances Graph Neural Networks with hierarchical prior knowledge to improve single-cell classification of tabular data in hematologic samples, significantly boosting performance by capturing crucial biological relationships.

Abstract: In the complex landscape of hematologic samples such as peripheral blood or bone marrow derived from flow cytometry (FC) data, cell-level prediction presents profound challenges. This work explores injecting hierarchical prior knowledge into graph neural networks (GNNs) for single-cell multi-class classification of tabular cellular data. By representing the data as graphs and encoding hierarchical relationships between classes, we propose our hierarchical plug-in method to be applied to several GNN models, namely, FCHC-GNN, and effectively designed to capture neighborhood information crucial for single-cell FC domain. Extensive experiments on our cohort of 19 distinct patients, demonstrate that incorporating hierarchical biological constraints boosts performance significantly across multiple metrics compared to baseline GNNs without such priors. The proposed approach highlights the importance of structured inductive biases for gaining improved generalization in complex biological prediction tasks.

Submission Number: 19