HemaGraph: Breaking Barriers in Hematologic Single Cell Classification
  with Graph Attention
Lorenzo Bini Fatemeh Nassajian Mojarrad Thomas Matthes Stéphane Marchand-Maillet
In the realm of hematologic cell populations classification, the intricate
patterns within flow cytometry data necessitate advanced analytical tools. This
paper presents 'HemaGraph', a novel framework based on Graph Attention Networks
(GATs) for single-cell multi-class classification of hematological cells from
flow cytometry data. Harnessing the power of GATs, our method captures subtle
cell relationships, offering highly accurate patient profiling. Based on
evaluation of data from 30 patients, HemaGraph demonstrates classification
performance across five different cell classes, outperforming traditional
methodologies and state-of-the-art methods. Moreover, the uniqueness of this
framework lies in the training and testing phase of HemaGraph, where it has
been applied for extremely large graphs, containing up to hundreds of thousands
of nodes and two million edges, to detect low frequency cell populations (e.g.
0.01% for one population), with accuracies reaching 98%. Our findings
underscore the potential of HemaGraph in improving hematoligic multi-class
classification, paving the way for patient-personalized interventions. To the
best of our knowledge, this is the first effort to use GATs, and Graph Neural
Networks (GNNs) in general, to classify cell populations from single-cell flow
cytometry data. We envision applying this method to single-cell data from
larger cohort of patients and on other hematologic diseases.