FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class
  Classification in Flow Cytometry Benchmarking
Lorenzo Bini Fatemeh Nassajian Mojarrad Margarita Liarou Thomas Matthes Stéphane Marchand-Maillet
This paper presents FlowCyt, the first comprehensive benchmark for
multi-class single-cell classification in flow cytometry data. The dataset
comprises bone marrow samples from 30 patients, with each cell characterized by
twelve markers. Ground truth labels identify five hematological cell types: T
lymphocytes, B lymphocytes, Monocytes, Mast cells, and Hematopoietic
Stem/Progenitor Cells (HSPCs). Experiments utilize supervised inductive
learning and semi-supervised transductive learning on up to 1 million cells per
patient. Baseline methods include Gaussian Mixture Models, XGBoost, Random
Forests, Deep Neural Networks, and Graph Neural Networks (GNNs). GNNs
demonstrate superior performance by exploiting spatial relationships in
graph-encoded data. The benchmark allows standardized evaluation of clinically
relevant classification tasks, along with exploratory analyses to gain insights
into hematological cell phenotypes. This represents the first public flow
cytometry benchmark with a richly annotated, heterogeneous dataset. It will
empower the development and rigorous assessment of novel methodologies for
single-cell analysis.