FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking

TL;DR

FlowCyt introduces the first comprehensive benchmark dataset for multi-class flow cytometry cell classification, enabling standardized evaluation of various machine learning methods including GNNs, with insights into hematological cell phenotypes.

Contribution

This paper provides the first public, richly annotated flow cytometry dataset and benchmark for multi-class classification, facilitating development and assessment of novel single-cell analysis methods.

Findings

GNNs outperform other models by leveraging spatial relationships.

Semi-supervised learning improves classification accuracy.

Benchmark enables standardized evaluation of methods.

Abstract

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.