Single-Cell Masked Autoencoder: An Accurate and Interpretable Automated Immunophenotyper

Abstract

High-throughput single-cell cytometry data are crucial for understanding the immune system’s role in diseases and treatment response. However, the prevailing methods used for analyzing cytometry data, specifically manual gating and clustering methods, have certain limitations with scalability, robustness, and accuracy. In this study, we propose a single-cell masked autoencoder (scMAE), which offers an automated solution for immunophenotyping tasks such as cell type prediction. Our model aims to preserve the cell type definitions designed by the user, making interpretation and cross-study comparisons more accessible. The scMAE model follows a pre-train and fine-tune paradigm. During pre-training, scMAE utilizes Masked Single-cell Modelling (MScM) to learn relationships between protein markers in immune cells without the need for prior labeling information. Subsequently, the scMAE is fine-tuned on multiple specialized tasks, using a smaller designated portion of labeled data. Through evaluation experiments, we demonstrated that the pre-trained scMAE overcomes limitations of manual gating and clustering methods, providing accurate and interpretable cellular immunophenotyping. The introduction of scMAE represents a significant advancement in immunology research, enabling prediction and interpretation of cellular-level in immune disease.