The impacts of active and self-supervised learning on efficient annotation of single-cell expression data

Michael J. Geuenich (), Dae-won Gong and Kieran R. Campbell ()
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Michael J. Geuenich: Sinai Health System
Dae-won Gong: Sinai Health System
Kieran R. Campbell: Sinai Health System

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract A crucial step in the analysis of single-cell data is annotating cells to cell types and states. While a myriad of approaches has been proposed, manual labeling of cells to create training datasets remains tedious and time-consuming. In the field of machine learning, active and self-supervised learning methods have been proposed to improve the performance of a classifier while reducing both annotation time and label budget. However, the benefits of such strategies for single-cell annotation have yet to be evaluated in realistic settings. Here, we perform a comprehensive benchmarking of active and self-supervised labeling strategies across a range of single-cell technologies and cell type annotation algorithms. We quantify the benefits of active learning and self-supervised strategies in the presence of cell type imbalance and variable similarity. We introduce adaptive reweighting, a heuristic procedure tailored to single-cell data—including a marker-aware version—that shows competitive performance with existing approaches. In addition, we demonstrate that having prior knowledge of cell type markers improves annotation accuracy. Finally, we summarize our findings into a set of recommendations for those implementing cell type annotation procedures or platforms. An R package implementing the heuristic approaches introduced in this work may be found at https://github.com/camlab-bioml/leader .

Date: 2024
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DOI: 10.1038/s41467-024-45198-y

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