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

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 th

SUBMITTER: Geuenich MJ 

PROVIDER: S-EPMC10837127 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature