MINGLE: a mutual information-based interpretable framework for automatic cell type annotation in single-cell chromatin accessibility data

Single-cell chromatin accessibility sequencing (scCAS) has proven invaluable for investigating the intricate landscape of epigenomic heterogeneity. We propose MINGLE, a mutual information-based interpretable framework that leverages cellular similarities and topological structures for accurate cell type annotation of scCAS data. Additionally, we introduce a convex hull-based strategy to effectively identify novel cell types. Extensive experiments demonstrate MINGLE's superior annotation performance, particularly for rare and novel cell types, delivering valuable biological insights compared to existing methods. Moreover, MINGLE excels in cross-batch, cross-tissue, and cross-species scenarios, showing robustness to data imbalance and size, highlighting its versatility for complex annotation tasks.