ArticleGenome biology2025
MINGLE: a mutual information-based interpretable framework for automatic cell type annotation in single-cell chromatin accessibility data.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- MethyAnno: An Interpretable Automated Annotation Method Leveraging Multi-Scale Information and Metric Learning Framework for scDNAm Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- DiTSim: A Diffusion-Transformers Based Single-Cell ATAC-seq Data Simulator.Interdisciplinary sciences, computational life sciences · 2026Article
- High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND.Genome research · 2026Article
- Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation.PLoS computational biology · 2026Article
- MINGLE: a mutual information-based interpretable framework for automatic cell type annotation in single-cell chromatin accessibility data.Genome biology · 2025Article
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Authors and funding
3 authors.
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Abstract
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.
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