MethyAnno: An Interpretable Automated Annotation Method Leveraging Multi‐Scale Information and Metric Learning Framework for scDNAm Data
TL;DR
MethyAnno is proposed as an interpretable deep metric learning framework that leverages multi‐scale information for accurate cell type annotation of scDNAm data and establishes MethyAnno as a robust and biologically interpretable tool for accurate cell type annotation and downstream epigenetic analysis.
Abstract
ABSTRACT Single‐cell DNA methylation (scDNAm) sequencing provides unique insights into epigenetic heterogeneity and cell‐specific regulatory landscapes. However, accurate cell type annotation for scDNAm data remains challenging, as the distinct data distribution of scDNAm hinders the adaptation of annotation methods from other omics, and specialized annotation tools for scDNAm are currently lacking. Here, MethyAnno is proposed as an interpretable deep metric learning framework that leverages multi‐scale information for accurate cell type annotation of scDNAm data. Additionally, MethyAnno enables generalized category discovery in open‐set scenarios by utilizing density‐based clustering to automatically estimate the number of novel cell types, while simultaneously deciphering cell‐type‐specific epigenetic signatures for biological interpretability. Extensive experiments demonstrate that MethyAnno excels in cross‐dataset annotation and novel type discovery, showing exceptional robustness in few‐shot scenarios for rare cell types. Moreover, interpretability analysis in the human brain dataset correctly recovers the genetic link between Sst interneurons and epilepsy heritability, the association of OPC cells with Alzheimer's disease, as well as the regulatory role of Pvalb cells in synaptic plasticity. Taken together, these findings establish MethyAnno as a robust and biologically interpretable tool for accurate cell type annotation and downstream epigenetic analysis.