Accurate cell type annotation is essential for revealing the dynamic, cell type-specific accessibility of regulatory elements from single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data. However, unlike the more mature single-cell RNA-sequencing (scRNA-seq) cell type annotation workflows, scATAC-seq cell type annotation remains challenging due to extreme sparsity, high dimensionality, the scarcity of labelled scATAC references, and pronounced batch effects across datasets. To enable annotation without relying on extensive scATAC labels, we introduce CARA, a cross-omics Bayesian framework that transfers cell type knowledge from scRNA-seq to scATAC-seq. CARA simultaneously enables cell type annotation, batch correction, and detection of cell types absent from the reference by combining pretraining on scRNA-seq data and semisupervised learning on target scATAC-seq data, along with distribution alignment, dynamic class weighting, and novel cell type detection. Across diverse benchmark datasets, CARA consistently outperforms baseline methods, remaining robust to batch effects. CARA's cross-modal alignment and robust annotation strategy preserve fine-grained lineage structure, enabling reconstruction of the hematopoietic differentiation trajectory. Through multidimensional uncertainty and latent-space clustering, CARA identifies novel, rare, or aberrant populations outside the reference cell type space, providing candidates for further biological validation and perturbation. Using an omics-specific generative framework and distribution alignment, CARA delivers accurate knowledge transfer and detects novel cell types in single-cell DNA methylation data, demonstrating seamless extensibility to new modalities. Ultimately, CARA offers a powerful and flexible solution for cross-modal cell type annotation in complex single-cell settings, facilitating the discovery of novel cell types and mechanistic insight into cell type-specific regulation across diverse analyses.
Lan Cao, Wenhao Zhang, Feng Zhou et al.· Genome Research· 0 citations
Accurate annotation of cell types in single-cell transcriptome sequencing (scRNA-seq) data is critical for understanding cellular identities. The transcriptional regulatory networks (TRNs), which map the regulatory relationships between transcription factors (TFs) and their target genes (TGs), capture the molecular dependencies underlying transcriptional programs. However, most existing cell type annotation methods do not fully exploit this regulatory information. Therefore, we introduce ScanNet, a Single cell annotation method informed by transcriptional regulation Network, to integrate prior knowledge of TRN into data of gene expression and capture the cell-type-specific characteristics underlying TRN mechanism. TRN can be naturally represented as heterogeneous graphs consisting of two regulatory elements, TFs and TGs connected by directed edges, thereby encoding the regulatory dependencies that shape transcriptional programs and ultimately determine cellular identity. To leverage this structure, ScanNet introduces an iterative heterogeneous graph convolutional framework that learns both local and global cellular embeddings through a dual-channel encoder. The Regulation-level Encoder applies iterative heterogeneous graph convolution to capture local TF-TG regulatory interactions within TRN, while the Expression-level Encoder learns global cellular transcriptional states. By integrating the multiple-view representations, ScanNet can accurately annotate cell types. Comprehensive evaluations across eight scRNA-seq datasets spanning different species, sample scales, and sequencing platforms demonstrate that ScanNet consistently outperforms ten state-of-the-art cell type annotation methods. By embedding prior TRN structures into a heterogeneous graph, ScanNet also achieves robust performance in cross-platform cell type annotation and in identifying novel cell types under constrained structural information. Moreover, the ScanNet framework can be flexibly transferred to single-cell ATAC-seq (scATAC-seq) data by mapping chromatin accessibility to gene level, where it achieves superior performance compared to existing annotation tools. Overall, ScanNet is a scalable, transferable, and mechanistically informed framework for accurate cell type annotation across diverse single-cell data modalities.
Yongyu Long, Wenhao Zhang, Lan Cao et al.· PLoS Computational Biology· 0 citations
