ScanNet: Single-cell annotation informed by transcriptional regulation Network via iterative heterogeneous graph learning

Yongyu Long, Wenhao Zhang, Lan Cao, Xiaobing Huang and Ying Wang

PLOS Computational Biology, 2026, vol. 22, issue 8, 1-24

Abstract: 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.Author summary: Accurate cell type annotation is essential for deciphering cellular heterogeneity. Most existing annotating methods are based on gene expression profile from scRNA-seq data. However, behind the observed gene expression profile, gene regulation is the essential mechanism to drive the transcriptional progress, which offer more intrinsic biological insight. While most current annotation approaches don’t incorporate regulatory information, limiting their capacity to capture the mechanistic basis of cell states. Therefore, we incorporate transcriptional regulatory networks (TRNs) as prior knowledge to assist more accurate cell type annotation. We introduce ScanNet, an iterative heterogenous graph learning framework that incorporates TRNs as both prior knowledge and structural constraints to capture biologically interpretable, cell-type-specific characteristics. In benchmark evaluations with ten baselines on multiple datasets, ScanNet shows (1) superior annotation performance on scRNA-seq data, (2) robust performance across seven sequencing platforms, (3) capability to identify novel cellular populations beyond existing annotations, and (4) effective transferability to scATAC‑seq data.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014602 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 14602&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014602

DOI: 10.1371/journal.pcbi.1014602

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().