Functionally Guided Graph Learning for Robust Cross-Patient Cell-Type Annotation in Single-Cell RNA Sequencing.

PRISM: Prior-enhanced Inference for Spatial Transcriptomic Cell Type Mapping

Abstract Motivation Cell type annotation in spatial transcriptomics (ST) is fundamental for deciphering complex tissue organization and spatially resolved biological processes. Most existing methods perform ST cell type annotation by transferring labels from single-cell RNA-seq (scRNA) data to ST data, but typically rely on weakly constrained representations that neglect structured spatial dependencies and treat marker gene selection as an isolated preprocessing step. This renders them vulnerable to substantial domain gaps as well as platform-specific noise, resulting in unstable predictions and limited biological interpretability. Results To address these issues, we propose Prior-enhanced Inference for Spatial Transcriptomic Cell Type Mapping (PRISM), a novel three-stage framework integrating biological prior construction, pseudo-label generation, and multi-level ST refinement. First, PRISM constructs a cross-domain biological prior to explicitly extract marker genes to enforce positive biological discriminability. Next, it adopts a prior-enhanced self-training strategy, where scRNA-trained ensembles generate reliable pseudo-label candidates for ST data, serving as a robust anchor for cross-domain adaptation. Finally, the framework consolidates high-quality ensemble predictions selected via metric-guided evaluation, encodes spatial information, and optimizes the model under dual-directional biological constraints. Extensive experiments on eleven ST datasets across six platforms, two species, and multiple tissue contexts validate PRISM. Specifically, on the five labeled benchmarks, PRISM shows strong overall performance under both Accuracy and Macro-F1 evaluation across brain and non-brain tissues. Moreover, under fully label-free settings, PRISM achieves the best overall composite rank across all datasets, demonstrating strong robustness to domain shift and platform heterogeneity. Availability and implementation PRISM is available at https://github.com/lilab-ai4s/PRISM and https://doi.org/10.5281/zenodo.20529683.

scLGGCL: Label-Guided Graph Contrastive Learning for Single-Cell Fusion Clustering.

A Hierarchical Multimodal Knowledge Graph for Neural Cell-Type-Specific Regulation Integrating Single-Cell Transcriptomics and Literature Evidence

The nervous system comprises highly diverse cell types governed by cell-type-specific molecular regulatory programs. However, regulatory evidence is scattered across unstructured literature and described using inconsistent cell-type nomenclature and granularity, hindering systematic integration and cross-study comparison. Here, we construct a neural-cell-centric multimodal knowledge graph that transforms fragmented regulatory evidence into a standardized, computable substrate. We establish a three-level hierarchical cell-type taxonomy anchored to the Cell Ontology (79 nodes), integrate two large-scale human brain single-cell transcriptomic datasets (over 4 million cells) to derive molecular fingerprints, and use a large language model to retain 25,812 curated regulatory evidence records from PubMed abstracts. The resulting Neo4j graph contains 41,532 directed relationships. For knowledge graph embedding, we export a deduplicated non-paper training subgraph containing 19,819 triples over 10,660 entities, supporting cell-type-specific link prediction that prioritizes candidate regulators and markers, illustrated here for microglia. This framework provides a structured basis for cross-study comparison, hypothesis generation and knowledge-guided reasoning in neural cell-type-specific regulation.

Specificity-driven cell-gene graph learning identifies rare cell states in single-cell and spatial transcriptomic data

Detecting rare cell populations that drive development, differentiation, and disease-associated transformation remains a central challenge in biology and medicine. Although these populations often represent promising targets for intervention, they are difficult to resolve from single-cell transcriptomic data because most methods rely on homophily-based cell–cell similarity, which can merge rare cells into dominant populations and mask their subtle transcriptional signatures. The challenge is further amplified in multi-sample analyses, where batch correction can dilute rare-cell-specific signals. Here, we present scFormer, a heterogeneous graph transformer (HGT) framework for sensitive and robust rare-cell discovery. scFormer constructs a Z-score-guided cell-gene heterogeneous graph in which highly specific marker genes serve as informational bridges, embedding rare-cell features directly into the graph topology rather than inferring them from global neighbors. This design provides a clear biological rationale for rare-cell recovery, as low-abundance cells can remain connected through shared high-specificity genes even when local cell–cell neighborhoods are sparse. An integrated optimization strategy jointly performs representation learning, clustering, and optional batch correction, enabling rare-cell discovery while preserving biological structure. Across 125 simulated and 18 real datasets, scFormer consistently achieved competitive or superior performance relative to existing approaches. Applied to diverse multi-sample single-cell and spatial transcriptomics datasets, scFormer recovered known but weakly represented populations and revealed previously obscured cell states, including proliferative club cells in the airway epithelium, revival stem cells during intestinal regeneration, and rare embryonic cell states from spatial transcriptomics. Overall, scFormer provides a unified framework for identifying biologically meaningful rare populations while mitigating batch effects in multi-sample datasets.

FloREN: Decoding Immune Regulatory Networks through Interpretable Graph Transformer Patient Representations

A Framework for Learning Over REgulatory-Embedding Networks (FloREN), a supervised and interpretable sample representation method that enables improved sample stratification and biomarker discovery and supports downstream analyses that found specific immune network mechanisms in immune-mediated inflammatory diseases (IMIDs).

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

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.