TL;DR
The framework integrates three modules: dual-reconstruction to fuse attribute-structure information, contrastive learning under label guidance to extract semantic similarities, and deep embedding clustering to enable iterative optimization.
ScFANCL addresses a key limitation of conventional contrastive learning by applying a cosine-similarity-based threshold to exclude cells of the same type from the negative pool, thereby preserving biological continuity within cell types while maintaining inter-cluster separation.
Experiments on three cross-patient scRNA-seq data sets demonstrate that PathoGraph achieves stable annotation performance across 32 directed reference-to-query transfer tasks, showing competitive and stable performance compared with representative marker-based, correlation-based, and model-based annotation methods.
Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at single-cell resolution, but accurate identification of cell subpopulations remains challenging because of the high dimensionality, sparsity, and dropout effects of scRNA-seq data. Existing deep learning-based clustering methods have shown promis...
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, shows strong robustness to domain shift and platform heterogeneity.
SaDGAE, an unsupervised deep graph autoencoder (GAE) framework that jointly models gene expression patterns and cell–cell relationships, achieves strong and competitive clustering performance, yielding biologically interpretable clusters and accurately recovering known marker gene patterns.
Experiments indicated that scDFVA outperformed several other competing methods, demonstrating that the method is beneficial in single-cell clustering.
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