Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
Journal of chemical information and modeling
Gradient-Guided Graph Contrastive Learning for Mass Spectrometry-Based Proteomics Clustering
Single-cell proteomic data generated by mass spectrometry-based technologies provide direct insights into cellular functional states and have become increasingly important for revealing cellular heterogeneity in complex biological systems. Accurate clustering of such data is essential for identifying functionally distinct cell subpopulations and understanding biological processes such as immune responses, tumor heterogeneity, and cell fate regulation. However, mass spectrometry-based single-cell proteomic data are often characterized by high dimensionality, measurement noise, technical bias, and complex nonlinear structures, which pose major challenges to conventional clustering methods. To address these issues, this study proposes a gradient-information-guided graph contrastive learning framework for single-cell proteomic clustering. The proposed method adaptively reconstructs intercellular relationship graphs through gradient-guided structure learning and introduces a gradient-weighted contrastive loss to alleviate the influence of false-negative samples. By better preserving similarity among biologically related cells, the framework learns more robust and biologically meaningful representations. Experimental results on multiple data sets demonstrate that the proposed method outperforms conventional clustering approaches and existing graph contrastive learning methods in terms of clustering accuracy, stability, and biological consistency. Overall, this work provides an effective framework for clustering mass spectrometry-based single-cell proteomic data and offers new insights into the application of graph contrastive learning in bioinformatics.
28 July 2026
Read appraisal →Functional & integrative genomics
A multi-scale graph frequency network for structural and functional region analysis in spatial transcriptomics
Spatial transcriptomics enables the systematic exploration of how gene expression patterns are organized within intact tissues, yet effective analysis remains difficult due to the complexity of spatial dependencies and multi-scale tissue architectures. Here, we present the Spatial Graph Frequency Network (SGFN), a deep learning framework that integrates graph signal processing, graph attention, and contrastive learning to jointly model spatial topology and molecular features. Central to SGFN is a frequency-domain enhancement module that decomposes spatial graphs into multi-scale spectral components using the Laplacian eigenbasis, complemented by adaptive wavelet denoising when the retained graph-frequency sequence length permits valid decomposition. Evaluation across diverse biological systems-including the human dorsolateral prefrontal cortex, mouse brain, human breast cancer, osmFISH, MERFISH, STARmap, mouse embryonic development, head and neck angiosarcoma, and brain metastasis-shows that SGFN achieves improved or competitive performance relative to representative baseline methods in reference-based benchmarks, and identifies biologically coherent spatial or functional regions in unlabeled datasets supported by marker-gene, spatial-autocorrelation, cell-type-colocalization, and pathway-enrichment evidence. SGFN accurately reconstructed cortical layer architecture in the human brain, delineated immune and metabolic modules in tumors, and revealed spatiotemporal trajectories during embryogenesis. By combining interpretable frequency-domain representations with data-driven learning, SGFN provides a unified computational framework for decoding tissue organization and molecular heterogeneity, advancing the understanding of developmental, physiological, and pathological spatial systems.
7 July 2026
Read appraisal →Journal of advanced research
scMapNet: Marker-based cell type annotation of scRNA-seq data via vision transfer learning with tabular-to-image transformations
INTRODUCTION: Identifying cell types is a key step in single-cell RNA sequencing data analysis that aids in understanding cellular heterogeneity and facilitates downstream analyses such as those concerning cell-cell interactions and data integration. Cell-type annotation methods often rely on unsupervised clustering algorithms, followed by manual or automatic annotation via marker genes, which are prone to inefficiency and inconsistency. Supervised methods, while more automated and consistent, have gained remarkable attention because of the rapid growth of large-scale, high-quality single-cell datasets. However, these methods cannot effectively leverage cellular marker knowledge and much unlabelled data. OBJECTIVES: This study aims to introduce a novel deep learning method, scMapNet, which can sufficiently learn cellular marker knowledge and information from unlabelled data. METHODS: scMapNet is a self-supervised deep learning model based on masked autoencoders (MAE) and vision transformer (ViT), which adopts treemap transformations to leverage cell marker information and capture information by pretraining on large amounts of unlabelled data. RESULTS: scMapNet outperforms six competing methods across diverse datasets, excelling in accuracy and batch insensitivity. Moreover, scMapNet can effectively extract attention gene information, providing strong support for cell type identification. The models and codes are available at https://github.com/Yuz7/scMapNet. CONCLUSION: scMapNet shows significant performance in annotation accuracy and batch insensitivity. Besides, this method also has good interpretability and can provide biological insights for researchers.
3 July 2026
Read appraisal →