Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Cell systems
BulkFormer: A large-scale foundation model for bulk transcriptomes
Foundation models are transforming transcriptome analysis, yet most RNA sequencing (RNA-seq) foundation models are pretrained on sparse single-cell RNA-seq data, which typically captures only ∼3,000 genes per cell. This creates a need for models tailored to bulk transcriptomes, a modality that profiles ∼16,000 genes per sample and supports clinical and tissue-level analyses. Here, we present BulkFormer, a foundation model for bulk transcriptome analysis. BulkFormer contains ∼150 million parameters, covers 20,010 protein-coding genes, and is pretrained on 581,503 human bulk RNA-seq profiles. Its hybrid encoder combines a graph neural network to model explicit gene-gene relationships with a Performer module to capture global expression dependencies. Across five downstream tasks, BulkFormer outperformed existing single-cell foundation models while requiring substantially lower training cost. These results identify pretraining data modality as a key determinant of foundation model performance and establish BulkFormer as a framework for bulk transcriptome modeling. A record of this paper's transparent peer review process is included in the supplemental information.
17 July 2026
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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
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