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.