scKanFormer: A Transformer-KAN framework with biologically informed attention for cell type annotation in large-scale scRNA-seq data.

Authors

Lin Yuan, Junjie Cao, Shengguo Sun, Siguo Wang, Lan Ye, De-Shuang Huang

Published in

PLoS computational biology. Volume 22. Issue 8. Pages e1014607. Aug 14, 2026. Epub Aug 14, 2026.

Abstract

A key challenge in single-cell RNA sequencing (scRNA-seq) data analysis is accurately and efficiently identifying the cell type of each cell. Cell type annotation for scRNA-seq data not only needs to overcome batch effects caused by various factors but also requires effective handling of large-scale scRNA-seq datasets. Although deep learning has achieved remarkable progress in cell type annotation tasks, it still exhibits limitations in interpretability and robustness against batch effects. To tackle these issues, we propose a supervised framework based on the Transformer architecture, named scKanFormer, for cell type annotation on large-scale multi-class scRNA-seq data. To mitigate the problems of untraceable latent space, poor interpretability, and feature loss caused by the nonlinear aggregation of features in autoencoders, we employ the Transformer framework. This framework avoids dimensionality reduction and enables traceability from the attention layers back to the original input features. By integrating biological information, local and global attention mechanisms, and leveraging Kolmogorov-Arnold Networks (KAN), we enhance the model's ability to identify and interpret cellular features. The combination of Convolutional Neural Network (CNN) and Transformer enables more comprehensive data processing, thereby mitigating batch effects. To evaluate the effectiveness and robustness of scKanFormer, we compared it with nine state-of-the-art methods on benchmark datasets. Through systematic comparisons under different cell type annotation scenarios and across various cell types, we demonstrate that scKanFormer delivers precise, robust, and transferable high-resolution annotations. These annotations are insensitive to batch effects and exhibit clear biological interpretability. The data and source code are available at https://github.com/nathanyl/scKanFormer.

PMID:
42599963
Bibliographic data and abstract were imported from PubMed on 15 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.