SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation

TL;DR

SpikGPT is a novel hybrid deep learning framework combining scGPT embeddings with spiking Transformer architecture, enabling accurate, interpretable, and energy-efficient cell type annotation in single-cell transcriptomics, including detection of unseen cell types.

Contribution

The paper introduces SpikGPT, a new hybrid model that integrates biologically informed embeddings with spiking attention mechanisms for improved single-cell annotation.

Findings

Consistently outperforms existing annotation tools on benchmark datasets.

Effectively identifies unseen cell types by rejecting low-confidence predictions.

Demonstrates robustness across diverse datasets and cellular heterogeneity.

Abstract

Accurate and scalable cell type annotation remains a challenge in single-cell transcriptomics, especially when datasets exhibit strong batch effects or contain previously unseen cell populations. Here we introduce SpikGPT, a hybrid deep learning framework that integrates scGPT-derived cell embeddings with a spiking Transformer architecture to achieve efficient and robust annotation. scGPT provides biologically informed dense representations of each cell, which are further processed by a multi-head Spiking Self-Attention mechanism for energy-efficient feature extraction. Across multiple benchmark datasets, SpikGPT consistently matches or exceeds the performance of leading annotation tools. Notably, SpikGPT uniquely identifies unseen cell types by assigning low-confidence predictions to an "Unknown" category, allowing accurate rejection of cell states absent from the training reference. Together, these results demonstrate that SpikGPT is a versatile and reliable annotation tool capable of generalizing across datasets, resolving complex cellular heterogeneity, and facilitating discovery of novel or disease-associated cell populations.