Transformer-based Single-Cell Language Model: A Survey

TL;DR

This survey reviews transformer-based models for single-cell data analysis, highlighting their structures, applications, challenges, and future research directions in the emerging field of single-cell language modeling.

Contribution

It systematically summarizes recent developments in transformer-based single-cell language models and their applications, providing a comprehensive overview for researchers.

Findings

Transformers are effectively applied to single-cell data analysis tasks.

Single-cell language models facilitate tasks like cell clustering and gene regulation inference.

The review identifies key challenges and future directions in the field.

Abstract

The transformers have achieved significant accomplishments in the natural language processing as its outstanding parallel processing capabilities and highly flexible attention mechanism. In addition, increasing studies based on transformers have been proposed to model single-cell data. In this review, we attempt to systematically summarize the single-cell language models and applications based on transformers. First, we provide a detailed introduction about the structure and principles of transformers. Then, we review the single-cell language models and large language models for single-cell data analysis. Moreover, we explore the datasets and applications of single-cell language models in downstream tasks such as batch correction, cell clustering, cell type annotation, gene regulatory network inference and perturbation response. Further, we discuss the challenges of single-cell language models and provide promising research directions. We hope this review will serve as an up-to-date reference for researchers interested in the direction of single-cell language models.