scReader: Prompting Large Language Models to Interpret scRNA-seq Data

TL;DR

This paper introduces scReader, a hybrid method combining large language models with domain-specific models to interpret single-cell RNA sequencing data across species, improving annotation accuracy and interoperability.

Contribution

The study presents a novel hybrid approach that leverages LLMs and gene-level representations for cross-species single-cell data interpretation, addressing data scale disparities.

Findings

Improved cell annotation accuracy over existing methods

Enhanced cross-species data interoperability

Effective visualization of single-cell gene expression data

Abstract

Large language models (LLMs) have demonstrated remarkable advancements, primarily due to their capabilities in modeling the hidden relationships within text sequences. This innovation presents a unique opportunity in the field of life sciences, where vast collections of single-cell omics data from multiple species provide a foundation for training foundational models. However, the challenge lies in the disparity of data scales across different species, hindering the development of a comprehensive model for interpreting genetic data across diverse organisms. In this study, we propose an innovative hybrid approach that integrates the general knowledge capabilities of LLMs with domain-specific representation models for single-cell omics data interpretation. We begin by focusing on genes as the fundamental unit of representation. Gene representations are initialized using functional descriptions, leveraging the strengths of mature language models such as LLaMA-2. By inputting single-cell gene-level expression data with prompts, we effectively model cellular representations based on the differential expression levels of genes across various species and cell types. In the experiments, we constructed developmental cells from humans and mice, specifically targeting cells that are challenging to annotate. We evaluated our methodology through basic tasks such as cell annotation and visualization analysis. The results demonstrate the efficacy of our approach compared to other methods using LLMs, highlighting significant improvements in accuracy and interoperability. Our hybrid approach enhances the representation of single-cell data and offers a robust framework for future research in cross-species genetic analysis.