scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation (2402.12405v1)

Abstract: Despite the inherent limitations of existing LLMs in directly reading and interpreting single-cell omics data, they demonstrate significant potential and flexibility as the Foundation Model. This research focuses on how to train and adapt the LLM with the capability to interpret and distinguish cell types in single-cell RNA sequencing data. Our preliminary research results indicate that these foundational models excel in accurately categorizing known cell types, demonstrating the potential of the LLMs as effective tools for uncovering new biological insights.