Towards efficient integration of LLM gene embeddings to gene expression analysis

Music City Center 

The integration of large language models with single-cell gene expression data has introduced a new type of data that includes a gene embedding matrix alongside the traditional gene expression matrix. This study addresses an important challenge of effectively merging these two data sources to enhance the definition of cell-to-cell distances. We identify a computationally feasible method that significantly improves the clustering of cells of the same type within real single-cell datasets.