Contrastive Learning Enhances Language Model Based Cell Embeddings for Low-Sample Single Cell Transcriptomics

Large language models (LLMs) have shown strong ability in generating rich representations across domains such as natural language processing and generation, computer vision, and multimodal learning. However, their application in biomedical data analysis remains nascent. Single-cell transcriptomic profiling is essential for dissecting cell subtype diversity in development and disease, but rare subtypes pose challenges for scaling laws. We present a computational framework that integrates single-cell RNA sequencing (scRNA-seq) with LLMs to derive knowledge-informed gene embeddings. Highly expressed genes for each cell are mapped to NCBI Gene descriptions and embedded using models such as text-embedding-ada-002, BioBERT, and SciBERT. Applied to retinal ganglion cells (RGCs), which differ in vulnerability to glaucoma-related neurodegeneration, this strategy improves subtype classification, highlights biologically significant features, and reveals pathways underlying selective neuronal vulnerability. More broadly, it illustrates how LLM-derived embeddings can augment biological analysis under data-limited conditions and lay the groundwork for future foundation models in single-cell biology.