ScPlantLLM: A Foundation Model for Exploring Single-cell Expression Atlases in Plants.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into plant cellular diversity by enabling high-resolution analyses of gene expression at the single-cell level. However, the complexity of scRNA-seq data, including challenges in batch integration, cell type annotation, and gene regulatory network (GRN) inference, demands advanced computational approaches. To address these challenges, we developed scPlantLLM, a Transformer model trained on millions of plant single-cell data points. Using a sequential pretraining strategy incorporating masked language modeling and cell type annotation tasks, scPlantLLM generates robust and interpretable single-cell data embeddings. When applied to Arabidopsis thaliana datasets, scPlantLLM excels in clustering, cell type annotation, and b

SUBMITTER: Cao 曹广硕 G 

PROVIDER: S-EPMC12417071 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature