Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDA

Music City Center 

While single-cell RNA-sequencing has facilitated profiling of heterogeneous transcriptional responses to genetic perturbations at the single-cell level, there remains a pressing need for computational models that can decode the mechanisms driving these responses and accurately predict outcomes to prioritize target genes for experimental design. We present scLAMBDA, a deep generative learning framework designed to model and predict single-cell transcriptional responses to genetic perturbations. By leveraging gene embeddings derived from large language models, scLAMBDA effectively integrates prior biological knowledge and disentangles basal cell states from perturbation-specific salient representations. Through comprehensive evaluations on multiple datasets, scLAMBDA consistently outperformed other methods in predicting perturbation outcomes. It demonstrated robust generalization to unseen target genes and perturbations, capturing both average expression changes and the heterogeneity of single-cell responses. Furthermore, its predictions enable diverse downstream analyses, including the identification of differentially expressed genes and the exploration of genetic interactions.

single-cell RNA-sequencing

genetic perturbation

deep learning 

Section on Statistics in Genomics and Genetics