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

ABSTRACT: Understanding cellular responses to genetic perturbations is essential for understanding gene regulation and phenotype formation. While high-throughput single-cell RNA-sequencing has facilitated detailed profiling of heterogeneous transcriptional responses to 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. Here, we present scLAMBDA, a deep generative learning framework designed to model and predict single-cell transcriptional responses to genetic perturbations, including single-gene and combinatorial multi-gene perturbations. By leveraging gene embeddings derived from large language models, scLAMBDA effecti

SUBMITTER: Wang G 

PROVIDER: S-EPMC11643044 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature