Driver gene detection via causal inference on single cell embeddings

Driver genes are pivotal in different biological processes. Current methods generally identify driver genes by associative analysis. Leveraging on the development of current large language models (LLM) in single cell genomics, we propose a causal inference based approach called CID to identify driver genes from scRNA-seq data. Through experiments on three different datasets, we show that CID can (1) identify biologically meaningful driver genes that have not been captured by current associative-analysis based methods, and (2) accurately predict the change directions of target genes if a driver gene is knocked out.