Directing cellular transitions on gene graph-enhanced cell state manifold

A select few genes act as pivotal drivers in the process of cell state transitions. However, finding key genes involved in different transitions is challenging. To address this problem, we present CellNavi, a deep learning-based framework designed to predict genes that drive cell state transitions. CellNavi builds a driver gene predictor upon a cell state manifold, which captures the intrinsic features of cells by learning from large-scale, high-dimensional transcriptomics data and integrating gene graphs with causal connections. Our analysis shows that CellNavi can accurately predict driver genes for transitions induced by genetic modifications and chemical treatments across diverse cell types, conditions, and studies. It is proficient in tasks involving critical transitions such as cellular differentiation, disease progression, and drug response by leveraging a biologically meaningful cell state manifold. CellNavi represents a substantial advancement in the methodology for predicting driver genes and manipulating cell states, opening up new research opportunities in disease biology and therapeutic innovation.