# CellNavi predicts genes directing cellular transitions by learning a gene graph-enhanced cell state manifold

## TL;DR

CellNavi uses deep learning to identify key genes that drive cell state changes, helping understand disease and drug responses.

## Contribution

CellNavi introduces a gene graph-enhanced cell state manifold for accurate driver gene prediction across diverse conditions.

## Key findings

- CellNavi accurately predicts driver genes for transitions caused by genetic, chemical, and cytokine perturbations.
- The framework works across diverse cell types, conditions, and studies, including differentiation and disease progression.
- CellNavi improves driver gene prediction and cell state manipulation for disease biology and therapeutic discovery.

## Abstract

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. Here, 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 directional connections. Our analysis shows that CellNavi can accurately predict driver genes for transitions induced by genetic, chemical and cytokine perturbations across diverse cell types, conditions and studies. By leveraging a biologically meaningful cell state manifold, it is proficient in tasks involving critical transitions such as cellular differentiation, disease progression and drug response. CellNavi represents a substantial advancement in driver gene prediction and cell state manipulation, opening new avenues in disease biology and therapeutic discovery.

The authors integrate single-cell transcriptomic data with prior gene graphs to produce a biologically meaningful cell state manifold that can predict driver genes for genetic perturbations and differentiation events across diverse cell types.