CellGO: A novel deep learning-based framework and webserver for cell type-specific gene function interpretation

Abstract Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to account for complex interactions across genes and pathways under specific tissues and cell types. We introduce CellGO, a tool for cell type-specific gene functional analysis. CellGO employs a deep learning model to simulate signaling propagation within a cell, enabling the development of a heuristic pathway activity measuring system to identify cell type-specific active pathways given a single gene or a gene set. It is featured with additional functions to uncover pathway communities and the most active genes within pathways to facilitate mechanistic interpretation. This study demonstrated that CellGO can effectively capture cell type-specific pathways even when working with mixed cell-type markers. CellGO’s performance was benchmarked using gene knockout datasets, and its implementation effectively infers the cell type-specific pathogenesis of risk genes associated with neurodevelopmental and neurodegenerative disorders, suggesting its potential in understanding complex polygenic diseases. CellGO is accessible through a python package and a four-mode web interface for interactive usage with pretrained models on 71 single-cell datasets from human and mouse fetal and postnatal brains.