Exploring structured molecular landscape from single-cell multi-omics data by an explainable multimodal model

Abstract

Summary: There is an urgent need to understand the molecular landscape beyond the conventional cellular landscape, maximizing the translational use and generalized interpretation of state-of-the-art single-cell genomic techniques in biological studies. We introduced a multimodal explainable artificial intelligence (xAI) model Vec3D to identify a joint definition of cellular states and their distribution in a quantified graphic organization as structured molecular landscape (SML). First, Vec3D substantially improves the accuracy and efficiency of multimodal data analysis. Further, an SML was learned on CITE-seq data of human peripheral blood mononuclear cells (PBMCs), simultaneously revealing the predictive multi-label cell state and corresponding joint cell state markers with complementary effects from genes and proteins. Third, Vec3D demonstrated that the spatial-temporal SML efficiently characterizes molecular dynamics of cell lineages during human lung development. Collectively, Vec3D will be a broadly applicable computational method in the principle of “AI-for-biology”, providing a unified framework for understanding cellular homeostasis and imbalance through SML dynamics.

Keywords

• Biocomputational method
• Computational bioinformatics
• Omics
• Artificial intelligence