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

ABSTRACT: 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

SUBMITTER: Tang H 

PROVIDER: S-EPMC11607606 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature