A0704
Title: A multi-use graph neural network framework for single-cell multi-omics data
Authors: Peifeng Ruan - UT Southwestern Medical Center (United States) [presenting]
Abstract: The advances of single-cell multi-omics profiling technologies in biomedical research offer an unprecedented opportunity for understanding cell heterogeneity and subpopulations. There are many statistical and computational challenges in the integrative analyses of these rich data, including sequencing sparsity, complex differential patterns in gene expression, and different platforms and panels used to generate multiple single-cell multi-omics. A multi-use graph neural network framework is introduced that can effectively impute, and missing sequencing panels are predicted, multi-omics single-cell datasets are integrated, and cell-cell relationships with graph neural networks are formulated and aggregated. Comprehensive simulations and applications on multiple CITE-seq and single-cell RNA-sequencing datasets demonstrate that the proposed method is a powerful tool for general single-cell data multi-omics analyses that outperforms the existing methods for protein prediction, gene imputation and cell clustering.
