Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics.

ABSTRACT: Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data have been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been proposed for cell-type identification. However, these methods do not capture the higher-order topological relationship between different samples. In this work, we propose an attention-based graph neural network that captures the higher-order topological relationship between different samples and performs transductive learning for predicting cell types. The evaluation of our method on both simulation and publicly available datasets demonstrates the superiority of our method, scAGN, in terms of prediction accuracy. In addition, our method works b

SUBMITTER: Bhadani R 

PROVIDER: S-EPMC9957137 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature