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 has 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 thesis, 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. Evaluation of our method on publicly available datasets demonstrates the superiority of our method scAGN in terms of prediction accuracy. In addition, our method works best for highly sparse datasets in terms of F1 score, precision score, recall score, and Matthew's correlation coefficient as well. Further, our method's runtime complexity is consistently faster compared to other methods.