Attention-Based Graph Neural Network for Label Transfer in Single Cell Multiomics Data

• Activity Number: 409 - Statistical Advances in Single-Cell Research
• Type: Topic Contributed
• Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
• Sponsor: WNAR
• Author(s): Lingling An* and Rahul Bhadani and Zhuo Chen
• Companies: University of Arizona and University of Arizona and University of Arizona
• Keywords: single cell; multi-omics; graph neural network; machine learning; classification

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 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. 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, precision, recall, and Matthew’s correlation coefficients. Further, our method’s runtime complexity is consistently faster compared to other methods.