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

Rahul Bhadani, Zhuo Chen, Lingling An

Research output: Contribution to journalArticlepeer-review

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 best for highly sparse datasets in terms of F1 score, precision score, recall score, and Matthew’s correlation coefficients as well. Further, our method’s runtime complexity is consistently faster compared to other methods.

Original languageEnglish (US)
Article number506
JournalGenes
Volume14
Issue number2
DOIs
StatePublished - Feb 2023

Keywords

  • classification
  • graph neural network
  • label propagation
  • neural network
  • scRNA-seq
  • single-cell
  • transcriptomics

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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