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 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.

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“…Tailored performance metrics are essential for effectively addressing imbalanced datasets. Precision, Recall, F1-Score and Matthews Correlation Coefficient (MCC) are recognised as suitable measures for evaluating model performance on such datasets (Chicco and Jurman, 2020; Bhadani et al ., 2023). These metrics are calculated according to the four categories of confusion metrics: True Positives (TP), True Negatives (TN), False Positives (FP) and False Negatives (FN).…”