PIKE-R2P: Protein-protein interaction network-based knowledge embedding with graph neural network for single-cell RNA to protein prediction
Affiliation
School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China.Issue Date
2021
Metadata
Show full item recordAbstract
Background: Recent advances in simultaneous measurement of RNA and protein abundances at single-cell level provide a unique opportunity to predict protein abundance from scRNA-seq data using machine learning models. However, existing machine learning methods have not considered relationship among the proteins sufficiently. Results: We formulate this task in a multi-label prediction framework where multiple proteins are linked to each other at the single-cell level. Then, we propose a novel method for single-cell RNA to protein prediction named PIKE-R2P, which incorporates protein-protein interactions (PPI) and prior knowledge embedding into a graph neural network. Compared with existing methods, PIKE-R2P could significantly improve prediction performance in terms of smaller errors and higher correlations with the gold standard measurements. Conclusion: The superior performance of PIKE-R2P indicates that adding the prior knowledge of PPI to graph neural networks can be a powerful strategy for cross-modality prediction of protein abundances at the single-cell level.Citation
Dai X, Xu F, Wang S, Mundra PA, Zheng J. PIKE-R2P: Protein–protein interaction network-based knowledge embedding with graph neural network for single-cell RNA to protein prediction. BMC Bioinformatics . 2021 Jun;22(S6).Journal
BMC BioinformaticsDOI
10.1186/s12859-021-04022-wPubMed ID
34078261Additional Links
https://dx.doi.org/10.1186/s12859-021-04022-wType
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.1186/s12859-021-04022-w