Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data

Author:

Chen Guangyi1,Liu Zhi-Ping1ORCID

Affiliation:

1. Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University , Jinan, Shandong 250061, China

Abstract

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) data provides unprecedented opportunities to reconstruct gene regulatory networks (GRNs) at fine-grained resolution. Numerous unsupervised or self-supervised models have been proposed to infer GRN from bulk RNA-seq data, but few of them are appropriate for scRNA-seq data under the circumstance of low signal-to-noise ratio and dropout. Fortunately, the surging of TF-DNA binding data (e.g. ChIP-seq) makes supervised GRN inference possible. We regard supervised GRN inference as a graph-based link prediction problem that expects to learn gene low-dimensional vectorized representations to predict potential regulatory interactions. Results In this paper, we present GENELink to infer latent interactions between transcription factors (TFs) and target genes in GRN using graph attention network. GENELink projects the single-cell gene expression with observed TF-gene pairs to a low-dimensional space. Then, the specific gene representations are learned to serve for downstream similarity measurement or causal inference of pairwise genes by optimizing the embedding space. Compared to eight existing GRN reconstruction methods, GENELink achieves comparable or better performance on seven scRNA-seq datasets with four types of ground-truth networks. We further apply GENELink on scRNA-seq of human breast cancer metastasis and reveal regulatory heterogeneity of Notch and Wnt signalling pathways between primary tumour and lung metastasis. Moreover, the ontology enrichment results of unique lung metastasis GRN indicate that mitochondrial oxidative phosphorylation (OXPHOS) is functionally important during the seeding step of the cancer metastatic cascade, which is validated by pharmacological assays. Availability and implementation The code and data are available at https://github.com/zpliulab/GENELink. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China (NSFC

Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project

Innovation Method Fund of China

Fundamental Research Funds for the Central Universities

Tang Scholar and Program of Qilu Young Scholar of Shandong University

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference54 articles.

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4. DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data;Chen;Brief. Bioinform,2021

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