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CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing

Wei Liu1, Zhijie Teng2, Zejun Li3

  • 1School of Computer Science, Xiangtan University, Xiangtan, 411105, China. liuwei@xtu.edu.cn.

Interdisciplinary Sciences, Computational Life Sciences
|May 22, 2024
PubMed
Summary
This summary is machine-generated.

We developed CVGAE, a self-supervised method for gene regulatory network inference from single-cell RNA sequencing data. It improves accuracy and generalization, outperforming existing methods even in few-shot learning scenarios.

Keywords:
Gene regulatory network inferenceGraph neural networksRepresentation learningSingle-cell RNA sequencing

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Gene regulatory network (GRN) inference is vital for understanding gene regulation.
  • Current computational methods struggle with high-dimensional single-cell RNA sequencing data (scRNAseq) and network sparsity, limiting accuracy and generalization.
  • Existing GRN inference techniques often yield unsatisfactory results due to these challenges.

Purpose of the Study:

  • To propose a novel self-supervised method, CVGAE, for accurate and generalizable GRN inference from scRNAseq data.
  • To address the limitations of existing methods in handling high-dimensional data and network sparsity.
  • To evaluate the performance and learning capabilities of CVGAE, including its effectiveness in few-shot learning environments.

Main Methods:

  • Developed CVGAE, a self-supervised method utilizing graph neural networks for inductive representation learning.
  • Integrated gene expression data and observed topology into a low-dimensional vector space.
  • Employed FastICA to reduce computational complexity and multi-stacked GraphSAGE layers with an improved decoder to handle network sparsity.

Main Results:

  • CVGAE demonstrated superior performance compared to existing methods on multiple single-cell datasets with known ground-truth networks.
  • The method achieved comparable or superior results in few-shot learning scenarios, validating its learning and generalization capabilities.
  • The low-dimensional vectors derived from CVGAE effectively predict gene interactions based on mathematical distances.

Conclusions:

  • CVGAE offers a robust and effective approach for gene regulatory network inference using single-cell RNA sequencing data.
  • The self-supervised, graph neural network-based method overcomes key limitations of previous techniques, particularly in data complexity and sparsity.
  • CVGAE shows significant promise for advancing our understanding of gene regulatory mechanisms, even with limited training data.