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Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data.

Qiuyue Yuan1, Zhana Duren2

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|April 12, 2024
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Summary

LINGER infers gene regulatory networks using single-cell multiome data, significantly improving accuracy. This method also estimates transcription factor activity from gene expression data for disease studies.

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Gene regulatory network (GRN) inference traditionally uses gene expression data or low-resolution bulk data.
  • Integrating chromatin accessibility and RNA sequencing data presents challenges due to limited independent data points for learning complex mechanisms.

Purpose of the Study:

  • To develop a machine-learning method, LINGER (Lifelong Neural Network for Gene Regulation), for inferring GRNs from single-cell paired gene expression and chromatin accessibility data.
  • To leverage atlas-scale external bulk data and transcription factor motif prior knowledge for enhanced GRN inference.

Main Methods:

  • LINGER utilizes single-cell multiome data (gene expression and chromatin accessibility).
  • Incorporates external bulk data and transcription factor motif information as manifold regularization.
  • Applies a lifelong neural network approach for continuous learning and adaptation.

Main Results:

  • LINGER demonstrates a fourfold to sevenfold relative increase in accuracy compared to existing methods.
  • Reveals a complex regulatory landscape relevant to genome-wide association studies (GWAS).
  • Enables enhanced interpretation of disease-associated variants and genes.

Conclusions:

  • LINGER provides a powerful tool for GRN inference from single-cell multiome data.
  • Facilitates the estimation of transcription factor activity from gene expression data for identifying driver regulators in case-control studies.
  • Enhances understanding of disease mechanisms by linking genetic variants to regulatory elements and genes.