A Cartographic Tool to Predict Disease Risk-associated Pseudo-Dynamic Networks from Tissue-specific Gene Expression

Chixiang Chen, Biyi Shen, Lijun Zhang, Tonghui Yu, Ming Wang, Rongling Wu

Research output: Contribution to journalArticlepeer-review

Abstract

Understanding how genes are differentially expressed across tissues is key to reveal the etiology of human diseases. Genes are never expressed in isolation, but rather co-expressed in a community; thus, they co-act through intricate but well-orchestrated networks. However, existing approaches cannot coalesce the full properties of gene–gene communication and interactions into networks. In particular, the unavailability of dynamic gene expression data might impair the application of existing network models to unleash the complexity of human diseases. To address this limitation, we developed a statistical pipeline named DRDNetPro to visualize and trace how genes dynamically interact with each other across diverse tissues, to ascertain health risk from static expression data. This protocol contains detailed tutorials designed to learn a series of networks, with the illustration example from the Genotype-Tissue Expression (GTEx) project. The proposed toolbox relies on the method developed in our published paper (Chen et al., 2022), coding all genes into bidirectional, signed, weighted, and feedback looped networks, which will provide profound genomic information enabling medical doctors to design precise medicine.

Original languageEnglish (US)
Article numbere4583
JournalBio-protocol
Volume13
Issue number1
DOIs
StatePublished - Jan 5 2023

All Science Journal Classification (ASJC) codes

  • General Neuroscience
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Plant Science

Cite this