Learning gene regulatory networks from next generation sequencing data

Bochao Jia, Suwa Xu, Guanghua Xiao, Vishal Lamba, Faming Liang

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

In recent years, next generation sequencing (NGS) has gradually replaced microarray as the major platform in measuring gene expressions. Compared to microarray, NGS has many advantages, such as less noise and higher throughput. However, the discreteness of NGS data also challenges the existing statistical methodology. In particular, there still lacks an appropriate statistical method for reconstructing gene regulatory networks using NGS data in the literature. The existing local Poisson graphical model method is not consistent and can only infer certain local structures of the network. In this article, we propose a random effect model-based transformation to continuize NGS data and then we transform the continuized data to Gaussian via a semiparametric transformation and apply an equivalent partial correlation selection method to reconstruct gene regulatory networks. The proposed method is consistent. The numerical results indicate that the proposed method can lead to much more accurate inference of gene regulatory networks than the local Poisson graphical model and other existing methods. The proposed data-continuized transformation fills the theoretical gap for how to transform discrete data to continuous data and facilitates NGS data analysis. The proposed data-continuized transformation also makes it feasible to integrate different types of data, such as microarray and RNA-seq data, in reconstruction of gene regulatory networks.

Original languageEnglish (US)
Pages (from-to)1221-1230
Number of pages10
JournalBiometrics
Volume73
Issue number4
DOIs
StatePublished - Jan 1 2017

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Keywords

  • Data-continuized transformation
  • Gaussian graphical model
  • Gene regulatory network
  • Poisson graphical model
  • RNA-seq

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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