Integrative analysis of gene networks and their application to lung adenocarcinoma studies

Sangin Lee, Faming Liang, Ling Cai, Guanghua Xiao

Research output: Contribution to journalArticle

Abstract

The construction of gene regulatory networks (GRNs) is an essential component of biomedical research to determine disease mechanisms and identify treatment targets. Gaussian graphical models (GGMs) have been widely used for constructing GRNs by inferring conditional dependence among a set of gene expressions. In practice, GRNs obtained by the analysis of a single data set may not be reliable due to sample limitations. Therefore, it is important to integrate multiple data sets from comparable studies to improve the construction of a GRN. In this article, we introduce an equivalent measure of partial correlation coefficients in GGMs and then extend the method to construct a GRN by combining the equivalent measures from different sources. Furthermore, we develop a method for multiple data sets with a natural missing mechanism to accommodate the differences among different platforms in multiple sources of data. Simulation results show that this integrative analysis outperforms the standard methods and can detect hub genes in the true network. The proposed integrative method was applied to 12 lung adenocarcinoma data sets collected from different studies. The constructed network is consistent with the current biological knowledge and reveals new insights about lung adenocarcinoma.

Original languageEnglish (US)
Article number1176935117690778
JournalCancer Informatics
Volume16
DOIs
StatePublished - Feb 23 2017

Fingerprint

Gene Regulatory Networks
Information Storage and Retrieval
Biomedical Research
Adenocarcinoma of lung
Gene Expression
Datasets
Genes

Keywords

  • Gaussian graphical model
  • Gene regulatory network
  • Integrative analysis
  • Multiple hypothesis test
  • Partial correlation coefficient

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

Integrative analysis of gene networks and their application to lung adenocarcinoma studies. / Lee, Sangin; Liang, Faming; Cai, Ling; Xiao, Guanghua.

In: Cancer Informatics, Vol. 16, 1176935117690778, 23.02.2017.

Research output: Contribution to journalArticle

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