Economics at your fingertips  

A statistical method for measuring activation of gene regulatory networks

Esteves Gustavo H. () and Reis Luiz F. L.
Additional contact information
Esteves Gustavo H.: Statistics Department, University of Paraíba State, Campina Grande, PB, Brazil
Reis Luiz F. L.: Teaching and Research Institute, Sírio-Libânes Hospital, São Paulo-SP, Brazil

Statistical Applications in Genetics and Molecular Biology, 2018, vol. 17, issue 3, 11

Abstract: Motivation: Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks. Results: We present the mathematical construction of a statistical procedure for testing hypothesis regarding gene regulatory network activation. The real probability distribution for the test statistic is evaluated by a permutation based study. To illustrate the functionality of the proposed methodology, we also present a simple example based on a small hypothetical network and the activation measuring of two KEGG networks, both based on gene expression data collected from gastric and esophageal samples. The two KEGG networks were also analyzed for a public database, available through NCBI-GEO, presented as Supplementary Material. Availability: This method was implemented in an R package that is available at the BioConductor project website under the name maigesPack.

Keywords: gene regulatory networks; hypothesis tests; systems biology (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1515/sagmb-2016-0059

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().

Page updated 2021-05-24
Handle: RePEc:bpj:sagmbi:v:17:y:2018:i:3:p:11:n:3