Directed partial correlation: inferring large-scale gene regulatory network through induced topology disruptions

Yuan, Yinyin, Li, Chang-Tsun and Windram, Oliver 2011, Directed partial correlation: inferring large-scale gene regulatory network through induced topology disruptions, PLoS one, vol. 6, no. 4, doi: 10.1371/journal.pone.0016835.

Attached Files
Name Description MIMEType Size Downloads

Title Directed partial correlation: inferring large-scale gene regulatory network through induced topology disruptions
Author(s) Yuan, Yinyin
Li, Chang-TsunORCID iD for Li, Chang-Tsun orcid.org/0000-0003-4735-6138
Windram, Oliver
Journal name PLoS one
Volume number 6
Issue number 4
Article ID e16835
Total pages 12
Publisher Public Library of Science
Place of publication San Francisco, Calif.
Publication date 2011-04-06
ISSN 1932-6203
Keyword(s) Algorithms
Cluster Analysis
Computer Simulation
Databases, Genetic
Gene Regulatory Networks
Multivariate Analysis
Reproducibility of Results
Statistics as Topic
Time Factors
Transcription, Genetic
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
METABOLISM
EXPRESSION
STARCH
COLD
ABA
DNA
Summary Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power since the number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, large-scale data. We propose a directed partial correlation (DPC) method as an efficient and effective solution to regulatory network inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is that when a transcription factor is induced artificially within a gene network, the disruption of the network by the induction signifies a genes role in transcriptional regulation. The benchmarking results using GeneNetWeaver, the simulator for the DREAM challenges, provide strong evidence of the outstanding performance of the proposed DPC method. When applied to real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful network modules worthy of further investigation. These results collectively suggest DPC is a versatile tool for genomics research. The R package DPC is available for download (http://code.google.com/p/dpcnet/).
Language eng
DOI 10.1371/journal.pone.0016835
Field of Research MD Multidisciplinary
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2011, Yuan et al.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30119441

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 21 times in TR Web of Science
Scopus Citation Count Cited 23 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 86 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Fri, 08 Mar 2019, 09:55:56 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.