Yuan, Yinyin, Li, Chang-Tsun and Windram, Oliver P. (2011) Directed partial correlation : inferring large-scale gene regulatory network through induced topology disruptions. PL o S One, Vol.6 (No.4). doi:10.1371/journal.pone.0016835

Preview

PDF WRAP_Windram_pone.0016835.pdf - Requires a PDF viewer. Download (2307Kb)

Request Changes to record.

Abstract

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, largescale
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/).

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH426 Genetics
Divisions:	Faculty of Science > Computer Science Faculty of Science > Life Sciences (2010- ) Faculty of Science > Life Sciences (2010- ) > Warwick HRI (2004-2010)
Library of Congress Subject Headings (LCSH):	Genetic regulation -- Mathematical models, Topology, System analysis
Journal or Publication Title:	PL o S One
Publisher:	Public Library of Science
ISSN:	1932-6203
Official Date:	6 April 2011
Dates:	Date Event 6 April 2011 Published
Volume:	Vol.6
Number:	No.4
DOI:	10.1371/journal.pone.0016835
Status:	Peer Reviewed
Access rights to Published version:	Open Access
Funder:	University of Warwick. Dept. of Computer Science, Warwick HRI

Data sourced from Thomson Reuters' Web of Knowledge

Request changes or add full text files to a record

Repository staff actions (login required)

View Item

Downloads