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

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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). ISSN 1932-6203

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Official URL: http://dx.doi.org/10.1371/journal.pone.0016835

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

Identification Number:

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

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