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

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posted on 2011-04-06, 00:00 authored by Yinyin Yuan, Chang-Tsun LiChang-Tsun Li, Oliver Windram
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/).

History

Journal

PLoS one

Volume

6

Article number

e16835

Location

San Francisco, Calif.

Open access

  • Yes

eISSN

1932-6203

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2011, Yuan et al.

Issue

4

Publisher

Public Library of Science

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