Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been audpopular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power sinceudthe number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariateudinference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, largescaleuddata. We propose a directed partial correlation (DPC) method as an efficient and effective solution to regulatoryudnetwork inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scaleuddatasets. It combines the efficiency of partial correlation for setting up network topology by testing conditionaludindependence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is thatudwhen a transcription factor is induced artificially within a gene network, the disruption of the network by the inductionudsignifies a genes role in transcriptional regulation. The benchmarking results using GeneNetWeaver, the simulator for theudDREAM challenges, provide strong evidence of the outstanding performance of the proposed DPC method. When appliedudto real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful networkudmodules worthy of further investigation. These results collectively suggest DPC is a versatile tool for genomics research. TheudR package DPC is available for download (http://code.google.com/p/dpcnet/).
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