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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
Date:	6 April 2011
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
References:	1. Opgen-Rhein R, Strimmer K (2007) Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process. BMC Bioinformatics 8: S3. 2. Bernard A, Hartemink AJ (2005) Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data. Proceedings of the Pacific Symposium on Biocomputing. pp 459–70. 3. Lebre S (2009) Inferring dynamic genetic networks with low order independencies. Statistical Applications in Genetics and Molecular Biology 8. 4. Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37: 424–438. 5. Zou C, Feng J (2009) Granger causality vs. dynamic bayesian network inference: a comparative study. BMC Bioinformatics 10: 122+. 6. Marinazzo D, Pellicoro M, Stramaglia S (2008) Kernel-granger causality and the analysis of dynamical networks. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 77. 7. Scha¨ fer J, Strimmer K (2005) An empirical Bayes approach to inferring largescale gene association networks. Bioinformatics 21: 754–64. 8. Veiga D, Vicente F, Grivet M, de la Fuente A, Vasconcelos A (2007) Genomewide partial correlation analysis of escherichia coli microarray data. Genet Mol Res 6: 730–742. 9. Marbach D, Schaffter T, Mattiussi C, Floreano D (2009) Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods. Journal of Computational Biology 16: 229–239. 10. Stolovitzky G, Monroe DON, Califano A (2007) Dialogue on reverseengineering assessment and methods: The dream of high-throughput pathway inference. Annals of the New York Academy of Sciences 1115: 1–22. 11. Ltkepohl H (2006) New Introduction to Multiple Time Series Analysis. Springer Publishing Company, Incorporated. 12. Mukhopadhyay NDD, Chatterjee S (2006) Causality and pathway search in microarray time series experiment. Bioinformatics. 13. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society B: 289–300. 14. Bro R, Sidiropoulos ND, Smilde AK (2002) Maximum likelihood fitting using ordinary least squares algorithms. Journal of Chemometrics 16: 387–400. 15. Egan J (1975) Signal detection theory and ROC analysis, Series in Cognition and Perception. New York, NY, USA: Academic Press. 16. Fawcett T (2006) An introduction to roc analysis. Pattern Recogn Lett 27: 861–874. 17. Rijsbergen CJV (1979) Information Retrieval. Newton, MA, USA: Butterworth- Heinemann. 18. Smith S, Fulton D, Chia T, Thorneycroft D, Chapple A, et al. (2004) Diurnal changes in the transcriptom encoding enzymes of starch metabolism provide evidence for both transcriptionaland posttranscriptional regulation of starch metabolism inarabidopsis leaves. Plant Physiology 136: 2687–2699. 19. Zeeman SC, Smith SM, Smith AM (2007) The diurnal metabolism of leaf starch. Biochem J 401: 13–28. 20. Wichert S, Fokianos K, Strimmer K (2004) Identifying periodically expressed transcripts in microarray time series data. Bioinformatics 20: 5–20. 21. Lee H, Kong SW, Park PJ (2008) Integrative analysis reveals the direct and indirect interactions between dna copy number aberrations and gene expression changes. Bioinformatics 24: 889–896. 22. Tanay ASR, Sharan R (2002) Discovering statistically significant biclusters in gene expression data. Bioinformatics 18(1): S136–144. 23. The Gene Ontology Consortium (2000) Gene ontology: tool for the unification of biology. Nature Genetics 25: 25–29. 24. Wilhelm KS, Thomashow MF (1993) Arabidopsis thaliana cor15b, an apparent homologue of cor15a, is strongly responsive to cold and aba, but not drought. Plant Mol Biol 23: 1073–7. 25. Lin C, Thomashow MF (1992) Dna sequence analysis of a complementary dna for cold-regulated arabidopsis gene cor15 and characterization of the cor 15 polypeptide. Plant Physiol 99: 519–525. 26. Saibo NJM, Lourenco T, Oliveira MM (2009) Transcription factors and regulation of photosynthetic and related metabolism under environmental stresses. Ann Bot 103: 609–623. 27. Rook F, Hadingham SA, Li Y, Bevan MW (2006) Sugar and aba response pathways and the control of gene expression. Plant Cell Environ 29: 426–34. 28. Welch BL (1947) The generalization of ‘‘student’s’’ problem when several different population variances are involved. Biometrika 34: 28–35. 29. Schaffer R (1998) The late elongated hypocotyl mutation of arabidopsis disrupts circadian rhythms and the photoperiodic control of flowering. Cell 93: 1219–1229. 30. Wang ZY, Kenigsbuch D, Sun L, Harel E, Ong M, et al. (1997) A myb-related transcription factor is involved in the phytochrome regulation of an arabidopsis lhcb gene. PlantCell 9: 491–507.
URI:	http://wrap.warwick.ac.uk/id/eprint/4537