Abstract
With the completion of the Human Genome Project and the emergence of high-throughput technologies, a vast amount of molecular and biological data are being produced. Two of the most important and significant data sources come from microarray gene-expression experiments and respective databanks (e,g., Gene Expression Omnibus—GEO (http://www.ncbi.nlm.nih.gov/geo)), and from molecular pathways and Gene Regulatory Networks (GRNs) stored and curated in public (e.g., Kyoto Encyclopedia of Genes and Genomes—KEGG (http://www.genome.jp/kegg/pathway.html), Reactome (http://www.reactome.org/ReactomeGWT/entrypoint.html)) as well as in commercial repositories (e.g., Ingenuity IPA (http://www.ingenuity.com/products/ipa)). The association of these two sources aims to give new insight in disease understanding and reveal new molecular targets in the treatment of specific phenotypes.
Three major research lines and respective efforts that try to utilize and combine data from both of these sources could be identified, namely: (1) de novo reconstruction of GRNs, (2) identification of Gene-signatures, and (3) identification of differentially expressed GRN functional paths (i.e., sub-GRN paths that distinguish between different phenotypes). In this chapter, we give an overview of the existing methods that support the different types of gene-expression and GRN integration with a focus on methodologies that aim to identify phenotype-discriminant GRNs or subnetworks, and we also present our methodology.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brown PO, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21:33–37
Huang Y, Zhao Z, Xu H, Shyr Y, Zhang B (2012) Advances in systems biology: computational algorithms and applications. BMC Syst Biol 6(3)
Hung J-H, Yang T-H, Zhenjun H, Weng Z, DeLisi C (2012) Gene set enrichment analysis: performance evaluation and usage guidelines. Brief Bioinform 13(3):281–291
Heckera M, Lambecka S, Toepferb S, van Somerenc E, Guthke R (2009) Gene regulatory network inference: data integration in dynamic models—a review. Biosystems 96(1):86–103
Ein-Dor L, Kela I, Getz G, Givol D, Domany E (2005) Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 21(2):171–178
Iwamoto T, Pusztai L (2010) Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data? Genome Med 2(11):81
Shannon CEA (1948) Mathematical theory of communication. Bell Sys Tech J 27(3):379–423
Potamias G, Koumakis L, Moustakis V (2004) Gene selection via discretized gene-expression profiles and greedy feature-elimination. Meth Appl Artif Intelligence 3025:256–266
Li L, Weinberg CR, Darden TA, Pedersen LG (2001) Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17(12):1131–1142
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Yamanishi Y (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36:480–484
Ott MA, Gert V (2006) Correcting ligands, metabolites, and pathways. BMC Bioinformatics 7(1):517
Khatri P, Draghici S (2005) Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 21:3587–3595
Kauffman SA (1993) The origins of order: self-organization and selection in evolution. Oxford University Press, New York
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Ian H (2009) The WEKA data mining software: an update. SIGKDD Explorations 11(1)
Sutherland RL (2011) Endocrine resistance in breast cancer: new roles for ErbB3 and ErbB4. Breast Cancer Res 13(3):106
Hutcheson IR et al (2007) Heregulin beta1 drives gefitinib-resistant growth and invasion in tamoxifen-resistant MCF-7 breast cancer cells. Breast Cancer Res 9(4):50
Geistlinger L, Csaba G, Küffner R, Mulde N, Zimmer R (2011) From sets to graphs towards a realistic enrichment analysis of transcriptomic systems. Bioinformatics 27(13):366–373
Tarca AL, Draghici S, Khatri P, Hassan SS, Mittal P, Kim JS, Kim CJ, Kusanovic JP, Romero R (2009) A novel signaling pathway impact analysis. Bioinformatics 25(1):75–82
Judeh T, Johnson C, Kumar A, Zhu D (2013) TEAK: Topology Enrichment Analysis frameworK for detecting activated biological subpathways. Nucleic Acids Res 41(1):1425–1437
Nam S, Chang HR, Kim KT et al (2014) PATHOME: an algorithm for accurately detecting differentially expressed subpathways. Oncogene 33(41):4941–4951
Acknowledgment
This work was supported by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement N° 270089 and by the European Union (European Social Fund—ESF) and by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: Heracleitus II Investing in knowledge society through the European Social Fund.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media New York
About this protocol
Cite this protocol
Koumakis, L., Potamias, G., Tsiknakis, M., Zervakis, M., Moustakis, V. (2015). Integrating Microarray Data and GRNs. In: Guzzi, P. (eds) Microarray Data Analysis. Methods in Molecular Biology, vol 1375. Humana Press, New York, NY. https://doi.org/10.1007/7651_2015_252
Download citation
DOI: https://doi.org/10.1007/7651_2015_252
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3172-9
Online ISBN: 978-1-4939-3173-6
eBook Packages: Springer Protocols