Abstract
Multivariate microarray gene expression data are commonly collected to study the genomic responses under ordered conditions such as over increasing/decreasing dose levels or over time during biological processes, where the expression levels of a give gene are expected to be dependent. One important question from such multivariate gene expression experiments is to identify genes that show different expression patterns over treatment dosages or over time; these genes can also point to the pathways that are perturbed during a given biological process. Several empirical Bayes approaches have been developed for identifying the differentially expressed genes in order to account for the parallel structure of the data and to borrow information across all the genes. However, these methods assume that the genes are independent. In this paper, we introduce an alternative empirical Bayes approach for analysis of multivariate gene expression data by assuming a discrete Markov random field (MRF) prior, where the dependency of the differential expression patterns of genes on the networks are modeled by a Markov random field. Simulation studies indicated that the method is quite effective in identifying genes and the modified subnetworks and has higher sensitivity than the commonly used procedures that do not use the pathway information, with similar observed false discovery rates. We applied the proposed methods for analysis of a microarray time course gene expression study of TrkA- and TrkB-transfected neuroblastoma cell lines and identified genes and subnetworks on MAPK, focal adhesion, and prion disease pathways that may explain cell differentiation in TrkA-transfected cell lines.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lehmann KP, Phillips S, Sar M, Foster PMD, Gaido KW (2004) Dose-dependent alterations in gene expression and testosterone synthesis in the fetal testes of male rates exposed to Di (n-butyl) phthalate. Toxicol Sci 81:60–68
Seidel S, Stott W, Kan H, Sparrow B, Gollapudi B (2006) Gene expression dose–response of liver with a genotoxic and nongenotoxic carcinogen. Int J Toxicol 25:57–64
Yuan M, Kendziorski C (2006) Hidden Markov models for microarray time course data under multiple biological conditions (with discussion). J Am Stat Assoc 101(476):1323–1340
Tai YC, Speed T (2006) A multivariate empirical Bayes statistic for replicated microarray time course data. Ann Stat 34:2387–2412
Hong FX, Li H (2006) Functional hierarchical models for identifying genes with different time-course expression profiles. Biometrics 62:534–544
Wei Z, Li H (2008) A hidden spatial–temporal Markov random field model for network-based analysis of time course gene expression data. Ann Appl Stat 2(1):408–429
Wei Z, Li H (2007) A Markov random field model for network-based analysis of genomic data. Bioinformatics 23:1537–1544
Wei P, Pan W (2008) Incorporating gene networks into statistical tests for genomic data via a spatially correlated mixture model. Bioinformatics 24:404–411
Besag J (1974) Spatial interaction and the statistical analysis of lattice systems. J R Stat Soc Ser B 36:192–225
Besag J (1986) On the statistical analysis of dirty pictures. J R Stat Soc Ser B 48:259–302
Aitchison J, Dunsmore IR (1975) Statistical prediction analysis. Cambridge University Press, London
Smyth GK (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3(1):Article 3
Brodeur GM (2003) Neuroblastoma: biological insights into a clinical enigma. Nat Rev - Cancer 3:203–216
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264
Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: database for annotation, visualization and integrated discovery. Gen Biol 4:P3
Tombes RM, Auer KL, Mikkelsen R, et al (1998) The mitogenactivated protein (MAP) kinase cascade can either stimulate or inhibit DNA synthesis in primary cultures of rat hepatocytes depending upon whether its activation is acute/phasic or chronic. J biochemistry 330(Pt 3):1451–1460
Kao S, Jaiswal RK, Kolch W, Landreth GE (2001) Identification of the mechanisms regulating the differential activation of the MAPK cascade by epidermal growth factor and nerve growth factor in PC12 cells. J Biol Chem 276(21):18169–18177
Marshall CJ (1995) Specificity of receptor tyrosine kinase signaling: transient versus sustained extracellular signal-regulated kinase activation. Cell 80(2):179–185
Qui MS, Green SH (1992) PC12 cell neuronal differentiation is associated with prolonged p21ras activity and consequent prolonged ERK activity. Neuron 9(4):705–717
Basson MD (2008) An intracellular signal pathway that regulates cancer cell adhesion in response to extracellular forces. Cancer Res 68(1):2–4
Liu W, Bloom DA, Cance WG, Kurenova EV, Golubovskaya VM, Hochwald SN (2008) FAK and IGF-IR interact to provide survival signals in human pancreatic adenocarcinoma cells. Carcinogenesis 29(6):1096–1107
Beierle EA, Trujillo A, Nagaram A, Kurenova EV et al (2007) N-MYC regulates focal adhesion kinase expression in human neuroblastoma. J Biol Chem 282(17):12503–12516
Alfarano C, Andrade CE, Anthony K, Hahroos N, Bajec M et al (2005) The biomolecular interaction network database and related tools 2005 update. Nucleic Acids Res 33:D418–D424
Acknowledgments
This research was supported by NIH grants R01-CA127334 and P01-CA097323. We thank Mr. Edmund Weisberg, MS at Penn CCEB for editorial assistance.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this protocol
Cite this protocol
Zhi, W., Minturn, J., Rappaport, E., Brodeur, G., Li, H. (2013). Network-Based Analysis of Multivariate Gene Expression Data. In: Yakovlev, A., Klebanov, L., Gaile, D. (eds) Statistical Methods for Microarray Data Analysis. Methods in Molecular Biology, vol 972. Humana Press, New York, NY. https://doi.org/10.1007/978-1-60327-337-4_8
Download citation
DOI: https://doi.org/10.1007/978-1-60327-337-4_8
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-60327-336-7
Online ISBN: 978-1-60327-337-4
eBook Packages: Springer Protocols