Abstract
Pharmacogenomics and clinical studies that measure the temporal expression levels of patients can identify important pathways and biomarkers that are activated during disease progression or in response to treatment. However, researchers face a number of challenges when trying to combine expression profiles from these patients. Unlike studies that rely on lab animals or cell lines, individuals vary in their baseline expression and in their response rate. In this paper we present a generative model for such data. Our model represents patient expression data using two levels, a gene level which corresponds to a common response pattern and a patient level which accounts for the patient specific expression patterns and response rate. Using an EM algorithm we infer the parameters of the model. We used our algorithm to analyze multiple sclerosis patient response to Interferon-β. As we show, our algorithm was able to improve upon prior methods for combining patients data. In addition, our algorithm was able to correctly identify patient specific response patterns.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., et al.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisia by microarray hybridization. Mol. Biol. of the Cell. 9, 3273–3297 (1998)
Whitfield, M.L., Sherlock, G., Saldanha, A.J., Murray, J.I., et al.: Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell. 13(6), 1977–2000 (2002)
Inflammation and the Host Response to Injury, www.gluegrant.org
Bar-Joseph, Z.: Analyzing time series gene expression data. Bioinformatics 20(16), 2493–2503 (2004)
Nau, G.J., Richmond, J.F.L., Schlesinger, A., Jennings, E.G., et al.: Human Macrophage Activation Programs Induced by Bacterial Pathogens. PNAS 99, 1503–1508 (2002)
Weinstock-Guttman, B., Badgett, D., Patrick, K., Hartrich, L., et al.: Genomic effects of IFN-beta in multiple sclerosis patients. J. Immunol. 171(5), 1503–1508 (2002)
Sterrenburg, E., Turk, R., Peter, A.C., Hoen, P.A., van Deutekom, J.C., et al.: Large-scale gene expression analysis of human skeletal myoblast differentiation. Neuromuscul Disord. 14(8-9), 507–518 (2004)
Aach, J., Church, G.M.: Aligning gene expression time series with time warping algorithms. Bioinformatics 17, 495–508 (2001)
Bar-Joseph, Z., Gerber, G., Jaakkola, T.S., Gifford, D.K., Simon, I.: Continuous Representations of Time Series Gene Expression Data. Journal of Computational Biology 3-4, 39–48 (2003)
Calvano, S.E., Xiao, W., Richards, D.R., Felciano, R.M., et al.: A network-based analysis of systemic inflammation in humans. Nature 437, 1032–1037 (2005)
Gaffney, S., Smyth, P.: Joint Probabilistic Curve Clustering and Alignmen. In: Proceedings of The Eighteenth Annual Conference on Neural Information Processing Systems (NIPS) (2004)
Bar-Joseph, Z., Gerber, G., Jaakkola, T.S., Gifford, D.K., Simon, I.: Comparing the Continuous Representation of Time Series Expression Profiles to Identify Differentially Expressed Genes. PNAS 100(18), 10146–10151 (2003)
Storey, J.D., Xiao, W., Leek, J.T., Tompkins, R.G., Davis, R.W.: Significance analysis of time course microarray experiments. PNAS 102(36), 12837–12842 (2005)
Michalek, R., Tarantello, G.: Subharmonic solutions with prescribed minimal period for nonautonomous Hamiltonian systems. J. Diff. Eq. 72, 28–55 (1988)
Tarantello, G.: Subharmonic solutions for Hamiltonian systems via a ZZ p pseudoindex theory. Annali di Matematica Pura (to appear)
Troyanskaya, O., Cantor, M., et al.: Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001)
Sharan, R., Shamir, R.: Algorithmic Approaches to Clustering Gene Expression Data. Current Topics in Computational Biology, 269–300 (2002)
Piegl, L., Tiller, W.: The NURBS Book. Springer, New York (1997)
James, G., Hastie, T.: Functional Linear Discriminant Analysis for Irregularly Sampled Curves. Journal of the Royal Statistical Society, Series B 63, 533–550 (2001)
Xing, E.P., Jordan, M.I., Russell, S.: A generalized mean field algorithm for variational inference in exponential families. In: Proceedings of Uncertainty in Artificial Intelligence (UAI), pp. 583–591 (2003)
Supporting website: www.cs.cmu.edu/~zivbj/comb/combpatient.html
Achiron, A., et al.: Blood transcriptional signatures of multiple sclerosis: unique gene expression of disease activity. Ann. Neurol. 55(3), 410–417 (2004)
Takeba, Y., et al.: Txk, a member of nonreceptor tyrosine kinase of Tec family, acts as a Th1 cell-specific transcription factor and regulates IFN-gamma gene transcription. J. Immunol. 168(5), 2365–2370 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kaminski, N., Bar-Joseph, Z. (2006). A Patient-Gene Model for Temporal Expression Profiles in Clinical Studies. In: Apostolico, A., Guerra, C., Istrail, S., Pevzner, P.A., Waterman, M. (eds) Research in Computational Molecular Biology. RECOMB 2006. Lecture Notes in Computer Science(), vol 3909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732990_6
Download citation
DOI: https://doi.org/10.1007/11732990_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33295-4
Online ISBN: 978-3-540-33296-1
eBook Packages: Computer ScienceComputer Science (R0)