Abstract
Modern high-throughput technologies like microarray, mass spectrometry or next generation sequencing enable biologists to measure cell products like metabolites, peptides, proteins or mRNA. With the advance of the technologies there are more and more experiments that do not only compare the cell products under two or more specific conditions, but also track them over time. These experiments usually yield short time series for a large number of cell products, but with only a few replicates. The noise in the measurements, but also the often strong biological variation of the replicates makes a coherent analysis of such data difficult. In this paper, we focus on methods to correct measurement errors or deviations caused by biological variation in terms of a time shift, different reaction speed and different reaction intensity for replicates. We propose a regression model that can estimate corresponding parameters that can be used to correct the data and to obtain better results in the further analysis.
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References
Bar-Joseph, Z.: Analyzing time series gene expression data. Bioinformatics 20, 2493–2503 (2004)
Bezdek, J.C., Hathaway, R.J.: Convergence of alternating optimization. Neural, Parallel Sci. Comput. 11(4), 351–368 (2003)
Bar-Joseph, Z., Gerber, G., Jaakkola, T., Gifford, D., Simon, I.: Comparing the continuous representation of time series expression profiles to identify differentially expressed genes. Proc. Natl. Acad. Sci. USA 100, 10146–10151 (2003)
Bar-Joseph, Z., Gerber, G., Jaakkola, T., Gifford, D., Simon, I.: Continuous representations of time series gene expression data. J. Computational Biology 3, 341–356 (2003)
Ernst, J., Bar-Joseph, Z.: STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7 (2006), doi:10.1186/1471–2105–7–191
Ndukum, J., Fonseca, L., Santos, H., Voit, E., Datta, S.: Statistical inference methods for sparse biological time series data. BMC Systems Biology 5(1), 57 (2011)
Hoaglin, D., Mosteller, F., Tukey, J.: Understanding Robust and Exploratory Data Analysis. Wiley, New York (2000)
Huber, P.: Robust Statistics. Wiley, New York (2004)
Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723 (1974)
R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2009)
Villén, J., Gygi, S.: The SCX/IMAC enrichment approach for global phosphorylation analysis by mass spectrometry. Nature Protocols 3, 1630–1638 (2008)
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Klawonn, F., Abidi, N., Berger, E., Jänsch, L. (2012). Curve Fitting for Short Time Series Data from High Throughput Experiments with Correction for Biological Variation. In: Hollmén, J., Klawonn, F., Tucker, A. (eds) Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, vol 7619. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34156-4_15
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DOI: https://doi.org/10.1007/978-3-642-34156-4_15
Publisher Name: Springer, Berlin, Heidelberg
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