A Study of the Correlation Structure of Microarray Gene Expression Data Based on Mechanistic Modeling of Cell Population Kinetics

Linlin Chen (), Lev Klebanov (), Anthony Almudevar (), Christoph Proschel () and Andrei Yakovlev
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Linlin Chen: Rochester Institute of Technology, School of Mathematical Sciences
Lev Klebanov: Charles University, Department of Probability and Statistics
Anthony Almudevar: University of Rochester, Department of Biostatistics and Computational Biology
Christoph Proschel: University of Rochester, Department of Biomedical Genetics
Andrei Yakovlev: University of Rochester, Department of Biostatistics and Computational Biology

A chapter in Statistical Modeling for Biological Systems, 2020, pp 47-61 from Springer

Abstract: Abstract Sample correlations between gene pairs within expression profiles are potentially informative regarding gene regulatory pathway structure. However, as is the case with other statistical summaries, observed correlation may be induced or suppressed by factors which are unrelated to gene functionality. In this paper, we consider the effect of heterogeneity on observed correlations, both at the tissue and subject level. Using gene expression profiles from highly enriched samples of three distinct embryonic glial cell types of the rodent neural tube, the effect of tissue heterogeneity on correlations is directly estimated for a simple two component model. Then, a stochastic model of cell population kinetics is used to assess correlation effects for more complex mixtures. Finally, a mathematical model for correlation effects of subject-level heterogeneity is developed. Although decomposition of correlation into functional and nonfunctional sources will generally not be possible, since this depends on nonobservable parameters, reasonable bounds on the size of such effects can be made using the methods proposed here.

Keywords: Gene expression data; Cellular kinetics (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-34675-1_3

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DOI: 10.1007/978-3-030-34675-1_3

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