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

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.