Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis

TLDR

Gene expression is influenced by genetic, environmental, demographic, technical, and other unknown factors that can introduce substantial heterogeneity. The authors propose surrogate variable analysis (SVA) to mitigate the adverse effects of such heterogeneity in expression studies. SVA is applied alongside standard analytical methods to model the relationship between expression and variables of interest, and is demonstrated on disease class, time‑course, and genetic studies. Incorporating SVA improves biological accuracy and reproducibility, whereas neglecting heterogeneity reduces power, induces gene‑level dependence, and generates spurious signals even in well‑designed randomized studies.

Abstract

It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce “surrogate variable analysis” (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.