Simultaneous Assessment of Transcriptomic Variability and Tissue Effects in the Normal Mouse

Abstract

We consider two linear mixed models for the normal mouse data [Pritchard et al., 2001]. One models the log ₂ intensity measurements directly and the other models the log₂ ratios. In each approach, we treat a mouse as a fixed effect, and alternatively, we also model it as a random effect to assess its variability directly. We compare the results from these mixed model approaches. The models agree that array variance is much larger than other sources of variability, but differ somewhat in their lists of genes exhibiting the most significant mouse effects. Under a Bonferroni criterion, the ratio-based model we consider produces more genes with significant mouse effects than the intensity-based model, but fewer genes with significant tissue effects. Both models demonstrate a general statistical framework for concurrently estimating sources of variability and assessing their significance.