Statistical analysis of an RNA titration series evaluates microarray precision and sensitivity on a whole-array basis

Background  

Concerns are often raised about the accuracy of microarray technologies and the degree of cross-platform agreement, but there are yet no methods which can unambiguously evaluate precision and sensitivity for these technologies on a whole-array basis.     

EDC3 phosphorylation regulates growth and invasion through controlling P‐body formation and dynamics

Regulation of mRNA stability and translation plays a critical role in determining protein abundance within cells. Processing bodies (P‐bodies) are critical regulators of these processes. Here, we report that the Pim1 and 3 protein kinases bind to the P‐body protein enhancer of mRNA decapping 3 (EDC3) and phosphorylate EDC3 on serine (S)161, thereby modifying P‐body assembly. EDC3 phosphorylation is highly elevated in many tumor types, is reduced upon treatment of cells with kinase inhibitors, and blocks the localization of EDC3 to P‐bodies. Prostate cancer cells harboring an EDC3 S161A mutation show markedly decreased growth, migration, and invasion in tissue culture and in xenograft models. Consistent with these phenotypic changes, the expression of integrin β1 and α6 mRNA and protein is reduced in these mutated cells. These results demonstrate that EDC3 phosphorylation regulates multiple cancer‐relevant functions and suggest that modulation of P‐body activity may represent a new paradigm for cancer treatment.     

Marginalized Zero-Altered Models for Longitudinal Count Data

Count data often exhibit more zeros than predicted by common count distributions like the Poisson or negative binomial. In recent years, there has been considerable interest in methods for analyzing zero-inflated count data in longitudinal or other correlated data settings. A common approach has been to extend zero-inflated Poisson models to include random effects that account for correlation among observations. However, these models have been shown to have a few drawbacks, including interpretability of regression coefficients and numerical instability of fitting algorithms even when the data arise from the assumed model. To address these issues, we propose a model that parameterizes the marginal associations between the count outcome and the covariates as easily interpretable log relative rates, while including random effects to account for correlation among observations. One of the main advantages of this marginal model is that it allows a basis upon which we can directly compare the performance of standard methods that ignore zero inflation with that of a method that explicitly takes zero inflation into account. We present simulations of these various model formulations in terms of bias and variance estimation. Finally, we apply the proposed approach to analyze toxicological data of the effect of emissions on cardiac arrhythmias.