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This vignette includes locfdr’s complete help documentation, including usage tips, which could not fit in the R help file. It also demonstrates usage of locfdr through an example using the simulated data included in the package. 1 Description and Usage locfdr computes local false discovery rates, following the definitions and description in the references listed below. locfdr(zz, bre=120, df=7, pct=0, pct0=1/4, nulltype=1, type=0, plot=1, mult, mlests, main= " ", sw=0)

• A key aim of systems biology is to unravel the regulatory interactions among genes and gene products in a cell. Here we investigate a graphical model that treats the observed gene expression over time as realizations of random curves. This approach is centered around an estimator of dynamical pairwise correlation that takes account of the functional nature of the observed data. This allows to extend the graphical Gaussian modeling framework from i.i.d. data to analyze longitudinal genomic data. The new method is illustrated by analyzing highly replicated data from a genome experiment concerning the expression response of human T-cells to PMA and ionomicin treatment. Key-Words: • graphical model; longitudinal data; dynamical correlation; gene dependency networks. AMS Subject Classification:

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High-dimensional case-control analysis is encountered in many different settings in genomics. In order to rank genes accordingly, many different scores have been proposed, ranging from ad hoc modifications of the ordinary t statistic to complicated hierarchical Bayesian models. Here, we introduce the “shrinkage t ” statistic that is based on a novel and model-free shrinkage estimate of the variance vector across genes. This is derived in a quasi-empirical Bayes setting. The new rank score is fully automatic and requires no specification of parameters or distributions. It is computationally inexpensive and can be written analytically in closed form. Using a series of synthetic and three real expression data we studied the quality of gene rankings produced by the “shrinkage t ” statistic. The new score consistently leads to highly accurate rankings for the complete range of investigated data sets and all considered scenarios for acrossgene variance structures. KEYWORDS: high-dimensional case-control data, James-Stein shrinkage, limited-translation, quasi-empirical Bayes, regularized t statistic, variance shrinkage

Proceedings A mixture model approach to multiple testing for the genetic analysis of gene expression