In many scientific and medical settings, large-scale experiments are generating large quantities of data that lead to inferential problems involving multiple hypotheses. This has led to recent tremendous interest in statistical methods regarding the false discovery rate (FDR). Several authors...

We present a new instance of Laplace's second Law of Errors and show how it can be used in the analysis of data from microarray experiments. This error distribution is shown to fit microarray expression data much better than a normal distribution. The use of this distribution in a parametric...

Given a multiple testing situation, the null hypotheses that appear to have sufficiently low probabilities of truth may be rejected using a simple, nonparametric method based on decision theory. This applies not only to posterior levels of belief, but also to conditional probabilities in the...

The methodological advancement in microarray data analysis on the basis of false discovery rate (FDR) control, such as the q-value plots, allows the investigator to examine the FDR from several perspectives. However, when FDR control at the ``customary" levels 0.01, 0.05, or 0.1 does not provide...

Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test statistics across genes. The empirical Bayes methodology in the nonparametric and parametric formulations, as well as closely...

Modern methods of microarray data analysis are biased towards selecting those genes that display the most pronounced differential expression. The magnitude of differential expression does not necessarily indicate biological significance and other criteria are needed to supplement the information...

There is great interest in finding human genes expressed through pharmaceutical intervention, thus opening a genomic window into benefit and side-effect profiles of a drug. Human insight gained from FDA-required animal experiments has historically been limited, but in the case of gene expression...

An important application of gene expression microarray data is classification of biological samples or prediction of clinical and other outcomes. One necessary part of multivariate statistical analysis in such applications is dimension reduction. This paper provides a comparison study of three...

We present a statistical approach to scoring changes in activity of metabolic pathways from gene expression data. The method identifies the biologically relevant pathways with corresponding statistical significance. Based on gene expression data alone, only local structures of genetic networks...

One of the prevailing ideas in the literature on microarray data analysis is to pool the expression measures across genes and treat them as a sample drawn from some distribution. Several universal laws were proposed to analytically describe this distribution. This idea raises a number of...