BACKGROUND: Two-dimensional polyacrylomide gel electrophoresis (2D gel, 2D PAGE, 2-DE) is a powerful tool for analyzing the proteome of a organism. Differential analysis of 2D gel images aims at finding proteins that change under different conditions, which leads to large-scale hypothesis testing as in microarray data analysis. Two-component empirical Bayes (EB) models have been widely discussed for large-scale hypothesis testing and applied in the context of genomic data. They have not been implemented for the differential analysis of 2D gel data. In the literature, the mixture and null densities of the test statistics are estimated separately. The estimation of the mixture density does not take into account assumptions about the null density. Thus, there is no guarantee that the estimated null component will be no greater than the mixture density as it should be. RESULTS: We present an implementation of a two-component EB model for the analysis of 2D gel images. In contrast to the published estimation method, we propose to estimate the mixture and null densities simultaneously using a constrained estimation approach, which relies on an iteratively re-weighted least-squares algorithm. The assumption about the null density is naturally taken into account in the estimation of the mixture density. This strategy is illustrated using a set of 2D gel images from a factorial experiment. The proposed approach is validated using a set of simulated gels. CONCLUSIONS: The two-component EB model is a very useful for large-scale hypothesis testing. In proteomic analysis, the theoretical null density is often not appropriate. We demonstrate how to implement a two-component EB model for analyzing a set of 2D gel images. We show that it is necessary to estimate the mixture density and empirical null component simultaneously. The proposed constrained estimation method always yields valid estimates and more stable results. The proposed estimation approach proposed can be applied to other contexts where large-scale hypothesis testing occurs.

In microarray experiments, expression profiles are obtained for thousands of genes under several treatments. Traditionally, most of the statistical techniques employed are concentrated around univariate methods. They ignore the inter-gene dependence and do not use any prior biological knowledge. Gene set analysis addresses both these concerns by analyzing together a group of correlated genes, for example genes that share a common biological function, chromosomal location, or regulation. In this paper we propose a multivariate analysis of covariance model (MANCOVA) for gene set analysis with covariates. Principal component analysis (PCA) is used to address the dimensionality problem. The two testing procedures presented are shown to perform well using simulations.

Microarray technology has provided an opportunity to simultaneously monitor the expression levels of a large number of genes in response to intentional perturbations. A necessary step towards successful use of microarray technology is background correction which aims to remove noise. One of the most popular algorithms for background correction is the Robust Multichip Average (RMA) procedure which relies on an unjustified parametric assumption. In this paper we first check the fitness of the RMA model using a graphical approach and then propose a new background correction method based on a semiparametric RMA model (semi-RMA). Evaluation of the proposed approach based on spike-in data and MAQC (Microarray Quality Control project) data shows our semi-RMA model provides a better fit to microarray data than other approaches.

MOTIVATION: We propose a stepwise approach to identify recombination breakpoints in a sequence alignment. The approach can be applied to any recombination detection method that uses a permutation test and provides estimates of breakpoints. RESULTS: We illustrate the approach by analyses of a simulated dataset and alignments of real data from HIV-1 and human chromosome 7. The presented simulation results compare the statistical properties of one-step and two-step procedures. More breakpoints are found with a two-step procedure than with a single application of a given method, particularly for higher recombination rates. At higher recombination rates, the additional breakpoints were located at the cost of only a slight increase in the number of falsely declared breakpoints. However, a large proportion of breakpoints still go undetected. AVAILABILITY: A makefile and C source code for phylogenetic profiling and the maximum chi2 method, tested with the gcc compiler on Linux and WindowsXP, are available at http://stat-db.stat.sfu.ca/stepwise/ CONTACT: [email protected].