Estimating the proportion of differentially expressed genes in comparative DNA microarray experiments

DNA microarray experiments, a well-established experimental technique, aim at understanding the function of genes in some biological processes. One of the most common experiments in functional genomics research is to compare two groups of microarray data to determine which genes are differentially expressed. In this paper, we propose a methodology to estimate the proportion of differentially expressed genes in such experiments. We study the performance of our method in a simulation study where we compare it to other standard methods. Finally we compare the methods in real data from two toxicology experiments with mice.Comment: Published at http://dx.doi.org/10.1214/074921707000000076 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Application of Volcano Plots in Analyses of mRNA Differential Expressions with Microarrays

Volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log10(p-value) from the t test). We review the basic and an interactive use of the volcano plot, and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide an unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility to apply volcano plots to other fields beyond microarray.Comment: 8 figure

A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data

Microarray time course (MTC) gene expression data are commonly collected to study the dynamic nature of biological processes. One important problem is to identify genes that show different expression profiles over time and pathways that are perturbed during a given biological process. While methods are available to identify the genes with differential expression levels over time, there is a lack of methods that can incorporate the pathway information in identifying the pathways being modified/activated during a biological process. In this paper we develop a hidden spatial-temporal Markov random field (hstMRF)-based method for identifying genes and subnetworks that are related to biological processes, where the dependency of the differential expression patterns of genes on the networks are modeled over time and over the network of pathways. Simulation studies indicated that the method is quite effective in identifying genes and modified subnetworks and has higher sensitivity than the commonly used procedures that do not use the pathway structure or time dependency information, with similar false discovery rates. Application to a microarray gene expression study of systemic inflammation in humans identified a core set of genes on the KEGG pathways that show clear differential expression patterns over time. In addition, the method confirmed that the TOLL-like signaling pathway plays an important role in immune response to endotoxins.Comment: Published in at http://dx.doi.org/10.1214/07--AOAS145 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Identification of meat spoilage gene biomarkers in Pseudomonas putida using gene profiling

While current food science research mainly focuses on microbial changes in food products that lead to foodborne illnesses, meat spoilage remains as an unsolved problem for the meat industry. This can result in important economic losses, food waste and loss of consumer confidence in the meat market. Gram-negative bacteria involved in meat spoilage are aerobes or facultative anaerobes. These represent the group with the greatest meat spoilage potential, where Pseudomonas tend to dominate the microbial consortium under refrigeration and aerobic conditions. Identifying stress response genes under different environmental conditions can help researchers gain an understanding of how Pseudomonas adapts to current packaging and storage conditions. We examined the gene expression profile of Pseudomonas putida KT2440, which plays an important role in the spoilage of meat products. Gene expression profiles were evaluated to select the most differentially expressed genes at different temperatures (30 °C and 10 °C) and decreasing glucose concentrations, in order to identify key genes actively involved with the spoilage process. A total of 739 and 1269 were found to be differentially expressed at 30 °C and 10 °C respectively; of which 430 and 568 genes were overexpressed, and 309 and 701 genes were repressed at 30 °C and 10 °C respectively

GaGa: A parsimonious and flexible model for differential expression analysis

Hierarchical models are a powerful tool for high-throughput data with a small to moderate number of replicates, as they allow sharing information across units of information, for example, genes. We propose two such models and show its increased sensitivity in microarray differential expression applications. We build on the gamma--gamma hierarchical model introduced by Kendziorski et al. [Statist. Med. 22 (2003) 3899--3914] and Newton et al. [Biostatistics 5 (2004) 155--176], by addressing important limitations that may have hampered its performance and its more widespread use. The models parsimoniously describe the expression of thousands of genes with a small number of hyper-parameters. This makes them easy to interpret and analytically tractable. The first model is a simple extension that improves the fit substantially with almost no increase in complexity. We propose a second extension that uses a mixture of gamma distributions to further improve the fit, at the expense of increased computational burden. We derive several approximations that significantly reduce the computational cost. We find that our models outperform the original formulation of the model, as well as some other popular methods for differential expression analysis. The improved performance is specially noticeable for the small sample sizes commonly encountered in high-throughput experiments. Our methods are implemented in the freely available Bioconductor gaga package.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS244 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Screening for Differentially Expressed Genes: Are Multilevel Models Helpful?

Screening for changes in gene expression across biological conditions using microarrays is now a common tool in biology. Efficient use of these data for identifying important biological hypotheses is inherently a statistical problem. In this paper we present a broad Bayesian multilevel framework for developing computationally fast shrinkage-based screening tools for this purpose. Our scheme makes it easy to adapt the choice of statistics to the goals of the analysis and to the genomic distributions of signal and noise. We empirically investigate the extent to which these shrinkage-based statistics improve performance, and the conditions under which such improvements takes place. Our evaluation uses both extensive simulations and controlled biological experiments. The experimental data include a so-called spike-in experiment, in which the target biological signal is known, and a two-sample experiment, which illustrates the typical conditions in which the methods studied are applied. Our results emphasize two important practical concerns that are not receiving sufficient attention in applied work in this area. First, while shrinkage strategies based on multilevel models are able to improve selection performance, they require careful verification of the assumptions on the relationship between signal and noise. Incorrect specification of this relationship can negatively affect a selection procedure. Because this inter-gene relationship is generally identifiable in genomic experiments, we suggest a simple diagnostic plot to assist model checking. Secondly, no statistic performs optimally across two common categories of experimental goals: selecting genes with large changes, and selecting genes with reliably measured changes. Therefore, careful consideration of analysis goals is critical in the choice of the approach taken