Application of mixture models to detect differentially expressed genes

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches.

Using mixture models to detect differentially expressed genes

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local false discovery rate is provided for each gene, and it can be implemented so that the implied global false discovery rate is bounded as with the Benjamini-Hochberg methodology based on tail areas. The latter procedure is too conservative, unless it is modified according to the prior probability that a gene is not differentially expressed. An attractive feature of the mixture model approach is that it provides a framework for the estimation of this probability and its subsequent use in forming a decision rule. The rule can also be formed to take the false negative rate into account.

Cooperative and competitive adsorption of ethylene, ethane, nitrogen and argon on graphitized carbon black and in slit pores

In this paper we investigate the mixture adsorption of ethylene, ethane, nitrogen and argon on graphitized thermal carbon black and in slit pores by means of the Grand Canonical Monte Carlo simulations. Pure component adsorption isotherms on graphitized thermal carbon black are first characterized with the GCMC method, and then mixture simulations are carried out over a wide range of pore width, temperature, pressure and composition to investigate the cooperative and competitive adsorption of all species in the mixture. Results of mixture simulations are compared with the experimental data of ethylene and ethane (Friederich and Mullins, 1972) on Sterling FTG-D5 (homogeneous carbon black having a BET surface area of 13 m(2)/g) at 298 K and a pressure range of 1.3-93 kPa. Because of the co-operative effect, the Henry constant determined by the traditional chromatography method is always greater than that obtained from the volumetric method.

The effect of aluminium content on the mechanical properties and microstructure of die cast binary magnesium-aluminium alloys

This paper investigates the relationship between mechanical properties and microstructure in high pressure die cast binary Mg-Al alloys. As-cast test bars produced using high pressure die casting have been tested in tension in order to determine the properties for castings produced using this technique. It has been shown that increasing aluminium levels results in increases in yield strength and a decrease in ductility for these alloys. Higher aluminium levels also result in a decrease in creep rate at 150 degrees C. It has also been shown that an increase in aluminium levels results in an increase in the volume fraction of eutectic Mg17Al12 in the microstructure.

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

Motivation: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. Results: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

Dynamic binary translation using run-time feedbacks

Dynamic binary translation is the process of translating, modifying and rewriting executable (binary) code from one machine to another at run-time. This process of low-level re-engineering consists of a reverse engineering phase followed by a forward engineering phase. UQDBT, the University of Queensland Dynamic Binary Translator, is a machine-adaptable translator. Adaptability is provided through the specification of properties of machines and their instruction sets, allowing the support of different pairs of source and target machines. Most binary translators are closely bound to a pair of machines, making analyses and code hard to reuse. Like most virtual machines, UQDBT performs generic optimizations that apply to a variety of machines. Frequently executed code is translated to native code by the use of edge weight instrumentation, which makes UQDBT converge more quickly than systems based on instruction speculation. In this paper, we describe the architecture and run-time feedback optimizations performed by the UQDBT system, and provide results obtained in the x86 and SPARC® platforms.

Phase behavior of a phospholipid/fatty acid/water mixture studied in atomic detail

Molecular dynamics simulations have been used to study the phase behavior of a dipalmitoylphosphatidylcholine (DPPC)/palmitic acid (PA)/water 1:2:20 mixture in atomic detail. Starting from a random solution of DPPC and PA in water, the system adopts either a gel phase at temperatures below similar to 330 K or an inverted hexagonal phase above similar to 330 K in good agreement with experiment. It has also been possible to observe the direct transformation from a gel to an inverted hexagonal phase at elevated temperature (similar to 390 K). During this transformation, a metastable fluid lamellar intermediate is observed. Interlamellar connections or stalks form spontaneously on a nanosecond time scale and subsequently elongate, leading to the formation of an inverted hexagonal phase. This work opens the possibility of studying in detail how the formation of nonlamellar phases is affected by lipid composition and (fusion) peptides and, thus, is an important step toward understanding related biological processes, such as membrane fusion.

Mixture models for detecting differentially expressed genes in microarrays

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches.

Thin-layer isothermal drying of fructose, maltodextrin, and their mixture solutions

Solutions of fructose, maltodextrin (DE 5), and their mixtures at the ratios of 20:80, 40:60, 50:50, 60:40, and 80:20 were gelled with 1% agar-agar and dried under convective-conductive drying conditions. The thin slabs were maintained at isothermal drying condition of 30 and 50 degrees C. Yamamoto's simplified method based on regular regime approach was used to calculate the (effective) moisture diffusivity. Both the drying rates and the moisture diffusivity exhibited strong concentration dependence. The concentration dependence was stronger in the case of fructose and fructose rich solutions. Both the moisture diffusivity and drying rates of the mixture solutions were enhanced due to plasticization of fructose on maltodextrin, which is explained through free volume theory.

Performance analysis using equivariant kernel density estimator in nonlinear mixture

This paper investigates the performance analysis of separation of mutually independent sources in nonlinear models. The nonlinear mapping constituted by an unsupervised linear mixture is followed by an unknown and invertible nonlinear distortion, are found in many signal processing cases. Generally, blind separation of sources from their nonlinear mixtures is rather difficult. We propose using a kernel density estimator incorporated with equivariant gradient analysis to separate the sources with nonlinear distortion. The kernel density estimator parameters of which are iteratively updated to minimize the output independence expressed as a mutual information criterion. The equivariant gradient algorithm has the form of nonlinear decorrelation to perform the convergence analysis. Experiments are proposed to illustrate these results.