Orphan quality control shapes network dynamics and gene expression

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How to cluster gene expression dynamics in response to environmental signals

Organisms usually cope with change in the environment by altering the dynamic trajectory of gene expression to adjust the complement of active proteins. The identification of particular sets of genes whose expression is adaptive in response to environmental changes helps to understand the mechanistic base of gene-environment interactions essential for organismic development. We describe a computational framework for clustering the dynamics of gene expression in distinct environments through Gaussian mixture fitting to the expression data measured at a set of discrete time points. We outline a number of quantitative testable hypotheses about the patterns of dynamic gene expression in changing environments and gene-environment interactions causing developmental differentiation. The future directions of gene clustering in terms of incorporations of the latest biological discoveries and statistical innovations are discussed. We provide a set of computational tools that are applicable to modeling and analysis of dynamic gene expression data measured in multiple environments.     

Assessing chronic liver toxicity based on relative gene expression data

The risk associated with exposure to hepatotoxic drugs is difficult to quantify. Animal experiments to assess their chronic toxicological impact are time consuming. New quantitative approaches to correlate gene expression changes caused by drug exposure to chronic toxicity are required. This article proposes a mathematical model entitled Toxicologic Prediction Network (TPN) to assess chronic hepatotoxicity based on subchronic hepatic gene expression data in rats. A directed graph accounts for the interactions between the drugs, differentially expressed genes and chronic hepatotoxicity. A knowledge-based mathematical model estimates phenotypical exposure risk such as toxic hepatopathy, diffuse fatty change and hepatocellular adenoma for rats. The network's edges encoding the interaction strength are determined by solving an inversion problem that minimizes the difference between the observed and the predicted relative gene expressions as well as the chronic toxicity data. A realistic case study demonstrates how chronic health risk of three halogenated aromatic hydrocarbons can be inferred from subchronic gene expression data. The advantages of the TPN are further demonstrated through two novel applications: Estimation of toxicological impact of new drugs and drug mixtures as well as rigorous determination of the optimal drug formulation to achieve maximum potency with minimum side-effects. Prediction of animal toxicity may be relevant for assessing risk for humans in the future.     

Classification based upon gene expression data: bias and precision of error rates

MOTIVATION: Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. RESULTS: Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. AVAILABILITY: R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp     

Temperature influences upon vascular dynamics in cattle measured by Doppler ultrasonography

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Doppler-image ultrasonography was used to document vascular changes and blood flow rates of cattle (Bos taurus) under hot (32.7 °C) and cold (8.1 °C) conditions for 24 h.     

Geometry of gene expression dynamics

MOTIVATION: A gene expression trajectory moves through a high dimensional space where each axis represents the mRNA abundance of a different gene. Genome wide gene expression has a dynamic structure, especially in studies of development and temporal response. Both visualization and analyses of such data require an explicit attention to the temporal structure. RESULTS: Using three cell cycle trajectories from Saccharomyces cerevisiae to illustrate, we present several techniques which reveal the geometry of the data. We import phase-delay time plots from chaotic systems theory as a dynamic data visualization device and show how these plots capture important aspects of the trajectories. We construct an objective function to find an optimal two-dimensional projection of the cell cycle, demonstrate that the system returns to this plane after three different initial perturbations, and explore the conditions under which this geometric approach outperforms standard approaches such as singular value decomposition and Fourier analysis. Finally, we show how a geometric analysis can isolate distinct parts of the trajectories, in this case the initial perturbation versus the cell cycle. CONTACT: junhyong.kim@yale.edu     

Assessing reliability of gene clusters from gene expression data

The rapid development of microarray technologies has raised many challenging problems in experiment design and data analysis. Although many numerical algorithms have been successfully applied to analyze gene expression data, the effects of variations and uncertainties in measured gene expression levels across samples and experiments have been largely ignored in the literature. In this article, in the context of hierarchical clustering algorithms, we introduce a statistical resampling method to assess the reliability of gene clusters identified from any hierarchical clustering method. Using the clustering trees constructed from the resampled data, we can evaluate the confidence value for each node in the observed clustering tree. A majority-rule consensus tree can be obtained, showing clusters that only occur in a majority of the resampled trees. We illustrate our proposed methods with applications to two published data sets. Although the methods are discussed in the context of hierarchical clustering methods, they can be applied with other cluster-identification methods for gene expression data to assess the reliability of any gene cluster of interest. Electronic Publication     

Plant expression signals of the Agrobacterium T-cyt gene.

Within the 5' and 3' non-coding regions of the T-cyt gene from the octopine T-DNA of Agrobacterium tumefaciens sequences required for expression of this gene in plant cells were identified by deletion mutagenesis. The results show that 184 bp of the 5' non-coding region and 270 bp of the 3' non-coding region are sufficient for wild-type expression. Within the 5' non-coding region two essential expression signals were identified: (1.) an activator element located between -185 and -129 with respect to the ATG start codon and (2.) one out of two TATA boxes. Deletions of the activator element or the two TATA boxes resulted in nonfunctional genes. Deletion of the upstream TATA box and both putative CAAT boxes did not significantly affect expression. Within the 3' non-coding region, the polyadenylation box most distal to the stop codon was not essential for expression, but sequences more upstream, including a second polyadenylation box were found to be required for wild-type expression.     

Inferring models of gene expression dynamics

We study the problem of identifying genetic networks in which expression dynamics are modeled by a differential equation that uses logical rules to specify time derivatives. We make three main contributions. First, we describe computationally efficient procedures for identifying the structure and dynamics of such networks from expression time series. Second, we derive predictions for the expected amount of data needed to identify randomly generated networks. Third, if expression values are available for only some of the genes, we show that the structure of the network for these "visible" genes can be identified and that the size and overall complexity of the network can be estimated. We validate these procedures and predictions using simulation experiments based on randomly generated networks with up to 30,000 genes and 17 distinct regulators per gene and on a network that models floral morphogenesis in Arabidopsis thaliana.     

Assessing numerical dependence in gene expression summaries with the jackknife expression difference

Statistical methods to test for differential expression traditionally assume that each gene's expression summaries are independent across arrays. When certain preprocessing methods are used to obtain those summaries, this assumption is not necessarily true. In general, the erroneous assumption of dependence results in a loss of statistical power. We introduce a diagnostic measure of numerical dependence for gene expression summaries from any preprocessing method and discuss the relative performance of several common preprocessing methods with respect to this measure. Some common preprocessing methods introduce non-trivial levels of numerical dependence. The issue of (between-array) dependence has received little if any attention in the literature, and researchers working with gene expression data should not take such properties for granted, or they risk unnecessarily losing statistical power.     

Time-course data prediction for repeatedly measured gene expression

Variability in time course gene expression data is a natural phenomenon. The intention of this work is to predict the future time point data through observed sample data point. The Bayesian inference is carried to serve the objective. A total of 6 replicates 3 time point's data of 218 genes expression is adopted to illustrate the method. The estimates are found consistent with HPD interval to predict the future time point gene expression value. This proposed method can be adopted in other gene expression data setup to predict the future time course data.     

Jasmonate and salicylate as global signals for defense gene expression

Remarkably, only a few low molecular mass signals, including jasmonic acid, ethylene and salicylic acid, upregulate the expression of scores of defense-related genes. Using these regulators, the plant fine-tunes its defense gene expression against aggressors which, in some cases, may be able to disrupt or amplify plant defense signal pathways to their own ends.     

Assessing sources of variability in microarray gene expression data

Experiments using microarrays abound in genomic research, yet one factor remains in question. Without replication, how much stock can we put into the findings of microarray experiments? In addition, there is a growing desire to integrate microarray data with other molecular databases. To accomplish this in a scientifically acceptable manner, we must be able to measure the validity and quality of microarray data. Otherwise, it would be the weakest link in any integration process. Validating and evaluating the quality of data requires the ability to determine the reproducibility of results. Data obtained from a microarray experiment designed as a feasibility test provided a unique opportunity to partition and quantify several sources of variation that are likely to be present in most microarray experiments. We use this opportunity to discuss the origins of variability observed in microarray experiments and provide some suggestions for how to minimize or avoid them when designing an experiment.