Publications (3)12.7 Total impact
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Article: A model selection approach to discover age-dependent gene expression patterns using quantile regression models.
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ABSTRACT: It has been a long-standing biological challenge to understand the molecular regulatory mechanisms behind mammalian ageing. Harnessing the availability of many ageing microarray datasets, a number of studies have shown that it is possible to identify genes that have age-dependent differential expression (DE) or differential variability (DV) patterns. The majority of the studies identify "interesting" genes using a linear regression approach, which is known to perform poorly in the presence of outliers or if the underlying age-dependent pattern is non-linear. Clearly a more robust and flexible approach is needed to identify genes with various age-dependent gene expression patterns. Here we present a novel model selection approach to discover genes with linear or non-linear age-dependent gene expression patterns from microarray data. To identify DE genes, our method fits three quantile regression models (constant, linear and piecewise linear models) to the expression profile of each gene, and selects the least complex model that best fits the available data. Similarly, DV genes are identified by fitting and comparing two quantile regression models (non-DV and the DV models) to the expression profile of each gene. We show that our approach is much more robust than the standard linear regression approach in discovering age-dependent patterns. We also applied our approach to analyze two human brain ageing datasets and found many biologically interesting gene expression patterns, including some very interesting DV patterns, that have been overlooked in the original studies. Furthermore, we propose that our model selection approach can be extended to discover DE and DV genes from microarray datasets with discrete class labels, by considering different quantile regression models. In this paper, we present a novel application of quantile regression models to identify genes that have interesting linear or non-linear age-dependent expression patterns. One important contribution of this paper is to introduce a model selection approach to DE and DV gene identification, which is most commonly tackled by null hypothesis testing approaches. We show that our approach is robust in analyzing real and simulated datasets. We believe that our approach is applicable in many ageing or time-series data analysis tasks.BMC Genomics 01/2009; 10 Suppl 3:S16. · 4.07 Impact Factor -
Article: Differential variability analysis of gene expression and its application to human diseases.
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ABSTRACT: Current microarray analyses focus on identifying sets of genes that are differentially expressed (DE) or differentially coexpressed (DC) in different biological states (e.g. diseased versus non-diseased). We observed that in many human diseases, some genes have a significant increase or decrease in expression variability (variance). As these observed changes in expression variability may be caused by alteration of the underlying expression dynamics, such differential variability (DV) patterns are also biologically interesting. Here we propose a novel analysis for changes in gene expression variability between groups of samples, which we call differential variability analysis. We introduce the concept of differential variability (DV), and present a simple procedure for identifying DV genes from microarray data. Our procedure is evaluated with simulated and real microarray datasets. The effect of data preprocessing methods on identification of DV gene is investigated. The biological significance of DV analysis is demonstrated with four human disease datasets. The relationships among DV, DE and DC genes are investigated. The results suggest that changes in expression variability are associated with changes in coexpression pattern, which imply that DV is not merely stochastic noise, but informative signal. The R source code for differential variability analysis is available from the contact authors upon request.Bioinformatics 08/2008; 24(13):i390-8. · 5.47 Impact Factor -
Article: The effect of resveratrol on a cell model of human aging.
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ABSTRACT: The natural polyphenol resveratrol stimulates sirtuins and extends lifespan. Here resveratrol inhibited expression of replicative senescence marker INK4a in human dermal fibroblasts, and 47 of 19,000 genes from microarray experiments were differentially expressed. These included genes for growth, cell division, cell signaling, apoptosis, and transcription. Genes involved in Ras and ubiquitin pathways, Ras-GRF1, RAC3, and UBE2D3, were downregulated. The changes suggest resveratrol might alter sirtuin-regulated downstream pathways, rather than sirtuin activity. Serum deprivation and high confluency caused nuclear translocation of the SIRT1-regulated transcription factor FOXO3a. Our data indicate resveratrol's actions might cause FOXO recruitment to the nucleus.Annals of the New York Academy of Sciences 11/2007; 1114:407-18. · 3.15 Impact Factor
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2007
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University of Sydney
Sydney, New South Wales, Australia
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