A Laplace mixture model for identification of differential expression in microarray experiments

Bhowmick, Debjani; Davison, A G; Goldstein, Darlene R.; Ruffieux, Yann

doi:10.1093/biostatistics/kxj032

Cited by 37 publications

(35 citation statements)

References 12 publications

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“…AL distribution was introduced in Purdom and Holmes (2005) in the analysis of gene expression data to capture the peak at the center as well as the asymmetry in the distribution. The Laplace mixture model as a long tailed alternative to the normal distribution in identifying differentially expressed genes in microarray experiments was introduced in Bhowmick et al (2006). The Cauchy distribution was applied in Khondoker et al (2006) in modeling microarray experiments which can estimate gene expressions by taking the outliers into account.…”

Section: Resultsmentioning

confidence: 99%

“…The gene expression was also fitted by using an asymmetric Laplace distribution (Purdom & Holmes, 2005). However, in order to take outliers into account, the Cauchy distribution has been used for estimating gene expressions using data from multiple-laser scans (Khondoker, Glasbey, & Worton, 2006), and the Laplace mixture model was introduced as a long tailed alternative to the normal distribution (Bhowmick, Davison, Goldstein, & Ruffieux, 2006).…”

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confidence: 99%

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Application of Esscher Transformed Laplace Distribution in Microarray Gene Expression Data

Shanmugasundaram

George

Jeyaseelan

2016

J. Mod. App. Stat. Meth.

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Microarrays allow the study of the expression profile of hundreds to thousands of genes simultaneously. These expressions could be from treated samples and the healthy controls. The Esscher transformed Laplace distribution is used to fit microarray expression data as compared to Normal and Laplace distributions. The Maximum Likelihood Estimation procedure is used to estimate the parameters of the distribution. R codes are developed to implement the estimation procedure. A simulation study is carried out to test the performance of the algorithm. AIC and BIC criterion are used to compare the distributions. It is shown that the fit of the Esscher transformed Laplace distribution is better as compared to Normal and standard Laplace distributions.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Application of Esscher Transformed Laplace Distribution in Microarray Gene Expression Data

Shanmugasundaram

George

Jeyaseelan

2016

J. Mod. App. Stat. Meth.

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“…Gene selection methods based upon selection of highly differentially expressed genes are widely used. In this study, highly expressed genes are selected using common statistical methods (22), and genes with specific temporal expression patterns are selected with a specific mixture model, which is also used to group genes into time clusters.…”

Section: Resultsmentioning

confidence: 99%

“…Gene selection was done in two steps. First we selected a large number of highly expressed genes based on a Laplace mixture model (step 1) (22). We then used a mixture model, estimated by an expectation-maximization (EM) algorithm, to select, among the remaining genes, those with a specific pattern of expression (step 2).…”

Section: Methodsmentioning

confidence: 99%

Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia

Vallat

Kemper

Jung

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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Cellular behavior is sustained by genetic programs that are progressively disrupted in pathological conditions-notably, cancer. High-throughput gene expression profiling has been used to infer statistical models describing these cellular programs, and development is now needed to guide orientated modulation of these systems. Here we develop a regression-based model to reverseengineer a temporal genetic program, based on relevant patterns of gene expression after cell stimulation. This method integrates the temporal dimension of biological rewiring of genetic programs and enables the prediction of the effect of targeted gene disruption at the system level. We tested the performance accuracy of this model on synthetic data before reverse-engineering the response of primary cancer cells to a proliferative (protumorigenic) stimulation in a multistate leukemia biological model (i.e., chronic lymphocytic leukemia). To validate the ability of our method to predict the effects of gene modulation on the global program, we performed an intervention experiment on a targeted gene. Comparison of the predicted and observed gene expression changes demonstrates the possibility of predicting the effects of a perturbation in a gene regulatory network, a first step toward an orientated intervention in a cancer cell genetic program.temporal gene network | lasso penalty | lymphoproliferative disorder | B-cell antigen receptor | predicted intervention C ellular behavior is conditioned mostly by functional genetic programs in response to various environmental signals, as initially shown in simple organisms (1, 2). External stimuli activate cellular surface receptors that trigger multiple signaling cascades in cells. The ultimate targets of these cascades are transcription factors that initiate sequential transcriptional activations with high temporal coordination. The first activated genes, at early time-points, after cell stimulation, essentially have a fast and transient expression; their gene products activate expression of various target genes downstream of transcriptional regulation cascades. These latter genes have longer-lasting expression, and their products sustain the adapted cellular response to initial environmental stimulation (3). These functional molecular networks are disrupted in various pathologies (e.g., cancer) where genetic aberrations lead to tumoral cellular programs. Since the first application of high-throughput technologies for measuring gene expression, a number of methods have been proposed to reverse-engineer gene regulatory networks; considered to be the underlying structure of these genetic programs (4). These different methods were developed to infer gene potential interactions and to describe these networks at the system level (5). The next important goal was to develop statistical tools to control these systems (6). One of the key challenges is to determine which critical genes whose perturbed expression drive these pathological genetic programs toward targeted states. We propose here a predictive met...

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“…The method combines information across many genes and is proved to be more stable than the t-test. [9] noted that the assumption of normality for the components of the mixture is quite strong and in many cases not realistic. They suggested the use of a Laplace distribution as a long-tailed alternative to the Normal and modeled the mean relative expression levels as a Laplace mixture.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Mixture Model for Detecting Differentially Expressed Genes in Microarray Experiments

Razzaghi¹,

Zhang²

2016

JAS

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To determine the genes that are differentially expressed between samples in microarray experiments, traditionally the expression levels were assessed by taking the intensity levels at a spot on the array and flagging the gene if the magnitude of the fold change exceeded a threshold. Recently, however, there has been much effort to improve the methodology by incorporating the variability of the intensity ratios. While the Student's t-test and several of its variants have been proposed by several authors, a methodology that has found widespread popularity is the application of the mixture model in a hierarchical approach whereby the mean of the distribution of the normalized log ratios is assumed to be a random variable having a mixture of two components. One component is a point mass distribution concentrated at zero to represent the non-differentially expressed genes and another component is a suitable distribution with zero mean to represent the differentially expressed genes. The normal and the Laplace distributions have been previously suggested for the differentially expressed genes component of the mixture. But, once again, the symmetry assumption can make these distributions unsuitable. Here, we take a more general approach and apply the beta-normal model to describe the distribution of the mean of the differentially expressed genes. The advantage of this approach is that we no longer assume symmetry for the distribution and let the data determine its shape. We show that our approach includes the earlier results based on normality assumption as a special case. Simulation results demonstrate that there are advantages in using the more general beta-normal distribution. An example with a microarray experimental data is utilized to provide further illustration.

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A Laplace mixture model for identification of differential expression in microarray experiments

Cited by 37 publications

References 12 publications

Application of Esscher Transformed Laplace Distribution in Microarray Gene Expression Data

Application of Esscher Transformed Laplace Distribution in Microarray Gene Expression Data

Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia

A Generalized Mixture Model for Detecting Differentially Expressed Genes in Microarray Experiments

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