How to cluster gene expression dynamics in response to environmental signals

Wang, Yaqun; Xu, Meng; Wang, Zhong; Tao, Ming; Zhu, Junjia; Wang, Li; Li, Runze; Berceli, Scott A.; Wu, Rongling

doi:10.1093/bib/bbr032

Cited by 54 publications

(60 citation statements)

References 55 publications

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“…Therefore we use genotype-oriented curve clustering to partition longitudinal phenotypic data and obtain a grouping probability matrix for different genotypes. Curve clustering was originally used for differentiating the genetic expression pattern under time sequence [18]. We use this method to divide biological traits into three groups corresponding to 3 genotypes in F2 population, and assume each group is controlled by a specific genotype.…”

Section: Genotype-oriented Curve Clusteringmentioning

confidence: 99%

Fast Computation of Significance Threshold in QTL Mapping of Dynamic Quantitative Traits

Wang¹,

Tian²,

Li³

et al. 2017

J Biom Biostat

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Functional Mapping is a popular statistical method in QTL mapping studies for longitudinal data. The threshold for declaring statistical significance of a QTL is commonly obtained through permutation tests, which can be time consuming. To improve the computational efficiency of a permutation test of mixture models used in Functional Mapping, we first quantified the correlation between QTL and longitudinal data, using a curve clustering method. Then, the QTLs which are highly correlated with the outcome were computed in the improved permutation tests. As a result, it reduces the amount of computation in permutation tests and speeds up the computation for Functional Mapping analysis. Simulation studies and real data analysis were conducted to demonstrate that the proposed approach can greatly improve the computational efficiency of QTL mapping without loss of accuracy.

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Section: Genotype-oriented Curve Clusteringmentioning

confidence: 99%

Fast Computation of Significance Threshold in QTL Mapping of Dynamic Quantitative Traits

Wang¹,

Tian²,

Li³

et al. 2017

J Biom Biostat

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“…For a particular gene, the difference of its time-dependent expression curve between the two treatments describes the pattern of how this gene responds to the change in the treatment’s environment. Wang et al (23) have developed a dynamic model for clustering genes into distinct groups based on the temporal patterns of their expression profiles in a relation to specific biological functions.…”

Section: Mutual Informationmentioning

confidence: 99%

“…In gene expression analysis, clustering is a first step towards studying gene function by subdividing the genes into a smaller number of categories and then comparing dissimilarities among the categories (23,27). In each category or group, there are a set of functionally similar genes.…”

Section: Mutual Information Between Groups and Environmentsmentioning

confidence: 99%

“…The model presented in this article will take advantage of mutual information in measuring the non-linear dependency of different variables to unravel the dynamic changes of network architecture in a response to the environment. The model was used to analyse experimental gene expression data obtained from rabbit vein grafts exposed to two different wall shear conditions, where these different environments resulted in two distinct adaptation phenotypes (22,23). The model has been validated through a simulation study.…”

Section: Introductionmentioning

confidence: 99%

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Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information

Wang

Chen

Wang

et al. 2013

Nucleic Acids Research

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The capacity of an organism to respond to its environment is facilitated by the environmentally induced alteration of gene and protein expression, i.e. expression plasticity. The reconstruction of gene regulatory networks based on expression plasticity can gain not only new insights into the causality of transcriptional and cellular processes but also the complex regulatory mechanisms that underlie biological function and adaptation. We describe an approach for network inference by integrating expression plasticity into Shannon’s mutual information. Beyond Pearson correlation, mutual information can capture non-linear dependencies and topology sparseness. The approach measures the network of dependencies of genes expressed in different environments, allowing the environment-induced plasticity of gene dependencies to be tested in unprecedented details. The approach is also able to characterize the extent to which the same genes trigger different amounts of expression in response to environmental changes. We demonstrated the usefulness of this approach through analysing gene expression data from a rabbit vein graft study that includes two distinct blood flow environments. The proposed approach provides a powerful tool for the modelling and analysis of dynamic regulatory networks using gene expression data from distinct environments.

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“…By linking these genes to environmental variation, one can identify the pattern of how genes alter their expression to cause phenotypic plasticity when the environment changes [15]. In practice, numerous genes have been identified to be up- or down-regulated in response to specific environmental signals [16, 17].…”

Section: Introductionmentioning

confidence: 99%

Modeling Expression Plasticity of Genes that Differentiate Drug-sensitive from Drug-resistant Cells to Chemotherapeutic Treatment

Wang

Han

et al. 2014

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By measuring gene expression at an unprecedented resolution and throughput, RNA-seq has played a pivotal role in studying biological functions. Its typical application in clinical medicine is to identify the discrepancies of gene expression between two different types of cancer cells, sensitive and resistant to chemotherapeutic treatment, in a hope to predict drug response. Here we modified and used a mechanistic model to identify distinct patterns of gene expression in response of different types of breast cancer cell lines to chemotherapeutic treatment. This model was founded on a mixture likelihood of Poisson-distributed transcript read data, with each mixture component specified by the Skellam function. By estimating and comparing the amount of gene expression in each environment, the model can test how genes alter their expression in response to environment and how different genes interact with each other in the responsive process. Using the modified model, we identified the alternations of gene expression between two cell lines of breast cancer, resistant and sensitive to tamoxifen, which allows us to interpret the expression mechanism of how genes respond to metabolic differences between the two cell types. The model can have a general implication for studying the plastic pattern of gene expression across different environments measured by RNA-seq.

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How to cluster gene expression dynamics in response to environmental signals

Cited by 54 publications

References 55 publications

Fast Computation of Significance Threshold in QTL Mapping of Dynamic Quantitative Traits

Fast Computation of Significance Threshold in QTL Mapping of Dynamic Quantitative Traits

Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information

Modeling Expression Plasticity of Genes that Differentiate Drug-sensitive from Drug-resistant Cells to Chemotherapeutic Treatment

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