Generalized estimating equations by considering additive terms for analyzing time-course gene sets data

Time-course gene sets are collections of predefined groups of genes in some patients gathered over time. The analysis of time-course gene sets for testing gene sets which vary significantly over time is an important context in genomic data analysis. In this paper, the method of generalized estimating equations (GEEs), which is a semi-parametric approach, is applied to time-course gene set data. We propose a special structure of working correlation matrix to handle the association among repeated measurements of each patient over time. Also, the proposed working correlation matrix permits estimation of the effects of the same gene among different patients. The proposed approach is applied to an HIV therapeutic vaccine trial (DALIA-1 trial). This data set has two phases: pre-ATI and post-ATI which depend on a vaccination period. Using multiple testing, the significant gene sets in the pre-ATI phase are detected and data on two randomly selected gene sets in the post-ATI phase are also analyzed. Some simulation studies are performed to illustrate the proposed approaches. The results of the simulation studies confirm the good performance of our proposed approach.     

Performance of Gene Selection and Classification Methods in a Microarray Setting: A Simulation Study

In a previous article, we investigated the performance of several classification methods for cDNA-microarrays. Via simulations, various experimental settings could be explored without having to conduct expensive microarray studies. For the selection of genes, on which classification was based, one particular method was applied. Gene selection is, however, a very important aspect of classification. We extend the previous study by considering several gene selection methods. Furthermore, the stability of the methods with respect to distributional assumptions is examined by also considering data simulated from a symmetric and asymmetric Laplace distribution, in addition to normally distributed microarray data.     

Clustering gene expression time course data using mixtures of multivariate t-distributions

Clustering gene expression time course data is an important problem in bioinformatics because understanding which genes behave similarly can lead to the discovery of important biological information. Statistically, the problem of clustering time course data is a special case of the more general problem of clustering longitudinal data. In this paper, a very general and flexible model-based technique is used to cluster longitudinal data. Mixtures of multivariate t-distributions are utilized, with a linear model for the mean and a modified Cholesky-decomposed covariance structure. Constraints are placed upon the covariance structure, leading to a novel family of mixture models, including parsimonious models. In addition to model-based clustering, these models are also used for model-based classification, i.e., semi-supervised clustering. Parameters, including the component degrees of freedom, are estimated using an expectation-maximization algorithm and two different approaches to model selection are considered. The models are applied to simulated data to illustrate their efficacy; this includes a comparison with their Gaussian analogues—the use of these Gaussian analogues with a linear model for the mean is novel in itself. Our family of multivariate t mixture models is then applied to two real gene expression time course data sets and the results are discussed. We conclude with a summary, suggestions for future work, and a discussion about constraining the degrees of freedom parameter.     

美国梦·欧洲梦·中国梦

新科技的发展导致人类的时间意识和空间意识发生了根本变化。20世纪的苦难历程使现代性的"经济增长万能"和"个人绝对自由"受到质疑。这些人类历史上空前的大变动都要求我们重新定义人类状况,重新考虑人类的生存意义和生存方式。J.里夫金用"美国梦"和"欧洲梦"两个概念来说明在不同时空中的不同的思维方式与生存方式,并认为正在形成的"中国梦"必将对整个人类的未来产生深远影响。     

Gene Pathways Discovery in Asbestos-Related Diseases using Local Causal Discovery Algorithm

To learn about the progression of a complex disease, it is necessary to understand the physiology and function of many genes operating together in distinct interactions as a system. In order to significantly advance our understanding of the function of a system, we need to learn the causal relationships among its modeled genes. To this end, it is desirable to compare experiments of the system under complete interventions of some genes, e.g., knock-out of some genes, with experiments of the system without interventions. However, it is expensive and difficult (if not impossible) to conduct wet lab experiments of complete interventions of genes in animal models, e.g., a mouse model. Thus, it will be helpful if we can discover promising causal relationships among genes with observational data alone in order to identify promising genes to perturb in the system that can later be verified in wet laboratories. While causal Bayesian networks have been actively used in discovering gene pathways, most of the algorithms that discover pairwise causal relationships from observational data alone identify only a small number of significant pairwise causal relationships, even with a large dataset. In this article, we introduce new causal discovery algorithms—the Equivalence Local Implicit latent variable scoring Method (EquLIM) and EquLIM with Markov chain Monte Carlo search algorithm (EquLIM-MCMC)—that identify promising causal relationships even with a small observational dataset.     

Analysis of Survival Data with Group Lasso

Identification of influential genes and clinical covariates on the survival of patients is crucial because it can lead us to better understanding of underlying mechanism of diseases and better prediction models. Most of variable selection methods in penalized Cox models cannot deal properly with categorical variables such as gender and family history. The group lasso penalty can combine clinical and genomic covariates effectively. In this article, we introduce an optimization algorithm for Cox regression with group lasso penalty. We compare our method with other methods on simulated and real microarray data sets.     

Investigate Data Dependency for Dynamic Gene Regulatory Network Identification through High-dimensional Differential Equation Approach

Gene regulation plays a fundamental role in biological activities. The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. We proposed a comprehensive statistical procedure for ODE model to identify the dynamic GRN. In this article, we applied this model to different segments of time course gene expression data from a simulation experiment and a yeast cell cycle study. We found that the two cell cycle and one cell cycle data provided consistent results, but half cell cycle data produced biased estimation. Therefore, we may conclude that the proposed model can quantify both two cell cycle and one cell cycle gene expression dynamics, but not for half cycle dynamics. The findings suggest that the model can identify the dynamic GRN correctly if the time course gene expression data are sufficient enough to capture the overall dynamics of underlying biological mechanism.     

基因脉冲导入仪及其在动物肿瘤治疗上的应用

本文介绍了电穿孔技术的原理和天津理工大学自行研制的基因脉冲导入仪 LN-301的系统组成。使用该系统在昆明小白鼠进行肿瘤治疗实验。实验结果表明：基因脉冲导入仪有利于肿瘤的治疗。     

基于基因表达式编程的电力消费预测模型 ——武汉市的实证

 本文运用基因表达式编程（GEP）方法，基于武汉市1990年至2005年的相关统计数据，构建了电力消费预测模型。研究显示，GEP法能够较好地克服有限样本数据序列的缺陷，并具有精度高、计算过程较为简化等优势。研究结果表明，武汉市电力消费主要影响因素的影响程度依次排序为：产业结构、能源效率改进、社会消费总额、人口、地区生产总值指数和电力价格指数。基于上述结论我们提出了解决电力短缺应该以加强电力能源效率改进为主导的政策建议。