Baoxue Zhang

Comparative DNA methylome analysis of endometrial carcinoma reveals complex and distinct deregulation of cancer promoters and enhancers

BackgroundAberrant DNA methylation is a hallmark of many cancers. Classically there are two types of endometrial cancer, endometrioid adenocarcinoma (EAC), or Type I, and uterine papillary serous carcinoma (UPSC), or Type II. However, the whole genome DNA methylation changes in these two classical types of endometrial cancer is still unknown.ResultsHere we described complete genome-wide DNA methylome maps of EAC, UPSC, and normal endometrium by applying a combined strategy of methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylation-sensitive restriction enzyme digestion sequencing (MRE-seq). We discovered distinct genome-wide DNA methylation patterns in EAC and UPSC: 27,009 and 15,676 recurrent differentially methylated regions (DMRs) were identified respectively, compared with normal endometrium. Over 80% of DMRs were in intergenic and intronic regions. The majority of these DMRs were not interrogated on the commonly used Infinium 450K array platform. Large-scale demethylation of chromosome X was detected in UPSC, accompanied by decreased XIST expression. Importantly, we discovered that the majority of the DMRs harbored promoter or enhancer functions and are specifically associated with genes related to uterine development and disease. Among these, abnormal methylation of transposable elements (TEs) may provide a novel mechanism to deregulate normal endometrium-specific enhancers derived from specific TEs.ConclusionsDNA methylation changes are an important signature of endometrial cancer and regulate gene expression by affecting not only proximal promoters but also distal enhancers.

Model selection in binary and tobit quantile regression using the Gibbs sampler

A stochastic search variable selection approach is proposed for Bayesian model selection in binary and tobit quantile regression. A simple and efficient Gibbs sampling algorithm was developed for posterior inference using a location-scale mixture representation of the asymmetric Laplace distribution. The proposed approach is then illustrated via five simulated examples and two real data sets. Results show that the proposed method performs very well under a variety of scenarios, such as the presence of a moderately large number of covariates, collinearity and heterogeneity.

Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments

BackgroundTime-course microarray experiments produce vector gene expression profiles across a series of time points. Clustering genes based on these profiles is important in discovering functional related and co-regulated genes. Early developed clustering algorithms do not take advantage of the ordering in a time-course study, explicit use of which should allow more sensitive detection of genes that display a consistent pattern over time. Peddada et al. [1] proposed a clustering algorithm that can incorporate the temporal ordering using order-restricted statistical inference. This algorithm is, however, very time-consuming and hence inapplicable to most microarray experiments that contain a large number of genes. Its computational burden also imposes difficulty to assess the clustering reliability, which is a very important measure when clustering noisy microarray data.ResultsWe propose a computationally efficient information criterion-based clustering algorithm, called ORICC, that also takes account of the ordering in time-course microarray experiments by embedding the order-restricted inference into a model selection framework. Genes are assigned to the profile which they best match determined by a newly proposed information criterion for order-restricted inference. In addition, we also developed a bootstrap procedure to assess ORICCs clustering reliability for every gene. Simulation studies show that the ORICC method is robust, always gives better clustering accuracy than Peddadas method and saves hundreds of times computational time. Under some scenarios, its accuracy is also better than some other existing clustering methods for short time-course microarray data, such as STEM [2] and Wang et al. [3]. It is also computationally much faster than Wang et al. [3].ConclusionOur ORICC algorithm, which takes advantage of the temporal ordering in time-course microarray experiments, provides good clustering accuracy and is meanwhile much faster than Peddadas method. Moreover, the clustering reliability for each gene can also be assessed, which is unavailable in Peddadas method. In a real data example, the ORICC algorithm identifies new and interesting genes that previous analyses failed to reveal.

Functional k-means inverse regression

A new dimension reduction method is proposed for functional multivariate regression with a multivariate response and a functional predictor by extending the functional sliced inverse regression model. Naive application of existing dimension reduction techniques for univariate response will create too many hyper-rectangular slices. To avoid this curse of dimensionality, a new slicing method is proposed by clustering over the space of the multivariate response, which generates a much smaller set of slices of flexible shapes. The proposed method can be applied to any number of response variables and can be particularly useful for exploratory analysis. In addition, a new eigenvalue-based method for determining the dimensionality of the reduced space is developed. Real and simulation data examples are then presented to demonstrate the effectiveness of the proposed method.

Functional contour regression

In this paper, we propose functional contour regression (FCR) for dimension reduction in the functional regression context. FCR achieves dimension reduction using the empirical directions on the functional predictor in contours defined on the response variable. It is more efficient than the functional variants of the sliced inverse regression (SIR) method by exploiting inter-slice information. A modified BIC is used to determine the dimensionality of the effective dimension reduction space. We prove that FCR is consistent in estimating the functional regression parameters, and simulations show that the estimates given by our FCR method provide better prediction accuracy than other existing methods such as functional sliced inverse regression, functional inverse regression and wavelet SIR. The merit of FCR is further demonstrated by two real data examples.

Functional linear regression after spline transformation

Functional linear regression has been widely used to model the relationship between a scalar response and functional predictors. If the original data do not satisfy the linear assumption, an intuitive solution is to perform some transformation such that transformed data will be linearly related. The problem of finding such transformations has been rather neglected in the development of functional data analysis tools. In this paper, we consider transformation on the response variable in functional linear regression and propose a nonparametric transformation model in which we use spline functions to construct the transformation function. The functional regression coefficients are then estimated by an innovative procedure called mixed data canonical correlation analysis (MDCCA). MDCCA is analogous to the canonical correlation analysis between two multivariate samples, but is between a multivariate sample and a set of functional data. Here, we apply the MDCCA to the projection of the transformation function on the B-spline space and the functional predictors. We then show that our estimates agree with the regularized functional least squares estimate for the transformation model subject to a scale multiplication. The dimension of the space of spline transformations can be determined by a model selection principle. Typically, a very small number of B-spline knots is needed. Real and simulation data examples are further presented to demonstrate the value of this approach.