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High Dimensional Variable Selection In Longitudinal And Nonlinear Gene Environment Interaction Studies
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Book Synopsis High-dimensional Variable Selection in Longitudinal and Nonlinear Gene-environment Interaction Studies by : Fei Zhou
Download or read book High-dimensional Variable Selection in Longitudinal and Nonlinear Gene-environment Interaction Studies written by Fei Zhou and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Variable selection from both the frequentist and Bayesian frameworks has gained increasing popularity in the analysis of high-dimensional genomic data. Despite the success of existing studies, challenges still remain as tailored methods for sparse interaction structures are not available when the response variables are repeatedly measured and/or have heavy-tailed distributions. These challenges have motivated the development of novel variable selection methods proposed in the following projects. Meanwhile, powerful software packages from these projects are publically available to facilitate fast and reliable computation, as well as reproducible research. In the first project, we have developed a novel penalized variable selection method to identify important lipid-environment interactions in a longitudinal lipidomics study, where the environment factors refer to a group of dummy variables corresponding to a four-level treatment factor. An efficient Newton-Raphson based algorithm was proposed within the generalized estimating equation (GEE) framework. Simulation studies have demonstrated the superior performance of our method over alternatives, in terms of both identification accuracy and prediction performance. Analysis of the high-dimensional lipid datasets collected using mice from the skin cancer prevention study identified meaningful markers that provide fresh insight into the underlying mechanism of cancer preventive effects. In the second project, we have proposed a sparse group penalization method for the bi-level GxE interaction study under the repeatedly measured phenotype to accommodate more general environment factors. Within the quadratic inference function (QIF) framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual level. We conducted simulation studies to establish the advantage of the proposed regularization methods. In the case study, the environment factors include age, gender and treatment, which are either continuous or categorical. Our method leads to improved prediction and identification of main and interaction effects with important implications. In the third project, a sparse Bayesian quantile varying coefficient model has been developed for non-linear GxE studies. The proposed model can accommodate heavy-tailed errors and outliers from the disease phenotypes while pinpointing important non-linear interactions through Bayesian variable selection based on spike-and-slab priors. Fast computation has been facilitated by the efficient Gibbs sampler. Simulation studies and real data analysis with age as the univariate environment factor have been performed to show the superiority of the proposed method over multiple competing alternatives. The open source R packages with C++ implementations of all the methods under comparison have been provided along this dissertation. The R packages interep and springer, for the first two projects respectively, are available on CRAN. The R package for the last project on Bayesian regularized quantile varying coefficient model will be released soon to the public.
Book Synopsis High-dimensional Variable Selection for Genomics Data, from Both Frequentist and Bayesian Perspectives by : Jie Ren
Download or read book High-dimensional Variable Selection for Genomics Data, from Both Frequentist and Bayesian Perspectives written by Jie Ren and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Variable selection is one of the most popular tools for analyzing high-dimensional genomic data. It has been developed to accommodate complex data structures and lead to structured sparse identification of important genomics features. We focus on the network and interaction structure that commonly exist in genomic data, and develop novel variable selection methods from both frequentist and Bayesian perspectives. Network-based regularization has achieved success in variable selections for high-dimensional cancer genomic data, due to its ability to incorporate the correlations among genomic features. However, as survival time data usually follow skewed distributions, and are contaminated by outliers, network-constrained regularization that does not take the robustness into account leads to false identifications of network structure and biased estimation of patients' survival. In the first project, we develop a novel robust network-based variable selection method under the accelerated failure time (AFT) model. Extensive simulation studies show the advantage of the proposed method over the alternative methods. Promising findings are made in two case studies of lung cancer datasets with high dimensional gene expression measurements. Gene-environment (G×E) interactions are important for the elucidation of disease etiology beyond the main genetic and environmental effects. In the second project, a novel and powerful semi-parametric Bayesian variable selection model has been proposed to investigate linear and nonlinear G×E interactions simultaneously. It can further conduct structural identification by distinguishing nonlinear interactions from main-effects-only case within the Bayesian framework. The proposed method conducts Bayesian variable selection more efficiently and accurately than alternatives. Simulation shows that the proposed model outperforms competing alternatives in terms of both identification and prediction. In the case study, the proposed Bayesian method leads to the identification of effects with important implications in a high-throughput profiling study with high-dimensional SNP data. In the last project, a robust Bayesian variable selection method has been developed for G×E interaction studies. The proposed robust Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. Spike and slab priors are incorporated on both individual and group levels to identify the sparse main and interaction effects. Extensive simulation studies and analysis of both the diabetes data with SNP measurements from the Nurses' Health Study and TCGA melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives. To facilitate reproducible research and fast computation, we have developed open source R packages for each project, which provide highly efficient C++ implementation for all the proposed and alternative approaches. The R packages regnet and spinBayes, associated with the first and second project correspondingly, are available on CRAN. For the third project, the R package robin is available from GitHub and will be submitted to CRAN soon.
Book Synopsis Penalized Variable Selection for Gene-environment Interactions by : Yinhao Du
Download or read book Penalized Variable Selection for Gene-environment Interactions written by Yinhao Du and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene-environment (GxE) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting GxE interactions. Despite the success, variable selection is limited in the following aspects. First, multidimensional measurements have not been taken into fully account in interaction studies. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. Second, in the big data context, no variable selection method has been developed so far to conduct tailored interaction analysis. Third, the solution to case control association GxE studies with high dimensional genomics variants in the big data context has not been made available so far. In this dissertation, we tackle these challenges rising from GxE interaction studies in the modern era through the following projects. In the first project, we have developed a novel variable selection method to integrate multi-omics measurements in GxE interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction and link the disease outcomes to multiple effects in the integrative GxE studies via accommodating a sparse bi-level structure. Simulation studies show the integrative model leads to better identification of GxE interactions and regulators than that of the alternative methods. In two GxE lung cancer studies with high dimensional multi-omics data, the integrative model leads to improved prediction and findings with important biological implications. In the second project, we propose to conduct interaction studies in the big data context by adopting the divide-and-conquer strategy. In particular, the sparse group variable selection for important GxE effects has been developed within the framework of alternating direction method of multiplier (ADMM). To accommodate the large-scale data in terms of either samples or features, we have developed two novel parallel ADMM based variable selection methods across samples and features, respectively. The corresponding parallel algorithms can be efficiently implemented in distributed computing platforms. Simulation studies demonstrate that the parallel ADMM based penalization methods significantly improve the computational speed for analyzing large scale data from GxE interaction studies with satisfactory identification and prediction performance. In the third project, we extend the proposed parallel ADMM based variable selection for GxE interactions in the case-control association study of type 2 diabetes. Within the parallel computation framework, we have developed a penalized logistic regression model accommodating the bi-level selection tailored for the case control GxE interaction study. The advantage of the proposed parallel penalization method has been fully illustrated in the distributed learning scenario. Simulation studies show the proposed method dramatically reduces the computational time while maintaining a competitive performance compared to the non-parallel counterparts. In the case study of type 2 diabetes with environmental factors and high dimensional SNP measurements, the proposed parallel penalization method leads to the identification of biologically important interaction effects.
Book Synopsis Insights in Statistical Genetics and Methodology: 2022 by : Simon Charles Heath
Download or read book Insights in Statistical Genetics and Methodology: 2022 written by Simon Charles Heath and published by Frontiers Media SA. This book was released on 2023-10-24 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Research Topic is part of the Insights in Frontiers in Genetics series.
Book Synopsis Variable Selection for Data Aggregated from Different Sources with Group of Variable Structure by : Camilo Broc
Download or read book Variable Selection for Data Aggregated from Different Sources with Group of Variable Structure written by Camilo Broc and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, the amount of available genetic data on populations has growndrastically. From one side, a refinement of chemical technologies have made possible theextraction of the human genome of individuals at an accessible cost. From the other side,consortia of institutions and laboratories around the world have permitted the collectionof data on a variety of individuals and population. This amount of data raised hope onour ability to understand the deepest mechanisms involved in the functioning of our cells.Notably, genetic epidemiology is a field that studies the relation between the geneticfeatures and the onset of a disease. Specific statistical methods have been necessary forthose analyses, especially due to the dimensions of available data: in genetics, informationis contained in a high number of variables compared to the number of observations.In this dissertation, two contributions are presented. The first project called PIGE (Pathway-Interaction Gene Environment) deals with gene-environment interaction assessments.The second one aims at developing variable selection methods for data which has groupstructures in both the variables and the observations.The document is divided into six chapters. The first chapter sets the background of this work,where both biological and mathematical notations and concepts are presented and gives ahistory of the motivation behind genetics and genetic epidemiology. The second chapterpresent an overview of the statistical methods currently in use for genetic epidemiology.The third chapter deals with the identification of gene-environment interactions. It includesa presentation of existing approaches for this problem and a contribution of the thesis. Thefourth chapter brings off the problem of meta-analysis. A definition of the problem and anoverview of the existing approaches are presented. Then, a new approach is introduced.The fifth chapter explains the pleiotropy studies and how the method presented in theprevious chapter is suited for this kind of analysis. The last chapter compiles conclusionsand research lines for the future.
Book Synopsis Dimension Reduction and Variable Selection by : Hossein Moradi Rekabdarkolaee
Download or read book Dimension Reduction and Variable Selection written by Hossein Moradi Rekabdarkolaee and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: High-dimensional data are becoming increasingly available as data collection technology advances. Over the last decade, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics, signal processing, and environmental studies. Statistical techniques such as dimension reduction and variable selection play important roles in high dimensional data analysis. Sufficient dimension reduction provides a way to find the reduced space of the original space without a parametric model. This method has been widely applied in many scientific fields such as genetics, brain imaging analysis, econometrics, environmental sciences, etc. in recent years. In this dissertation, we worked on three projects. The first one combines local modal regression and Minimum Average Variance Estimation (MAVE) to introduce a robust dimension reduction approach. In addition to being robust to outliers or heavy-tailed distribution, our proposed method has the same convergence rate as the original MAVE. Furthermore, we combine local modal base MAVE with a $L_1$ penalty to select informative covariates in a regression setting. This new approach can exhaustively estimate directions in the regression mean function and select informative covariates simultaneously, while being robust to the existence of possible outliers in the dependent variable. The second project develops sparse adaptive MAVE (saMAVE). SaMAVE has advantages over adaptive LASSO because it extends adaptive LASSO to multi-dimensional and nonlinear settings, without any model assumption, and has advantages over sparse inverse dimension reduction methods in that it does not require any particular probability distribution on \textbf{X}. In addition, saMAVE can exhaustively estimate the dimensions in the conditional mean function. The third project extends the envelope method to multivariate spatial data. The envelope technique is a new version of the classical multivariate linear model. The estimator from envelope asymptotically has less variation compare to the Maximum Likelihood Estimator (MLE). The current envelope methodology is for independent observations. While the assumption of independence is convenient, this does not address the additional complication associated with a spatial correlation. This work extends the idea of the envelope method to cases where independence is an unreasonable assumption, specifically multivariate data from spatially correlated process. This novel approach provides estimates for the parameters of interest with smaller variance compared to maximum likelihood estimator while still being able to capture the spatial structure in the data.
Book Synopsis Regularized Methods for High-dimensional and Bi-level Variable Selection by : Patrick John Breheny
Download or read book Regularized Methods for High-dimensional and Bi-level Variable Selection written by Patrick John Breheny and published by . This book was released on 2009 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finally, the methods from all three sections are studied in a number of simulations and applied to real data from gene expression and genetic association studies.
Book Synopsis Variable Selection for High-dimensional Data with Error Control by : Han Fu (Ph. D. in biostatistics)
Download or read book Variable Selection for High-dimensional Data with Error Control written by Han Fu (Ph. D. in biostatistics) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many high-throughput genomic applications involve a large set of covariates and it is crucial to discover which variables are truly associated with the response. It is often desirable for researchers to select variables that are indeed true and reproducible in followup studies. Effectively controlling the false discovery rate (FDR) increases the reproducibility of the discoveries and has been a major challenge in variable selection research, especially for high-dimensional data. Existing error control approaches include augmentation approaches which utilize artificial variables as benchmarks for decision making, such as model-X knockoffs. We introduce another augmentation-based selection framework extended from a Bayesian screening approach called reference distribution variable selection. Ordinal responses, which were not previously considered in this area, were used to compare different variable selection approaches. We constructed various importance measures that fit into the selection frameworks, using either L1 penalized regression or machine learning techniques, and compared these measures in terms of the FDR and power using simulated data. Moreover, we applied these selection methods to high-throughput methylation data for identifying features associated with the progression from normal liver tissue to hepatocellular carcinoma to further compare and contrast their performances. Having established the effectiveness of FDR control for model-X knockoffs, we turned our attention to another important data type - survival data with long-term survivors. Medical breakthroughs in recent years have led to cures for many diseases, resulting in increased observations of long-term survivors. The mixture cure model (MCM) is a type of survival model that is often used when a cured fraction exists. Unfortunately, currently few variable selection methods exist for MCMs when there are more predictors than samples. To fill the gap, we developed penalized MCMs for high-dimensional datasets which allow for identification of prognostic factors associated with both cure status and/or survival. Both parametric models and semi-parametric proportional hazards models were considered for modeling the survival component. For penalized parametric MCMs, we demonstrated how the estimation proceeded using two different iterative algorithms, the generalized monotone incremental forward stagewise (GMIFS) and Expectation-Maximization (E-M). For semi-parametric MCMs where multiple types of penalty functions were considered, the coordinate descent algorithm was combined with E-M for optimization. The model-X knockoffs method was combined with these algorithms to allow for FDR control in variable selection. Through extensive simulation studies, our penalized MCMs have been shown to outperform alternative methods on multiple metrics and achieve high statistical power with FDR being controlled. In two acute myeloid leukemia (AML) applications with gene expression data, our proposed approaches identified important genes associated with potential cure or time-to-relapse, which may help inform treatment decisions for AML patients.
Book Synopsis High Dimensional Statistical Methods for Gene-environment Interactions by : Cen Wu
Download or read book High Dimensional Statistical Methods for Gene-environment Interactions written by Cen Wu and published by . This book was released on 2013 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Variable Selection in High Dimensional Data Analysis with Applications by :
Download or read book Variable Selection in High Dimensional Data Analysis with Applications written by and published by . This book was released on 2015 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis High-dimensional Variable Selection for GLMs and Survival Models by : Hassan Pazira
Download or read book High-dimensional Variable Selection for GLMs and Survival Models written by Hassan Pazira and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Topics on Variable Selection in High-Dimensional Data by : Jia Wang
Download or read book Topics on Variable Selection in High-Dimensional Data written by Jia Wang and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Variable selection has been extensively studied in the last few decades as it provides a principled solution to high dimensionality arising in a broad spectrum of real applications, such as bioinformatics, health studies, social science and econometrics. This dissertation is concerned with variable selection for ultrahigh-dimensional data when the dimension is allowed to grow with the sample size or the network size at an exponential rate. We propose new Bayesian approaches to selecting variables under several model frameworks, including (1) partially linear models (2) static social network models with degree heterogeneity and (3) time-varying network models. Firstly for partially linear models, we develop a procedure which employs the difference-based method to reduce the impact from the estimation of the nonparametric component, and incorporates Bayesian subset modeling with diffusing prior (BSM-DP) to shrink the corresponding estimator in the linear component. Secondly, a class of network models where the connection probability depends on ultrahigh-dimensional nodal covariates (homophily) and node-specific popularity (degree heterogeneity) is considered. We propose a Bayesian method to select nodal features in both dense and sparse networks under a relaxed assumption on popularity parameters. To alleviate the computational burden for large sparse networks, we particularly develop another working model in which parameters are updated based on a dense sub-graph at each step. Lastly, we extend the static model to time-varying cases, where the connection probability at time t is modeled based on observed nodal attributes at time t and node-specific continuous-time baseline functions evaluated at time t. Those Bayesian proposals are shown to be analogous to a mixture of L0 and L2 penalized methods and work well in the setting of highly correlated predictors. Corresponding model selection consistency is studied for all aforementioned models, in the sense that the probability of the true model being selected converges to one asymptotically. The finite sample performance of the proposed models is further examined by simulation studies and analyses on social-media and financial datasets.
Book Synopsis Statistical Methods for High Dimensional Variable Selection by : Kaiqiao Li
Download or read book Statistical Methods for High Dimensional Variable Selection written by Kaiqiao Li and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Two Tales of Variable Selection for High Dimensional Data by : Cong Liu
Download or read book Two Tales of Variable Selection for High Dimensional Data written by Cong Liu and published by . This book was released on 2012 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: We also conduct similar types of studies for comparison of two corresponding screening and selection procedures of LASSO and correlation screening in classification setting, i.e., $L_{1}$ penalized logistic regression and two-sample t-test. Initial results of exploratory analysis are presented to provide some insights on the preferred scenarios of the two methods respectively. Discussions are made on possible extensions, future works and difference between regression and classification setting.
Book Synopsis Variable Selection in Varying Multi-Index Coefficient Models with Applications to Gene-Environmental Interactions by : Shunjie Guan
Download or read book Variable Selection in Varying Multi-Index Coefficient Models with Applications to Gene-Environmental Interactions written by Shunjie Guan and published by . This book was released on 2017 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Variable Selection for High-dimensional Spatial Linear Models by : 曾奕齊
Download or read book Variable Selection for High-dimensional Spatial Linear Models written by 曾奕齊 and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Epistasis written by Ka-Chun Wong and published by . This book was released on 2021 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt:
