Generalized Species Sampling Priors with Latent Beta reinforcements

Many popular Bayesian nonparametric priors can be characterized in terms of exchangeable species sampling sequences. However, in some applications, exchangeability may not be appropriate. We introduce a {novel and probabilistically coherent family of non-exchangeable species sampling sequences characterized by a tractable predictive probability function with weights driven by a sequence of independent Beta random variables. We compare their theoretical clustering properties with those of the Dirichlet Process and the two parameters Poisson-Dirichlet process. The proposed construction provides a complete characterization of the joint process, differently from existing work. We then propose the use of such process as prior distribution in a hierarchical Bayes modeling framework, and we describe a Markov Chain Monte Carlo sampler for posterior inference. We evaluate the performance of the prior and the robustness of the resulting inference in a simulation study, providing a comparison with popular Dirichlet Processes mixtures and Hidden Markov Models. Finally, we develop an application to the detection of chromosomal aberrations in breast cancer by leveraging array CGH data.Comment: For correspondence purposes, Edoardo M. Airoldi's email is [email protected]; Federico Bassetti's email is [email protected]; Michele Guindani's email is [email protected] ; Fabrizo Leisen's email is [email protected]. To appear in the Journal of the American Statistical Associatio

A hierarchical Bayesian model for inference of copy number variants and their association to gene expression

A number of statistical models have been successfully developed for the analysis of high-throughput data from a single source, but few methods are available for integrating data from different sources. Here we focus on integrating gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. We specify a measurement error model that relates the gene expression levels to latent copy number states which, in turn, are related to the observed surrogate CGH measurements via a hidden Markov model. We employ selection priors that exploit the dependencies across adjacent copy number states and investigate MCMC stochastic search techniques for posterior inference. Our approach results in a unified modeling framework for simultaneously inferring copy number variants (CNV) and identifying their significant associations with mRNA transcripts abundance. We show performance on simulated data and illustrate an application to data from a genomic study on human cancer cell lines.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS705 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

An application in bioinformatics : a comparison of affymetrix and compugen human genome microarrays

The human genome microarrays from Compugen® and Affymetrix® were compared in the context of the emerging field of computational biology. The two premier database servers for genomic sequence data, the National Center for Biotechnology Information and the European Bioinformatics Institute, were described in detail. The various databases and data mining tools available through these data servers were also discussed. Microarrays were examined from a historical perspective and their main current applications-expression analysis, mutation analysis, and comparative genomic hybridization-were discussed. The two main types of microarrays, cDNA spotted microarrays and high-density spotted microarrays were analyzed by exploring the human genome microarray from Compugen® and the HGU133 Set from Affymetrix® respectively. Array design issues, sequence collection and analysis, and probe selection processes for the two representative types of arrays were described. The respective chip design of the two types of microarrays was also analyzed. It was found that the human genome microarray from Compugen 0 contains probes that interrogate 1,119,840 bases corresponding to 18,664 genes, while the HG-U133 Set from Affymetrix® contains probes that interrogate only 825,000 bases corresponding to 33,000 genes. Based on this, the efficiency of the 25-mer probes of the HG-U133 Set from Affymetrix® compared to the 60-mer probes of the microarray from Compugen® was questioned

Bayesian meta-analysis models for heterogeneous genomics data

<p>The accumulation of high-throughput data from vast sources has drawn a lot attentions to develop methods for extracting meaningful information out of the massive data. More interesting questions arise from how to combine the disparate information, which goes beyond modeling sparsity and dimension reduction. This dissertation focuses on the innovations in the area of heterogeneous data integration.</p><p>Chapter 1 contextualizes this dissertation by introducing different aspects of meta-analysis and model frameworks for high-dimensional genomic data.</p><p>Chapter 2 introduces a novel technique, joint Bayesian sparse factor analysis model, to vertically integrate multi-dimensional genomic data from different platforms. </p><p>Chapter 3 extends the above model to a nonparametric Bayes formula. It directly infers number of factors from a model-based approach.</p><p>On the other hand, chapter 4 deals with horizontal integration of diverse gene expression data; the model infers pathway activities across various experimental conditions. </p><p>All the methods mentioned above are demonstrated in both simulation studies and real data applications in chapters 2-4.</p><p>Finally, chapter 5 summarizes the dissertation and discusses future directions.</p>Dissertatio

Ruler arrays detect genomic insertions and deletions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 137-142).A Ruler Array measures the distance between a set of microarray probes and a set of experimentally defined locations in a nucleic acid, offering new possibilities for locating and characterizing changes in the nucleic acid sequence. Despite the known relevance of genomic changes to pathogens, cancer, development, and evolution, many of these changes evade detection by existing high-throughput techniques. Since a microarray can interrogate thousands or millions of probes at once, Ruler Arrays can screen a small genome or part of a mammalian sized genome for insertions, deletions, and inversions in a single experiment.by Philip Alexander Rolfe.Ph.D

Development of New Computational Tools for Analyzing Hi-C Data and Predicting Three-Dimensional Genome Organization

Background: The development of Hi-C (and related methods) has allowed for unprecedented sequence-level investigations into the structure-function relationship of the genome. There has been extensive effort in developing new tools to analyze this data in order to better understand the relationship between 3D genomic structure and function. While useful, the existing tools are far from maturity and (in some cases) lack the generalizability that would be required for application in a diverse set of organisms. This is problematic since the research community has proposed many cross-species "hallmarks" of 3D genome organization without confirming their existence in a variety of organisms. Research Objective: Develop new, generalizable computational tools for Hi-C analysis and 3D genome prediction. Results: Three new computational tools were developed for Hi-C analysis or 3D genome prediction: GrapHi-C (visualization), GeneRHi-C (3D prediction) and StoHi-C (3D prediction). Each tool has the potential to be used for 3D genome analysis in both model and non-model organisms since the underlying algorithms do not rely on any organism-specific constraints. A brief description of each tool follows. GrapHi-C is a graph-based visualization of Hi-C data. Unlike existing visualization methods, GrapHi-C allows for a more intuitive structural visualization of the underlying data. GeneRHi-C and StoHi-C are tools that can be used to predict 3D genome organizations from Hi-C data (the 3D-genome reconstruction problem). GeneRHi-C uses a combination of mixed integer programming and network layout algorithms to generate 3D coordinates from a ploidy-dependent subset of the Hi-C data. Alternatively, StoHi-C uses t-stochastic neighbour embedding with the complete set of Hi-C data to generate 3D coordinates of the genome. Each tool was applied to multiple, independent existing Hi-C datasets from fission yeast to demonstrate their utility. This is the first time 3D genome prediction has been successfully applied to these datasets. Overall, the tools developed here more clearly recapitulated documented features of fission yeast genomic organization when compared to existing techniques. Future work will focus on extending and applying these tools to analyze Hi-C datasets from other organisms. Additional Information: This thesis contains a collection of papers pertaining to the development of new tools for analyzing Hi-C data and predicting 3D genome organization. Each paper's publication status (as of January 2020) has been provided at the beginning of the corresponding chapter. For published papers, reprint permission was obtained and is available in the appendix

Bayesian Model Based Approaches In The Analysis Of Chromatin Structure And Motif Discovery

Efficient detection of transcription factor (TF) binding sites is an important and unsolved problem in computational genomics. Recently, due to the poor predictive ability of motif finding algorithms, along with the recent proliferation of high-throughput genomic technologies, there has been a drive to utilize secondary information, such as the positioning of nucleosomes, for improving predictions. Nucleosomes prevent transcription factor binding at those sites by blocking the TF access to the DNA. We aimed to construct an accurate map of nucleosome-free regions (NFRs), based on data from high-throughput genomic tiling arrays in yeast. Direct use of Hidden Markov Models are not always applicable due to variable-sized gaps and missing data. So we have extended the hidden Markov model procedure to a continuous time version while efficiently incorporating DNA sequence features that are relevant to nucleosome formation. Simulation studies and an application to a yeast nucleosomal assay demonstrate the advantages of the new method. The established biological role of nucleosomes in relation to TF binding, led us to formulate a joint model in the fourth chapter. The algorithm was implemented on the FAIRE data set, and comparisons were made with existing motif search algorithms. The fifth chapter deals with HMM asymptotics. We obtained results on consistency asymptotic normality and contiguity of a hidden Markov model. These have helped our inference on the convergence properties of the posterior and the consistency of the Bayesian posterior estimates. This has led to the conclusion that the Bayesian inference of a HMM run on sufficiently large datasets (which is typical, in the case of genomic data) leads us very close to the underlying true parameters, as in the case of iid models. The result is fairly general in nature to provide the justification for HMM inference in a wide variety of datasets