Uncovering latent structure in valued graphs: A variational approach

As more and more network-structured data sets are available, the statistical analysis of valued graphs has become common place. Looking for a latent structure is one of the many strategies used to better understand the behavior of a network. Several methods already exist for the binary case. We present a model-based strategy to uncover groups of nodes in valued graphs. This framework can be used for a wide span of parametric random graphs models and allows to include covariates. Variational tools allow us to achieve approximate maximum likelihood estimation of the parameters of these models. We provide a simulation study showing that our estimation method performs well over a broad range of situations. We apply this method to analyze host--parasite interaction networks in forest ecosystems.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS361 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language.

Programs for Bayesian inference of phylogeny currently implement a unique and fixed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been implemented by the developers of those programs. We developed a new open-source software package, RevBayes, to address these problems. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic-graphical models can be specified interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. Rev is similar to the R language and the BUGS model-specification language, and should be easy to learn for most users. The strength of RevBayes is the simplicity with which one can design, specify, and implement new and complex models. Fortunately, this tremendous flexibility does not come at the cost of slower computation; as we demonstrate, RevBayes outperforms competing software for several standard analyses. Compared with other programs, RevBayes has fewer black-box elements. Users need to explicitly specify each part of the model and analysis. Although this explicitness may initially be unfamiliar, we are convinced that this transparency will improve understanding of phylogenetic models in our field. Moreover, it will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. RevBayes is freely available at http://www.RevBayes.com [Bayesian inference; Graphical models; MCMC; statistical phylogenetics.]

Phylogenetic Stochastic Mapping without Matrix Exponentiation

Phylogenetic stochastic mapping is a method for reconstructing the history of trait changes on a phylogenetic tree relating species/organisms carrying the trait. State-of-the-art methods assume that the trait evolves according to a continuous-time Markov chain (CTMC) and work well for small state spaces. The computations slow down considerably for larger state spaces (e.g. space of codons), because current methodology relies on exponentiating CTMC infinitesimal rate matrices -- an operation whose computational complexity grows as the size of the CTMC state space cubed. In this work, we introduce a new approach, based on a CTMC technique called uniformization, that does not use matrix exponentiation for phylogenetic stochastic mapping. Our method is based on a new Markov chain Monte Carlo (MCMC) algorithm that targets the distribution of trait histories conditional on the trait data observed at the tips of the tree. The computational complexity of our MCMC method grows as the size of the CTMC state space squared. Moreover, in contrast to competing matrix exponentiation methods, if the rate matrix is sparse, we can leverage this sparsity and increase the computational efficiency of our algorithm further. Using simulated data, we illustrate advantages of our MCMC algorithm and investigate how large the state space needs to be for our method to outperform matrix exponentiation approaches. We show that even on the moderately large state space of codons our MCMC method can be significantly faster than currently used matrix exponentiation methods.Comment: 33 pages, including appendice

Bayesian inference of ancestral dates on bacterial phylogenetic trees

The sequencing and comparative analysis of a collection of bacterial genomes from a single species or lineage of interest can lead to key insights into its evolution, ecology or epidemiology. The tool of choice for such a study is often to build a phylogenetic tree, and more specifically when possible a dated phylogeny, in which the dates of all common ancestors are estimated. Here, we propose a new Bayesian methodology to construct dated phylogenies which is specifically designed for bacterial genomics. Unlike previous Bayesian methods aimed at building dated phylogenies, we consider that the phylogenetic relationships between the genomes have been previously evaluated using a standard phylogenetic method, which makes our methodology much faster and scalable. This two-step approach also allows us to directly exploit existing phylogenetic methods that detect bacterial recombination, and therefore to account for the effect of recombination in the construction of a dated phylogeny. We analysed many simulated datasets in order to benchmark the performance of our approach in a wide range of situations. Furthermore, we present applications to three different real datasets from recent bacterial genomic studies. Our methodology is implemented in a R package called BactDating which is freely available for download at https://github.com/xavierdidelot/BactDating

Bayesian methods for source attribution using HIV deep sequence data

The advent of pathogen deep-sequencing technology provides new opportunities for infec- tious disease surveillance, especially for fast-evolving viruses like human immunodeficiency virus (HIV). In particular, multiple reads per host contain detailed information on viral within- host diversity. This information allows the reconstruction of partial directed transmission networks, where estimates of who is source and who is recipient are directly available from the phylogenetic ordering of the viruses of any two individuals. This is a new approach for phylodynamics, and the topic of my thesis. In this thesis, I present updates to the bioinformatics pipeline used by the Phylogenetics And Networks for Generalised Epidemics in Africa consortium for processing HIV deep sequence data and running the phyloscanner program. I then present a semi-parametric Bayesian Poisson model for inferring infectious disease transmission flows and the sources of infection at the population level. The framework is computationally scalable in high- dimensional flow spaces thanks to Hilbert Space Gaussian process approximations, allows for sampling bias adjustments, and estimation of gender- and age-specific transmission flows at a finer resolution than previously possible. In this sense, the methods that I developed enable us to overcome some problems which have been unable to be solved by conventional phylodynamic approaches. We apply the approach to densely sampled, population-based HIV deep-sequence data from Rakai, Uganda. I focus on characterising age-specific transmission dynamics, and examining the sources of HIV infections in adolescent and young women in particular.Open Acces

Detecting recombination and its mechanistic association with genomic features via statistical models

Recombination is a powerful weapon in the evolutionary arsenal of retroviruses such as HIV. It enables the production of chimeric variants or recombinants that may confer a selective advantage to the pathogen over the host immune response. Recombinants further accentuate differences in virulence, disease progression and drug resistance mutation patterns already observed in non-recombinant variants of HIV. This thesis describes the development of a rapid genotyper for HIV sequences employing supervised learning algorithms and its application to complex HIV recombinant data, the application of a hierarchical model for detection of recombination hotspots in the HIV-1 genome and the extension of this model enabling estimation of the association between recombination probabilities and covariates of interest. The rapid genotyper for HIV-1 explores a solution to the genotyping problem in the machine learning paradigm. Of the algorithms tested, the genotyper built using Bayesian additive regression trees (BART) was most successful in efficiently classifying complex recombinants that pose a challenge to other currently available genotyping methods. We also developed a novel method, bootSMOTE, for generating synthetic data in order to supplement insufficient training data. We found that supplementation with synthetic recombinants especially boosts identification of complex recombinants. We describe the genotyper software available for download as well as a web interface enabling rapid classiffication of HIV-1 sequences. Hotspots for recombination in the HIV-1 genome are modeled using spatially smoothed changepoint processes. This hierarchical model uses a phylogenetic recombination detection model of dual changepoint processes at the lower level. The upper level applies a Gaussian Markov random eld (GMRF) hyperprior to population-level recombination probabilities in order to efficiently combine the information from many individual recombination events as inferred at the lower level. Focusing on 544 unique recombinant sequences, we found a novel hotspot in the pol gene of HIV-1 while confirming the presence of a high recombination activity in the env gene. Valuable insights into the molecular mechanism of recombination may be gained by extending the GMRF model to include covariates of interest. We add a level to the hierarchical model and allow for the simultaneous inference of recombination probabilities as well their association with genomic covariates of interest. Using a set of 527 unique recombinants, we confirmed the presence of the pol hotspot. Interestingly, we found significant positive associations of spatial fluctuations in recombination probabilities with genomic regions prone to forming secondary structure as well as significant negative associations with regions that support tight RNA-DNA hybrid formation. Overall, our results support the theory that pause sites along the genome promote recombination