Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines

Background

Recent advances in genome technologies and the subsequent collection of genomic information at various molecular resolutions hold promise to accelerate the discovery of new therapeutic targets. A critical step in achieving these goals is to develop efficient clinical prediction models that integrate these diverse sources of high-throughput data. This step is challenging due to the presence of high-dimensionality and complex interactions in the data. For predicting relevant clinical outcomes, we propose a flexible statistical machine learning approach that acknowledges and models the interaction between platform-specific measurements through nonlinear kernel machines and borrows information within and between platforms through a hierarchical Bayesian framework. Our model has parameters with direct interpretations in terms of the effects of platforms and data interactions within and across platforms. The parameter estimation algorithm in our model uses a computationally efficient variational Bayes approach that scales well to large high-throughput datasets.

Results

We apply our methods of integrating gene/mRNA expression and microRNA profiles for predicting patient survival times to The Cancer Genome Atlas (TCGA) based glioblastoma multiforme (GBM) dataset. In terms of prediction accuracy, we show that our non-linear and interaction-based integrative methods perform better than linear alternatives and non-integrative methods that do not account for interactions between the platforms. We also find several prognostic mRNAs and microRNAs that are related to tumor invasion and are known to drive tumor metastasis and severe inflammatory response in GBM. In addition, our analysis reveals several interesting mRNA and microRNA interactions that have known implications in the etiology of GBM.

Conclusions

Our approach gains its flexibility and power by modeling the non-linear interaction structures between and within the platforms. Our framework is a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers. We have a freely available software at: http://odin.mdacc.tmc.edu/~vbaladan.

     

教育中的同伴效应研究述评：概念、模型与方法

教育中的同伴效应是指宿舍、班级、年级或学校内同伴的背景、行为及产出对学生产出或行为的影响。教育中的同伴效应起初使用的是同质性模型,即认为无论个体如何选择同伴,总效益是不变的,同伴效应是一种零和现象。进而发展到异质性模型,即认为同伴效应对不同个体的作用结果是不一样的,通过合理的分配能够提高总效益。研究方法则从以普通最小二乘法为主的统计关联研究,发展到借助于随机实验、自然实验以及准实验的因果推断研究。同伴效应的研究为正确地认识和评价相关教育政策,获得最优组织学校教学的方式,提高教学效率提供了依据。     

The effects of demographic stochasticity and parameter uncertainty on predicting the establishment of introduced species

Invasive species are a serious threat to biodiversity worldwide and predicting whether an introduced species will first establish and then become invasive can be useful to preserve ecosystem services. Establishment is influenced by multiple factors, such as the interactions between the introduced individuals and the resident community, and demographic and environmental stochasticity. Field observations are often incomplete or biased. This, together with an imperfect knowledge of the ecological traits of the introduced species, makes the prediction of establishment challenging. Methods that consider the combined effects of these factors on our ability to predict the establishment of an introduced species are currently lacking. We develop an inference framework to assess the combined effects of demographic stochasticity and parameter uncertainty on our ability to predict the probability of establishment following the introduction of a small number of individuals. We find that even moderate levels of demographic stochasticity influence both the probability of establishment, and, crucially, our ability to correctly predict that probability. We also find that estimation of the demographic parameters of an introduced species is fundamental to obtain precise estimates of the interaction parameters. For typical values of demographic stochasticity, the drop in our ability to predict an establishment can be 30% when having priors on the demographic parameters compared to having their accurate values. The results from our study illustrate how demographic stochasticity may bias the prediction of the probability of establishment. Our method can be applied to estimate probability of establishment of introduced species in field scenarios, where time series data and prior information on the demographic traits of the introduced species are available.     

Evidential statistics as a statistical modern synthesis to support 21st century science

During the 20th century, population ecology and science in general relied on two very different statistical paradigms to solve its inferential problems: error statistics (also referred to as classical statistics and frequentist statistics) and Bayesian statistics. A great deal of good science was done using these tools, but both schools suffer from technical and philosophical difficulties. At the turning of the 21st century (Royall in Statistical evidence: a likelihood paradigm. Chapman & Hall, London, 1997 ; Lele in The nature of scientific evidence: statistical, philosophical and empirical considerations. The University of Chicago Press, Chicago, pp 191–216, 2004a ), evidential statistics emerged as a seriously contending paradigm. Drawing on and refining elements from error statistics, likelihoodism, Bayesian statistics, information criteria, and robust methods, evidential statistics is a statistical modern synthesis that smoothly incorporates model identification, model uncertainty, model comparison, parameter estimation, parameter uncertainty, pre-data control of error, and post-data strength of evidence into a single coherent framework. We argue that evidential statistics is currently the most effective statistical paradigm to support 21st century science. Despite the power of the evidential paradigm, we think that there is no substitute for learning how to clarify scientific arguments with statistical arguments. In this paper we sketch and relate the conceptual bases of error statistics, Bayesian statistics and evidential statistics. We also discuss a number of misconceptions about the paradigms that have hindered practitioners, as well as some real problems with the error and Bayesian statistical paradigms solved by evidential statistics.     

Unravelling the invasion history of the Asian tiger mosquito in Europe

Multiple introductions are key features for the establishment and persistence of introduced species. However, little is known about the contribution of genetic admixture to the invasive potential of populations. To address this issue, we studied the recent invasion of the Asian tiger mosquito (Aedes albopictus) in Europe. Combining genome‐wide single nucleotide polymorphisms and historical knowledge using an approximate Bayesian computation framework, we reconstruct the colonization routes and establish the demographic dynamics of invasion. The colonization of Europe involved at least three independent introductions in Albania, North Italy and Central Italy that subsequently acted as dispersal centres throughout Europe. We show that the topology of human transportation networks shaped demographic histories with North Italy and Central Italy being the main dispersal centres in Europe. Introduction modalities conditioned the levels of genetic diversity in invading populations, and genetically diverse and admixed populations promoted more secondary introductions and have spread farther than single‐source invasions. This genomic study provides further crucial insights into a general understanding of the role of genetic diversity promoted by modern trade in driving biological invasions.     

The evolution of microendemism in a reef fish (Hypoplectrus maya)

Marine species tend to have extensive distributions, which are commonly attributed to the dispersal potential provided by planktonic larvae and the rarity of absolute barriers to dispersal in the ocean. Under this paradigm, the occurrence of marine microendemism without geographic isolation in species with planktonic larvae poses a dilemma. The recently described Maya hamlet (Hypoplectrus maya, Serranidae) is exactly such a case, being endemic to a 50‐km segment of the Mesoamerican Barrier Reef System (MBRS). We use whole‐genome analysis to infer the demographic history of the Maya hamlet and contrast it with the sympatric and pan‐Caribbean black (H. nigricans), barred (H. puella) and butter (H. unicolor) hamlets, as well as the allopatric but phenotypically similar blue hamlet (H. gemma). We show that H. maya is indeed a distinct evolutionary lineage, with genomic signatures of inbreeding and a unique demographic history of continuous decrease in effective population size since it diverged from congeners just ~3,000 generations ago. We suggest that this case of microendemism may be driven by the combination of a narrow ecological niche and restrictive oceanographic conditions in the southern MBRS, which is consistent with the occurrence of an unusually high number of marine microendemics in this region. The restricted distribution of the Maya hamlet, its decline in both census and effective population sizes, and the degradation of its habitat place it at risk of extinction. We conclude that the evolution of marine microendemism can be a fast and dynamic process, with extinction possibly occurring before speciation is complete.     

Population genomic evidence for plant glacial survival in Scandinavia

Quaternary glaciations have played a major role in shaping the genetic diversity and distribution of plant species. Strong palaeoecological and genetic evidence supports a postglacial recolonization of most plant species to northern Europe from southern, eastern and even western glacial refugia. Although highly controversial, the existence of small in situ glacial refugia in northern Europe has recently gained molecular support. We used genomic analyses to examine the phylogeography of a species that is critical in this debate. Carex scirpoidea Michx subsp. scirpoidea is a dioecious, amphi‐Atlantic arctic–alpine sedge that is widely distributed in North America, but absent from most of Eurasia, apart from three extremely disjunct populations in Norway, all well within the limits of the Weichselian ice sheet. Range‐wide population sampling and variation at 5,307 single nucleotide polymorphisms show that the three Norwegian populations comprise unique evolutionary lineages divergent from Greenland with high between‐population divergence. The Norwegian populations have low within‐population genetic diversity consistent with having experienced genetic bottlenecks in glacial refugia, and host private alleles that probably accumulated in long‐term isolated populations. Demographic analyses support a single, pre‐Weichselian colonization into Norway from East Greenland, and subsequent divergence of the three populations in separate refugia. Other refugial areas are identified in North‐east Greenland, Minnesota/Michigan, Colorado and Alaska. Admixed populations in British Columbia and West Greenland indicate postglacial contact. Taken together, evidence from this study strongly indicates in situ glacial survival in Scandinavia.     

Contrasting patterns of genetic structure and phylogeography in the marine agarophytes Gelidiophycus divaricatus and G. freshwateri (Gelidiales,Rhodophyta) from East Asia

The evolutionary and population demographic history of marine red algae in East Asia is poorly understood. Here, we reconstructed the phylogeographies of two upper intertidal species endemic to East Asia, Gelidiophycus divaricatus and G. freshwateri. Phylogenetic and phylogeographic inferences of 393 mitochondrial cox1, 128 plastid rbcL, and 342 nuclear ITS2 sequences were complemented with ecological niche models. Gelidiophycus divaricatus, a southern species adapted to warm water, is characterized by a high genetic diversity and a strong geographical population structure, characteristic of stable population sizes and sudden reduction to recent expansion. In contrast, G. freshwateri, a northern species adapted to cold temperate conditions, is genetically relatively homogeneous with a shallow population structure resulting from steady population growth and recent equilibrium. The overlap zone of the two species roughly matches summer and winter isotherms, indicating that surface seawater temperature is a key feature influencing species range. Unidirectional genetic introgression was detected at two sites on Jeju Island where G. divaricatus was rare while G. freshwateri was common, suggesting the occurrence of asymmetric natural hybrids, a rarely reported event for rhodophytes. Our results illustrate that Quaternary climate oscillations have left strong imprints on the current day genetic structure and highlight the importance of seawater temperature and sea level change in driving speciation in upper intertidal seaweed species.