Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering

A Bayesian model-based clustering approach is proposed for identifying differentially expressed genes in meta-analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from non-differentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration-driven discovery rate (IDR; Choi et al., 2003) can be easily computed. The model-based approach is also compared with commonly used permutation methods, and it is shown that the model-based approach is superior to the permutation methods when there are excessive under-expressed genes compared to over-expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets.     

On analyzing circadian rhythms data using nonlinear mixed models with harmonic terms

Wang, Ke, and Brown (2003, Biometrics59, 804-812) developed a smoothing-based approach for modeling circadian rhythms with random effects. Their approach is flexible in that fixed and random covariates can affect both the amplitude and phase shift of a nonparametrically smoothed periodic function. In motivating their approach, Wang et al. stated that a simple sinusoidal function is too restrictive. In addition, they stated that "although adding harmonics can improve the fit, it is difficult to decide how many harmonics to include in the model, and the results are difficult to interpret." We disagree with the notion that harmonic models cannot be a useful tool in modeling longitudinal circadian rhythm data. In this note, we show how nonlinear mixed models with harmonic terms allow for a simple and flexible alternative to Wang et al.'s approach. We show how to choose the number of harmonics using penalized likelihood to flexibly model circadian rhythms and to estimate the effect of covariates on the rhythms. We fit harmonic models to the cortisol circadian rhythm data presented by Wang et al. to illustrate our approach. Furthermore, we evaluate the properties of our procedure with a small simulation study. The proposed parametric approach provides an alternative to Wang et al.'s semiparametric approach and has the added advantage of being easy to implement in most statistical software packages.     

Random-effects ordination: describing and predicting multivariate correlations and co-occurrences

Ecology is inherently multivariate, but high-dimensional data are difficult to understand. Dimension reduction with ordination analysis helps with both data exploration and clarification of the meaning of inferences (e.g., randomization tests, variation partitioning) about a statistical population. Most such inferences are asymmetric, in that variables are classified as either response or explanatory (e.g., factors, predictors). But this asymmetric approach has limitations (e.g., abiotic variables may not entirely explain correlations between interacting species). We study symmetric population-level inferences by modeling correlations and co-occurrences, using these models for out-of-sample prediction. Such modeling requires a novel treatment of ordination axes as random effects, because fixed effects only allow within-sample predictions. We advocate an iterative methodology for random-effects ordination: (1) fit a set of candidate models differing in complexity (e.g., number of axes); (2) use information criteria to choose among models; (3) compare model predictions with data; (4) explore dimension-reduced graphs (e.g., biplots); (5) repeat 1–4 if model performance is poor. We describe and illustrate random-effects ordination models (with software) for two types of data: multivariate-normal (e.g., log morphometric data) and presence–absence community data. A large simulation experiment with multivariate-normal data demonstrates good performance of (1) a small-sample-corrected information criterion and (2) factor analysis relative to principal component analysis. Predictive comparisons of multiple alternative models is a powerful form of scientific reasoning: we have shown that unconstrained ordination can be based on such reasoning.     

DIM SUM: demography and individual migration simulated using a Markov chain

An increasing number of studies seek to infer demographic history, often jointly with genetic relationships. Despite numerous analytical methods for such data, few simulations have investigated the methods' power and robustness, especially when underlying assumptions have been violated. DIM SUM (Demography and Individual Migration Simulated Using a Markov chain) is a stand-alone Java program for the simulation of population demography and individual migration while recording ancestor-descendant relationships. It does not employ coalescent assumptions or discrete population boundaries. It is extremely flexible, allowing the user to specify border positions, reactions of organisms to borders, local and global carrying capacities, individual dispersal kernels, rates of reproduction and strategies for sampling individuals. Spatial variables may be specified using image files (e.g., as exported from gis software) and may vary through time. In combination with software for genetic marker simulation, DIM SUM will be useful for testing phylogeographic (e.g., nested clade phylogeographic analysis, coalescent-based tests and continuous-landscape frameworks) and landscape-genetic methods, specifically regarding violations of coalescent assumptions. It can also be used to explore the qualitative features of proposed demographic scenarios (e.g. regarding biological invasions) and as a pedagogical tool. DIM SUM (with user's manual) can be downloaded from http://code.google.com/p/bio-dimsum.     

Limits of Data Analysis in Scientific Inference: Reply to Sleep et al.

ABSTRACT Statistical inference is an important element of science, but these inferences are constrained within the framework established by the objectives and design of a study. The choice of approach to data analysis, while important, has far less consequence on scientific inference than claimed by Sleep et al. (2007). Their principal assertion—that when model selection is used as the approach to data analysis, all studies provide a reliable foundation for distinguishing among mechanistic explanatory hypotheses—is incorrect and encourages faulty inferences. Sleep et al. (2007) overlook the critical distinction between inferences that result from studies designed a priori to discriminate among a set of candidate explanations versus inferences that result from exploring data post hoc from studies designed originally to meet pattern-based objectives. No approach to data analysis, including model selection, has the power to overcome fundamental limitations on inferences imposed by study design. The comments by Sleep et al. (2007) reinforce the need for scientists to understand clearly the inferential basis for their scientific claims, including the roles and limitations of data analysis.     

msBayes: Pipeline for testing comparative phylogeographic histories using hierarchical approximate Bayesian computation

Background  

Although testing for simultaneous divergence (vicariance) across different population-pairs that span the same barrier to gene flow is of central importance to evolutionary biology, researchers often equate the gene tree and population/species tree thereby ignoring stochastic coalescent variance in their conclusions of temporal incongruence. In contrast to other available phylogeographic software packages, msBayes is the only one that analyses data from multiple species/population pairs under a hierarchical model.     

Developing EPIC markers for chalcidoid Hymenoptera from EST and genomic data

Increasing numbers of phylogeographic studies make comparative inferences about the histories of co-distributed species. Although the aims of such studies are best achieved by jointly analysing sequences from multiple loci in a model-based framework, such data currently exist for few nonmodel systems. We used existing genomic data and expressed sequence tags (ESTs) for Hymenoptera and other insects to design intron-crossing primers for 40 loci, mainly ribosomal proteins, for chalcidoid parasitoids. Amplification success was scored on a range of taxa associated with two natural communities; oak galls and figs. Taxa were chosen at increasing distance from Nasonia, which was used for primer design, (i) within Pteromalids, (ii) within Chalcidoidea (Eupelmidae, Eulophidae, Eurytomidae, Ormyridae, Torymidae) and (iii) for a selection of distantly related gall and fig wasps (Cynipidae, Agaonidae). To assess the utility of these loci for phylogeographic and population genetic studies, we compared genetic diversity between Western Palaearctic refugia for two species. Our results show that it is feasible to design a large number of exon-primed-intron-crossing (EPIC) loci that may be informative about phylogeographic history within species but amplify across a large taxonomic range.     

Statistical phylogeography

While studies of phylogeography and speciation in the past have largely focused on the documentation or detection of significant patterns of population genetic structure, the emerging field of statistical phylogeography aims to infer the history and processes underlying that structure, and to provide objective, rather than ad hoc explanations. Methods for parameter estimation are now commonly used to make inferences about demographic past. Although these approaches are well developed statistically, they typically pay little attention to geographical history. In contrast, methods that seek to reconstruct phylogeographic history are able to consider many alternative geographical scenarios, but are primarily nonstatistical, making inferences about particular biological processes without explicit reference to stochastically derived expectations. We advocate the merging of these two traditions so that statistical phylogeographic methods can provide an accurate representation of the past, consider a diverse array of processes, and yet yield a statistical estimate of that history. We discuss various conceptual issues associated with statistical phylogeographic inferences, considering especially the stochasticity of population genetic processes and assessing the confidence of phylogeographic conclusions. To this end, we present some empirical examples that utilize a statistical phylogeographic approach, and then by contrasting results from a coalescent-based approach to those from Templeton's nested cladistic analysis (NCA), we illustrate the importance of assessing error. Because NCA does not assess error in its inferences about historical processes or contemporary gene flow, we performed a small-scale study using simulated data to examine how our conclusions might be affected by such unconsidered errors. NCA did not identify the processes used to simulate the data, confusing among deterministic processes and the stochastic sorting of gene lineages. There is as yet insufficient justification of NCA's ability to accurately infer or distinguish among alternative processes. We close with a discussion of some unresolved problems of current statistical phylogeographic methods to propose areas in need of future development.     

Early human dispersals into the Iberian Peninsula: a comment on Martínez et al.(2010) and Garcia et al. (2011)

Garcia et al. (2011) recently discussed early human dispersals into the Iberian Peninsula, describing several putative lithic artifacts (Martínez et al., 2010) recovered from layer 7 of the Vallpara díssection (Madurell-Malapeira et al., 2010) in Terrassa (Vallès-Penedès Basin, Catalonia, Spain). According to the authors' opinion, such evidence (1) fills a gap in the chronology of early human occupation in Iberia, (2) indicates that these populations had primary and early access to carcasses, and (3) confirms that early human populations were equipped with advanced cultural traits enabling them to survive in unfavourable climatic conditions. We argue below that the record of human activity at Vallparadís (Martínez et al., 2010;Garcia et al., 2011) is doubtful and even that if confirmed, a chronological gap would remain (contra Garcia et al., 2011). Additional remarks on assertions by these authors on the Vallparadís geology, taphonomy and paleonvironment are also provided.     

华南长江和西江流域鲤科宽鳍鱲和马口鱼不同线粒体DNA谱系之间的体形分化

本文研究了两种鲤科鱼类,即分布于华南长江与西江的宽鳍鱲和马口鱼的不同线粒体DNA谱系之间的体形差异。近期基于线粒体DNA的这两个物种的系统发育分析认为这两个类元内包含多个谱系(宽鳍鱲A-D和马口鱼1-5)。本文采用几何形态度量学的方法研究了这些不同线粒体DNA谱系间是否存在体形方面的差异。在宽鳍鱲的4个不同的线粒体DNA谱系中,有3个被纳入本研究内容。结果显示:在宽鳍鱲的3个线粒体DNA谱系中,2个具有体形的差异。在马口鱼的5个线粒体DNA谱系中,3个被纳入本文的研究。结果显示所有3个谱系均存在体形差异。因此,线粒体DNA的谱系差异很大程度上表现在谱系间的体形差异方面,表明线粒体DNA谱系可能对应于不同的物种。     

Cumulative cultural evolution and demography

The idea that demographic change may spur or slow down technological change has become widely accepted among evolutionary archaeologists and anthropologists. Two models have been particularly influential in promoting this idea: a mathematical model by Joseph Henrich, developed to explain the Tasmanian loss of culture during the Holocene; and an agent-based adaptation thereof, devised by Powell et al. to explain the emergence of modern behaviour in the Late Pleistocene. However, the models in question make rather strong assumptions about the distribution of skills among social learners and about the selectivity of social learning strategies. Here I examine the behaviour of these models under more conservative and, on empirical and theoretical grounds, equally reasonable assumptions. I show that, some qualifications notwithstanding, Henrich's model largely withstands my robustness tests. The model of Powell et al., in contrast, does not-a finding that warrants a fair amount of skepticism towards Powell et al.'s explanation of the Upper Paleolithic transition. More generally, my evaluation of the accounts of Henrich and of Powell et al. helpfully clarify which inferences their popular models do and not support.     

Brief communication: human cranial variation fits iterative founder effect model with African origin

Recent studies comparing craniometric and neutral genetic affinity matrices have concluded that, on average, human cranial variation fits a model of neutral expectation. While human craniometric and genetic data fit a model of isolation by geographic distance, it is not yet clear whether this is due to geographically mediated gene flow or human dispersal events. Recently, human genetic data have been shown to fit an iterative founder effect model of dispersal with an African origin, in line with the out-of-Africa replacement model for modern human origins, and Manica et al. (Nature 448 (2007) 346-349) have demonstrated that human craniometric data also fit this model. However, in contrast with the neutral model of cranial evolution suggested by previous studies, Manica et al. (2007) made the a priori assumption that cranial form has been subject to climatically driven natural selection and therefore correct for climate prior to conducting their analyses. Here we employ a modified theoretical and methodological approach to test whether human cranial variability fits the iterative founder effect model. In contrast with Manica et al. (2007) we employ size-adjusted craniometric variables, since climatic factors such as temperature have been shown to correlate with aspects of cranial size. Despite these differences, we obtain similar results to those of Manica et al. (2007), with up to 26% of global within-population craniometric variation being explained by geographic distance from sub-Saharan Africa. Comparative analyses using non-African origins do not yield significant results. The implications of these results are discussed in the light of the modern human origins debate.     

MetMAP: an integrated Matlab package for analysis and optimization of metabolic systems

In previous works we have presented and applied a method to predict the parameter profile that optimizes biochemical systems regarding either a single or a set of metabolic responses within physiological constraints [Vera et al., 2003a]. This optimization technique requires a previous model definition and a translation to S-system form and the use of widely available linear programming packages. However, in dealing with these issues the interested researcher has to confront additional difficulties because of a lack of connectivity among available software packages or routines specifically designed to perform different tasks. In addition to this difficulty is the unavailability of any automated package which is capable of performing such optimizations and the previous required analysis. This situation prompted us to develop an integrated software package able to deal with these tasks in a single program environment. In this paper we present a software package for the model definition, analysis and optimization of a biochemical system. It starts with a given model definition that is directly translated to its equivalent S-system form. Once the model quality assessment is performed (stability and sensitivity analysis) the program determines the parameter profile that yields the optimized response compatible with a predefined set of constraints. Moreover the package finds the set of solutions obtained when more than one system's responses are to be optimized (multiobjective optimization).     

Regression analysis of doubly censored failure time data using the additive hazards model

Doubly censored failure time data arise when the survival time of interest is the elapsed time between two related events and observations on occurrences of both events could be censored. Regression analysis of doubly censored data has recently attracted considerable attention and for this a few methods have been proposed (Kim et al., 1993, Biometrics 49, 13-22; Sun et al., 1999, Biometrics 55, 909-914; Pan, 2001, Biometrics 57, 1245-1250). However, all of the methods are based on the proportional hazards model and it is well known that the proportional hazards model may not fit failure time data well sometimes. This article investigates regression analysis of such data using the additive hazards model and an estimating equation approach is proposed for inference about regression parameters of interest. The proposed method can be easily implemented and the properties of the proposed estimates of regression parameters are established. The method is applied to a set of doubly censored data from an AIDS cohort study.     

前瞻研究中多因素人群归因危险度的Poisson回归模型估计

本文提出多因素前瞻研究中利用Poisson回归发病率预测模型和相对危险度估计调整和综合人群归因危险度的方法,与Bruzzi等和Deubner等提出的多因素人群归因危险度估计方法进行了比较,强调在前瞻资料的人群归因危险度的估计中利用poisson回归模型考虑失访病例和随访时间效应,并能直接估计相对危险度的优势.应用所建立的方法对启东县肝病人群14年前瞻观察资料进行肝癌危险因素的人群归因危险度的估计。     

Heterogeneity in genetic diversity among non-coding loci fails to fit neutral coalescent models of population history

Inferring aspects of the population histories of species using coalescent analyses of non-coding nuclear DNA has grown in popularity. These inferences, such as divergence, gene flow, and changes in population size, assume that genetic data reflect simple population histories and neutral evolutionary processes. However, violating model assumptions can result in a poor fit between empirical data and the models. We sampled 22 nuclear intron sequences from at least 19 different chromosomes (a genomic transect) to test for deviations from selective neutrality in the gadwall (Anas strepera), a Holarctic duck. Nucleotide diversity among these loci varied by nearly two orders of magnitude (from 0.0004 to 0.029), and this heterogeneity could not be explained by differences in substitution rates alone. Using two different coalescent methods to infer models of population history and then simulating neutral genetic diversity under these models, we found that the observed among-locus heterogeneity in nucleotide diversity was significantly higher than expected for these simple models. Defining more complex models of population history demonstrated that a pre-divergence bottleneck was also unlikely to explain this heterogeneity. However, both selection and interspecific hybridization could account for the heterogeneity observed among loci. Regardless of the cause of the deviation, our results illustrate that violating key assumptions of coalescent models can mislead inferences of population history.     

Model selection as a tool for phylogeographic inference: an example from the willow Salix melanopsis

Phylogeographic inference has typically relied on analyses of data from one or a few genes to provide estimates of demography and population histories. While much has been learned from these studies, all phylogeographic analysis is conditioned on the data, and thus, inferences derived from data that represent a small sample of the genome are unavoidably tenuous. Here, we demonstrate one approach for moving beyond classic phylogeographic research. We use sequence capture probes and Illumina sequencing to generate data from >400 loci in order to infer the phylogeographic history of Salix melanopsis, a riparian willow with a disjunct distribution in coastal and the inland Pacific Northwest. We evaluate a priori phylogeographic hypotheses using coalescent models for parameter estimation, and the results support earlier findings that identified post‐Pleistocene dispersal as the cause of the disjunction in S. melanopsis. We also conduct a series of model selection exercises using IMa2, Migrate‐n and ?a?i. The resulting ranking of models indicates that refugial dynamics were complex, with multiple regions in the inland regions serving as the source for postglacial colonization. Our results demonstrate that new sources of data and new approaches to data analysis can rejuvenate phylogeographic research by allowing for the identification of complex models that enable researchers to both identify and estimate the most relevant parameters for a given system.     

Testing the level of ant activity associated with quorum sensing: an empirical approach leading to the establishment and test of a null-model (response to the comment of Richardson et al.)

In a paper in this journal (Nouvellet et al., 2010), we presented results from experiments on the behaviour of the Pharaoh's ant, Monomorium pharaonis, along with a substantial statistical and theoretical analysis of the results. In a minor part of our paper, we compared our results with the related work of Richardson et al. (2010a). These authors have subsequently commented on our interpretation of their work (Richardson et al., 2011). In this Letter we respond to the comments of Richardson et al. (2011), and give detailed arguments why we stand by our original conclusions.     

Unveiling steady-state multiplicity in hybridoma cultures: the cybernetic approach

Mammalian cells grown in suspension produce waste metabolites such as lactate, alanine, and ammonia, which reduce the yield of cell mass and the desired product on the nutrients supplied. Previous studies (Cruz et al., 1999; Europa et al., 2000; Follstad et al., 1999) have shown that the cells can be made to alter their metabolism by starving them on their nutrients in continuous cultures at low dilution rates or starting the culture as a fed-batch. This leads to multiple steady states in continuous reactors, with some states being more favorable than others. Mathematical models that take into account the metabolic regulation that leads to these multiple steady states are invaluable tools for bioreactor control. In this article we present a cybernetic modeling strategy in which Metabolic Flux Analysis (MFA) is used to guide the cybernetic formulation. The hybridoma model presented as a result of this strategy considers the partially substitutable, partially complementary nature of glucose and glutamine. The choice of competitions within the network is guided by MFA and the model is successful in explaining the three multiple steady states observed. The cybernetic model though identified for the hybridoma experiments of Hu and others (Europa et al., 2000) seem generally applicable to mammalian systems as it captures the pathways that are common to mammalian cells grown in suspension. The model presented here could be used for start-up strategies for continuous reactors and model-based feedback control for maintaining high productivity of the reactor.