Digest: Hypothesis testing in biogeography using phylogenetic trees*

How can we test whether biogeographic dispersal rates coincide with geologic events? Hua and Bromham developed a model-based approach to generate a null distribution of colonization times under constant dispersal rates, overcoming a problem related to the geometry of phylogenetic trees.     

Hypothesis testing in animal social networks

Behavioural ecologists are increasingly using social network analysis to describe the social organisation of animal populations and to test hypotheses. However, the statistical analysis of network data presents a number of challenges. In particular the non-independent nature of the data violates the assumptions of many common statistical approaches. In our opinion there is currently confusion and uncertainty amongst behavioural ecologists concerning the potential pitfalls when hypotheses testing using social network data. Here we review what we consider to be key considerations associated with the analysis of animal social networks and provide a practical guide to the use of null models based on randomisation to control for structure and non-independence in the data.     

Hypothesis testing in semiparametric additive mixed models

We consider testing whether the nonparametric function in a semiparametric additive mixed model is a simple fixed degree polynomial, for example, a simple linear function. This test provides a goodness-of-fit test for checking parametric models against nonparametric models. It is based on the mixed-model representation of the smoothing spline estimator of the nonparametric function and the variance component score test by treating the inverse of the smoothing parameter as an extra variance component. We also consider testing the equivalence of two nonparametric functions in semiparametric additive mixed models for two groups, such as treatment and placebo groups. The proposed tests are applied to data from an epidemiological study and a clinical trial and their performance is evaluated through simulations.     

Hypothesis testing with the similarity index

Multilocus DNA fingerprinting methods have been used extensively to address genetic issues in wildlife populations. Hypotheses concerning population subdivision and differing levels of diversity can be addressed through the use of the similarity index (S), a band-sharing coefficient, and many researchers construct hypothesis tests with S based on the work of Lynch. It is shown in the present study, through mathematical analysis and through simulations, that estimates of the variance of a mean S based on Lynch's work are downwardly biased. An unbiased alternative is presented and mathematically justified. It is shown further, however, that even when the bias in Lynch's estimator is corrected, the estimator is highly imprecise compared with estimates based on an alternative approach such as 'parametric bootstrapping' of allele frequencies. Also discussed are permutation tests and their construction given the interdependence of Ss which share individuals. A simulation illustrates how some published misuses of these tests can lead to incorrect conclusions in hypothesis testing.     

Hypothesis testing for proportions with overdispersion

The properties of likelihood ratio tests and simpler t-tests are investigated by simulation under an assumed beta-binomial model for parameter values typically found in toxicological studies. It is found that likelihood ratio methods are at least as powerful as the simpler approaches and in certain situations can be significantly more powerful.     

Hypothesis testing in high-throughput screening for drug discovery

Following the success of small-molecule high-throughput screening (HTS) in drug discovery, other large-scale screening techniques are currently revolutionizing the biological sciences. Powerful new statistical tools have been developed to analyze the vast amounts of data in DNA chip studies, but have not yet found their way into compound screening. In HTS, characterization of single-point hit lists is often done only in retrospect after the results of confirmation experiments are available. However, for prioritization, for optimal use of resources, for quality control, and for comparison of screens it would be extremely valuable to predict the rates of false positives and false negatives directly from the primary screening results. Making full use of the available information about compounds and controls contained in HTS results and replicated pilot runs, the Z score and from it the p value can be estimated for each measurement. Based on this consideration, we have applied the concept of p-value distribution analysis (PVDA), which was originally developed for gene expression studies, to HTS data. PVDA allowed prediction of all relevant error rates as well as the rate of true inactives, and excellent agreement with confirmation experiments was found.     

假说检验和科学方法

本文的意图是让研究者审视研究方法,并在研究设计中充分使用假说检验,并在选择模式物种时充分理解其自然史.我们的总前提是,按照"强推论"(指假定拒绝某一假说而不是支持某一偏爱假说)的逻辑,科学能够进展得更快、更可观、更有确定性.我们强调并提供了符合逻辑的一系列步骤,即确定科学问题或确定具有未知生物学意义的问题;列出所有可靠的、能解释所观察现象的假说,每个假说列出其可检验的、可证明其无根据的预测;然后是符合预测检验的实验或研究设计.我们也强调,模式物种对于解决科学的理论问题以及得出推论是很重要的.本文所展示的不是新思想,只是提醒研究者要注意遵循的基本研究途径.     

Hypothesis testing approaches to the exon prediction problem

MOTIVATION: Many gene identification methods assign scores to gene elements prior to their assembly into predicted genes. The scoring system is often based on log-likelihood ratios. These methods usually perform well but it is difficult to interpret how significant a score is. RESULTS: We have developed several tests of significance for the scores: (1) a sum-of-scores test (SST), (2) an intersection-union test (IUT), based on a multiple hypothesis testing interpretation of an exon's score and (3) a meta-analytical approach (MA), which combines several P-values, corresponding to the exon's parts, to yield a global P-value. We performed simulation studies, which show that the MA has better sensitivity and specificity than other methods and is easier to interpret by non-expert users. This is an improvement over other methods and is especially relevant for users who would like to predict incomplete gene sequences.     

Hypothesis testing in comparative and experimental studies of function-valued traits

Many traits of evolutionary interest, when placed in their developmental, physiological, or environmental contexts, are function-valued. For instance, gene expression during development is typically a function of the age of an organism and physiological processes are often a function of environment. In comparative and experimental studies, a fundamental question is whether the function-valued trait of one group is different from another. To address this question, evolutionary biologists have several statistical methods available. These methods can be classified into one of two types: multivariate and functional. Multivariate methods, including univariate repeated-measures analysis of variance (ANOVA), treat each trait as a finite list of data. Functional methods, such as repeated-measures regression, view the data as a sample of points drawn from an underlying function. A key difference between multivariate and functional methods is that functional methods retain information about the ordering and spacing of a set of data values, information that is discarded by multivariate methods. In this study, we evaluated the importance of that discarded information in statistical analyses of function-valued traits. Our results indicate that functional methods tend to have substantially greater statistical power than multivariate approaches to detect differences in a function-valued trait between groups.     

Hypothesis testing for the genetic background of quantitative traits

The testing of Bayesian point null hypotheses on variance component models have resulted in a tough assignment for which no clear and generally accepted method exists. In this work we present what we believe is a succeeding approach to such a task. It is based on a simple reparameterization of the model in terms of the total variance and the proportion of the additive genetic variance with respect to it, as well as on the explicit inclusion on the prior probability of a discrete component at origin. The reparameterization was used to bypass an arbitrariness related to the impropriety of uninformative priors onto unbounded variables while the discrete component was necessary to overcome the zero probability assigned to sets of null measure by the usual continuous variable models. The method was tested against computer simulations with appealing results.     

Hypothesis testing for data from different family study designs

A test statistic that is valid for data collected according to a particular type of family study design is not necessarily valid when applied to data obtained from a different type of family study design. When this can occur, a different test that usually is valid is developed for each type of family study design. However, investigators might find that their data come from two (or more) different family study designs, each requiring a different test, yet they want an overall conclusion, essentially a valid hypothesis test that is as powerful as possible. When the underlying genetic model is unknown, it is not clear how to proceed, as several alternative approaches might appear feasible. By using as an example the development of a test of association for data concerning affected singletons and their parents and affected sib pairs and their parents, it is shown that it may not be possible to develop a universally optimal approach without knowledge of the underlying genetic model.     

Hovenkamp's ostracized vicariance analysis: testing new methods of historical biogeography

All methods currently employed in cladistic biogeography usually give contrasting results and are theoretically disputed. In two overlooked papers, Hovenkamp (1997, 2001 ) strongly criticized methods currently used by biogeographers and proposed two other methods. However, his criticisms have remained unanswered and his methods rarely applied. I used three different data sets to show the superiority of Hovenkamp's methods. Both methods proposed by Hovenkamp do not suffer from the unrealistic assumptions that underlie other methods commonly used in cladistic biogeography. The method proposed in 2001 is more powerful than the previous method published in 1997, because it does not use a priori assumptions about the areas involved. However, the method proposed in 1997 may be a valid alternative for large data sets.
© The Willi Hennig Society 2007.     

Hypothesis testing via integrated computer modeling and digital fluorescence microscopy

Computational modeling has the potential to add an entirely new approach to hypothesis testing in yeast cell biology. Here, we present a method for seamless integration of computational modeling with quantitative digital fluorescence microscopy. This integration is accomplished by developing computational models based on hypotheses for underlying cellular processes that may give rise to experimentally observed fluorescent protein localization patterns. Simulated fluorescence images are generated from the computational models of underlying cellular processes via a "model-convolution" process. These simulated images can then be directly compared to experimental fluorescence images in order to test the model. This method provides a framework for rigorous hypothesis testing in yeast cell biology via integrated mathematical modeling and digital fluorescence microscopy.     

Hypothesis formulation and testing in aquatic bioassays: a deterministic model approach

The paper examines the significance of toxicant kinetics information obtained from aquatic toxicity bioassays and bioconcentration tests. The data, bioconcentration kinetics and acute mortality versus exposure-duration information for juvenile American flagfish (Jordanella foridae) exposed to 1,4-dichlorobenzene, are interpreted in terms of a one-compartment, first-order kinetics model. The output of the model is used to formulate a testable hypothesis regarding the comparison of toxicant kinetics derived from both bioconcentration test exposures and toxicity bioassays. The model's estimates of the toxicant body burden attained at mortality are compared with theoretical and observed body burdens from literature sources. The use of a simple, deterministic residue-based, one-compartment, first-order kinetics model to evaluate existing data, as well as to formulate hypotheses to direct experimental designs, is examined.     

Hypothesis testing in ecology: psychological aspects and the importance of theory maturation

Proper hypothesis testing is the subject of much debate in ecology. According to studies in cognitive psychology, confirmation bias (a tendency to seek confirming evidence) and theory tenacity (persistent belief in a theory in spite of contrary evidence) pervasively influence actual problem solving and hypothesis testing, often interfering with effective testing of alternative hypotheses. On the other hand, these psychological factors play a positive role in the process of theory maturation by helping to protect and nurture a new idea until it is suitable for critical evaluation. As a theory matures it increases in empirical content and its predictions become more distinct. Efficient hypothesis testing is often not possible when theories are in an immature state, as is the case in much of ecology. Problem areas in ecology are examined in light of these considerations, including failure to publish negative results, misuses of mathematical models, confusion resulting from ambiguous terms (such as "diversity" and "niche"), and biases against new ideas.     

Hypothesis testing under mixture models: application to genetic linkage analysis

In this paper we propose a new class of statistics to test a simple hypothesis against a family of alternatives characterized by a mixture model. Unlike the likelihood ratio statistic, whose large sample distribution is still unknown in this situation, these new statistics have a simple asymptotic distribution to which to refer under the null hypothesis. Simulation results suggest that it has adequate power in detecting the alternatives. Its application to genetic linkage analysis in the presence of the genetic heterogeneity that motivated this work is emphasized.