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This paper is motivated by the recent interest in the analysis of high-dimensional microbiome data. A key feature of these data is the presence of "structural zeros" which are microbes missing from an observation vector due to an underlying biological process and not due to error in measurement. Typical notions of missingness are unable to model these structural zeros. We define a general framework which allows for structural zeros in the model and propose methods of estimating sparse high-dimensional covariance and precision matrices under this setup. We establish error bounds in the spectral and Frobenius norms for the proposed estimators and empirically verify them with a simulation study. The proposed methodology is illustrated by applying it to the global gut microbiome data of Yatsunenko and others (2012. Human gut microbiome viewed across age and geography. Nature 486, 222-227). Using our methodology we classify subjects according to the geographical location on the basis of their gut microbiome. [ABSTRACT FROM AUTHOR]

Recent developments in ecological statistics have reached behavioral ecology, and an increasing number of studies now apply analytical tools that incorporate alternatives to the conventional null hypothesis testing based on significance levels. However, these approaches continue to receive mixed support in our field. Because our statistical choices can influence research design and the interpretation of data, there is a compelling case for reaching consensus on statistical philosophy and practice. Here, we provide a brief overview of the recently proposed approaches and open an online forum for future discussion (https://bestat.ecoinformatics.org/). From the perspective of practicing behavioral ecologists relying on either correlative or experimental data, we review the most relevant features of information theoretic approaches, Bayesian inference, and effect size statistics. We also discuss concerns about data quality, missing data, and repeatability. We emphasize the necessity of moving away from a heavy reliance on statistical significance while focusing attention on biological relevance and effect sizes, with the recognition that uncertainty is an inherent feature of biological data. Furthermore, we point to the importance of integrating previous knowledge in the current analysis, for which novel approaches offer a variety of tools. We note, however, that the drawbacks and benefits of these approaches have yet to be carefully examined in association with behavioral data. Therefore, we encourage a philosophical change in the interpretation of statistical outcomes, whereas we still retain a pluralistic perspective for making objective statistical choices given the uncertainties around different approaches in behavioral ecology. We provide recommendations on how these concepts could be made apparent in the presentation of statistical outputs in scientific papers. [ABSTRACT FROM PUBLISHER]

Models for estimating the probability of occurrence of a species in the presence of imperfect detection are important in many ecological disciplines. In these “site occupancy” models, the possibility of heterogeneity in detection probabilities among sites must be considered because variation in abundance (and other factors) among sampled sites induces variation in detection probability ( p). In this article, I develop occurrence probability models that allow for heterogeneous detection probabilities by considering several common classes of mixture distributions for p. For any mixing distribution, the likelihood has the general form of a zero-inflated binomial mixture for which inference based upon integrated likelihood is straightforward. A recent paper by Link (2003, Biometrics 59, 1123–1130) demonstrates that in closed population models used for estimating population size, different classes of mixture distributions are indistinguishable from data, yet can produce very different inferences about population size. I demonstrate that this problem can also arise in models for estimating site occupancy in the presence of heterogeneous detection probabilities. The implications of this are discussed in the context of an application to avian survey data and the development of animal monitoring programs. [ABSTRACT FROM AUTHOR]

Felsenstein (1978, Syst. Zool. 27:401–410) showed that the method of maximum parsimony can be inconsistent, i.e., lead to an incorrect result with an infinite amount of data. The situation in which this inconsistency occurs is often called the “Felsenstein zone,” the phenomenon also known as “long-branch attraction.” Felsenstein derived a sufficient inconsistency condition from a model for four taxa with only two different parameters for the probability of change on the five branches connecting the four taxa. In the present paper, his approach is used to derive the inconsistency condition of maximum parsimony from the most general model for four taxa, i.e., with five different parameters for the probabilities of change on the five branches and, for the first time, for characters with k states (k = 2, 3, 4, 5, 6, …). This is used to determine the factors that can cause the inconsistency of maximum parsimony. It is shown that the probability of change on all five branches and the number of character states play a role in causing inconsistency. [Felsenstein zone; k-state characters; long-branch attraction.] [ABSTRACT FROM AUTHOR]

The article introduces papers on large-scale biology included in the September 2006 issue of "The Plant Cell." The topics include transcriptomics, proteomics, metabolomics ad epigenomics. The papers include one by Dan Nettleton on the explanation of the statistical design and analysis principles essential in transcriptome biology and another by Philip Benfey and colleagues' discussion on the state of transcriptomics research.

In this work, the position of contemporary microbiology is considered from the perspective of scientific success, and a list of historical points and lessons learned from the fields of medical microbiology, microbial ecology and systems biology is presented. In addition, patterns in the development of top-down research topics that emerged over time as well as overlapping ideas and personnel, which are the first signs of trans-domain research activities in the fields of metagenomics, metaproteomics, metatranscriptomics and metabolomics, are explored through analysis of the publication networks of 28 654 papers using the computer programme Pajek. The current state of affairs is defined, and the need for meta-analyses to leverage publication biases in the field of microbiology is put forward as a very important emerging field of microbiology, especially since microbiology is progressively dealing with multi-scale systems. Consequently, the need for cross-fertilisation with other fields/disciplines instead of 'more microbiology' is needed to advance the field of microbiology as such. The reader is directed to consider how novel technologies, the introduction of big data approaches and artificial intelligence have transformed microbiology into a multi-scale field and initiated a shift away from its history of mostly manual work and towards a largely technology-, data- and statistics-driven discipline that is often coupled with automation and modelling. [ABSTRACT FROM AUTHOR]

Four groups of postsecondary students, 25 who were deaf/hard of hearing (D/HH), 25 with a learning disability, 25 who were English language learners (ELLs), and 25 without an identified disability studied notes that included text and graphical information based on a physics or a marine biology lecture. The latter 3 groups were normally hearing. All groups had higher scores on post- than on pretests for each lecture, with each group showing generally similar gains in amount of material learned from the pretest to the posttest. For each lecture, the D/HH students scored lower on the pre- and posttests than the other 3 groups of participants. Results indicated that students acquired measurable amounts of information from studying these types of notes for relatively short periods and that the notes have equal potential to support the acquisition of information by each of these groups of students. [ABSTRACT FROM PUBLISHER]

In biology, information flows from the environment to the genome by the process of natural selection. However, it has not been clear precisely what sort of information metric properly describes natural selection. Here, I show that Fisher information arises as the intrinsic metric of natural selection and evolutionary dynamics. Maximizing the amount of Fisher information about the environment captured by the population leads to Fisher's fundamental theorem of natural selection, the most profound statement about how natural selection influences evolutionary dynamics. I also show a relation between Fisher information and Shannon information (entropy) that may help to unify the correspondence between information and dynamics. Finally, I discuss possible connections between the fundamental role of Fisher information in statistics, biology and other fields of science. [ABSTRACT FROM AUTHOR]

In genetics, we often encounter a large number of highly correlated test statistics. The most famous conservative bound for multiple comparison is Bonferroni's bound, which is suitable when the test statistics are independent but not when the test statistics are highly correlated. This article proposes a new conservative bound that is easily calculated without multiple integration and is a good approximation when the test statistics are highly correlated. The performance of the proposed method is evaluated by simulation and real data analysis. [ABSTRACT FROM AUTHOR]

Investigates the effect of compositional heterogeneity on phylogenetic estimates. Ability of phylogenetic methods to recover the correct topology; Analysis of data sets generated by Monte Carlo simulation; Methods for assessing compositional heterogeneity; Relation between the assumption of compositional homogeneity and the data intended for phylogenetic analysis.

Compares maximum likelihood (ML) and maximum parsimony (MP), the methods that biologists use to solve a classic phylogenetic inference problem. Objections made against ML and MP; Applications of ML and MP; Statistical philosophies that provide guidance concerning how nuisance parameters should be handled; Strategies for handling nuisance parameters; Assumptions of parsimony about the evolutionary process.

The authors are clear, "If you are a biologist ... this is your book" and "We assume no prior training in statistics". Statistics is "explained" in two diagrams in the introduction, outlining classical hypothesis testing I versus i the exploratory data analysis approach. The problem is that this appears to be a chimera of three rather different books: statistics, multivariate methods for molecular biology and an R cookbook. [Extracted from the article]