Rank-based Analyses Research Articles

MutRank: an R shiny web-application for exploratory targeted mutual rank-based coexpression analyses integrated with user-provided supporting information.

The rapid assignment of genotypes to phenotypes has been a historically challenging process. The discovery of genes encoding biosynthetic pathway enzymes for defined plant specialized metabolites has been informed and accelerated by the detection of gene clusters. Unfortunately, biosynthetic pathway genes are commonly dispersed across chromosomes or reside in genes clusters that provide little predictive value. More reliably, transcript abundance of genes underlying biochemical pathways for plant specialized metabolites display significant coregulation. By rapidly identifying highly coexpressed transcripts, it is possible to efficiently narrow candidate genes encoding pathway enzymes and more easily predict both functions and functional associations. Mutual Rank (MR)-based coexpression analyses in plants accurately demonstrate functional associations for many specialized metabolic pathways; however, despite the clear predictive value of MR analyses, the application is uncommonly used to drive new pathway discoveries. Moreover, many coexpression databases aid in the prediction of both functional associations and gene functions, but lack customizability for refined hypothesis testing. To facilitate and speed flexible MR-based hypothesis testing, we developed MutRank, an R Shiny web-application for coexpression analyses. MutRank provides an intuitive graphical user interface with multiple customizable features that integrates user-provided data and supporting information suitable for personal computers. Tabular and graphical outputs facilitate the rapid analyses of both unbiased and user-defined coexpression results that accelerate gene function predictions. We highlight the recent utility of MR analyses for functional predictions and discoveries in defining two maize terpenoid antibiotic pathways. Beyond applications in biosynthetic pathway discovery, MutRank provides a simple, customizable and user-friendly interface to enable coexpression analyses relating to a breadth of plant biology inquiries. Data and code are available at GitHub: https://github.com/eporetsky/MutRank.

Altered Expression of Aggrecan, FAM20B, B3GALT6, and EXTL2 in Patients with Osteoarthritis and Kashin-Beck Disease

The objective of this study was to investigate the expression of enzymes involved in synthesis and modification of chondroitin sulfate (CS) in knee cartilage tissue of patients with osteoarthritis (OA) and Kashin-Beck disease (KBD). The knee articular cartilage samples were obtained from 18 age- and gender-matched donors with 6 each in KBD, OA, and control groups. Hematoxylin and eosin (HE) staining, toluidine blue (TB) staining, and immunohistochemical (IHC) staining were performed to estimate the expression level and localization of aggrecan, along with FAM20B, GalT-II, and EXTL2, which are associated with CS synthesis and modification. Rank-based analyses of variance test was used for the multiple comparisons of discrepancy in the positive staining rate among the 3 groups. In HE and TB staining results, damaged morphology, decreased chondrocyte numbers and proteoglycans were observed in OA and KBD groups compared with the control group. In line with these trends, the positive staining rates of aggrecan were lower in KBD and OA groups than in the control group. Meanwhile, the positive staining rates of CS chain modifying enzymes FAM20B, GalT-II, and EXTL2 decreased in OA and KBD groups. In conclusion, it was demonstrated that altered expression of CS chain modifying enzymes in OA and KBD groups influenced the synthesis procession of CS and could contribute to the damage of cartilage. Further investigation of these enzymes can provide new theoretical and experimental targets for OA and KBD pathogenesis studies.

An Assessment of Physical Education Teacher Candidates’ Dispositions

The study assessed physical education teacher candidates’ (TCs) dispositions toward teaching. Participants included 72 physical education TCs (80.56% males and 19.44% females). TCs completed the Missouri Educator Profile (MEP) once, as part of the requirements for admission to the teacher education program at their institution. The MEP is a 10-point Likert scale with six subscales: Achievement (AT), Social Influence (SI), Interpersonal Effectiveness (IP), Self-Adjustment (SA), Conscientiousness (CN), and Practical Intelligence (PI). The predictor variables were TCs’ sex, PE major status, cumulative GPA (GPA), number of hours attempted (HRA), number of hours passed (HRP) at the time of taking the MEP, transfer student status (TSS), and first semester of enrollment (FSE). Descriptive data indicated that the SA subscale (45.83%) had the highest percentage of TCs with high scores, followed by SI (33.33%). Conversely, the lowest percentage of TCs with high scores was in PI (5.56%). Nonparametric rank-based regression analyses showed that sex and PE major status were significant predictors of SI, IP, and PI. Female TCs had lower scores than their male counterparts on these subscales. TCs who maintained their status as PE majors after taking the MEP had lower scores than those who changed majors. Furthermore, HRA and HRP significantly predicted the IP and PI subscales. TCs with higher number of hours attempted had higher scores on the IP and PI subscales. Conversely, the higher the number of hours passed, the lower the score on the IP and PI subscales. All the predictor variables were not significant for subscales AT, SA, and CN. Finally, transfer student status was not a significant predictor of any of the dispositions. Physical Education Teacher Education programs need to teach and assess TCs’ dispositions on a regular basis. Dispositional assessment data could serve as valuable source of feedback for teacher education program improvement.

Genotype–phenotype associations in children with copy number variants associated with high neuropsychiatric risk in the UK (IMAGINE-ID): a case-control cohort study

Several copy number variants (CNVs) are associated with a high risk of neurodevelopmental and psychiatric disorders (referred to as ND-CNVs). We aimed to characterise the effect of ND-CNVs on childhood development and investigate whether different ND-CNVs lead to distinct and specific patterns of cognitive and behavioural outcomes. In this case-control study, we used data from the Intellectual Disability and Mental Health: Assessing the Genomic Impact on Neurodevelopment (IMAGINE-ID) study. Children aged 4 years and older with pathogenic CNV or single nucleotide variants were recruited via the UK National Health Service (NHS) medical genetic clinic network and via patient support groups to complete broad online phenotyping, from whom children aged 6-19 years with at least one of a specific group of ND-CNVs (1q21.1 [proximal duplication, and distal deletion and duplication], 2p16.3 deletion, 9q34.3 deletion, 15q11.2 deletion, 15q13.3 deletion and duplication, 16p11.2 [proximal deletion and duplication, and distal deletion], and 22q11.2 deletion and duplication) and their families were approached for a deep phenotyping, home-based assessment, and we report on this sample here. We invited siblings of index children to participate as controls, for whom the presence of ND-CNVs was excluded by use of microarray results and also medical records where possible. We systematically assessed the children for psychiatric disorders and broader traits of neurodevelopmental, cognitive, and psychopathological origin and compared results of ND-CNV carriers with control siblings to test the hypothesis that phenotypes would differ by genotype, both quantitatively in terms of severity and qualitatively in the pattern of associated impairments. Between Oct 1, 2014, and Dec 31, 2018, of 2819 children recruited, 258 (9%) had one ND-CNV of interest, with 13 CNVs across nine loci, and underwent a home-based assessment. 106 control siblings were enrolled. 186 (80%) of ND-CNV carriers met criteria for one or more psychiatric disorder (odds ratio [OR] 13·8, 95% CI 7·2-26·3, compared with controls). The risk of attention-deficit hyperactivity disorder (OR 6·9, 3·2-15·1), oppositional defiant disorder (OR 3·6, 1·4-9·4), any anxiety disorder (OR 2·9, 1·2-6·7), and autism spectrum disorder traits (OR 44·1, 15·3-127·5) was particularly high compared with controls. ND-CNV carriers were impaired across all neurodevelopmental, cognitive, and psychopathological traits compared with controls. Only moderate quantitative and qualitative differences in phenotypic profile were found between genotypes. Overall, the range of phenotypes was broadly similar for all ND-CNV genotypes. Traits did show some evidence of genotypic specificity, with rank-based analyses showing moderate qualitative and quantitative profile differences between ND-CNVs; however, the specific genotype accounted for a low proportion of variance in cognitive and behavioural outcomes (approximately 5-20% depending on the trait). The 13 ND-CNVs studied have a similar range of adverse effects on childhood neurodevelopment, despite subtle quantitative and qualitative differences. Genomic risk for neuropsychiatric disorder has pleiotropic effects on multiple processes and neural circuits and indicates that future research should avoid being narrowly focused on single phenotypes. UK Medical Research Council and Medical Research Foundation.

A Monte Carlo study of REML and robust rank-based analyses for the random intercept mixed model

ABSTRACTRestricted maximum likelihood (REML) methods are traditionally used for analyzing mixed models. Based on a multivariate normal likelihood, these analyses are sensitive to outliers. Recently developed robust rank-based procedures offer a complete analysis of mixed model: estimation of fixed effects, standard errors, and estimation of variance components. The results of a large Monte Carlo study are presented, comparing these two analyses for many situations over multivariate normal and contaminated normal distributions. The rank-based analyses are much more powerful and efficient than the REML analyses over all non-normal situations, while losing little power for normal errors.

New Precision Metrics for Contrast Sensitivity Testing.

Visual sensitivity is comprehensively described by the contrast sensitivity function (CSF), but current routine clinical care does not include its assessment because of the time-consuming need to estimate thresholds for a large number of spatial frequencies. The quick CSF method, however, dramatically reduces testing times by using a Bayesian information maximization rule. We evaluate the test-retest variability of a tablet-based quick CSF implementation in a study with 100 subjects who repeatedly assessed their vision with and without optical correction. We first discuss two commonly used measures of repeatability, intraclass correlation and the Bland-Altman Coefficient of Repeatability, and show that they are vulnerable to artifacts. Instead, we propose to formulate precision as an information retrieval task: from all repeat test scores, can we retrieve a certain individual based on their first test score? We then use rank-based analyses such as mean average precision as a better measure to compare different test metrics, and show that the highest test-retest precision is achieved using a summary statistic, the area under the log CSF (AULCSF). This demonstrates the benefit of assessment of the whole CSF compared to sensitivity at individual spatial frequencies only. AULCSF also yields best discrimination performance (99.2%) between measurements that were taken with and without glasses, respectively, even better than CSF Acuity. The tablet-based quick CSF thus enables the rapid and reliable home monitoring of visual function, which has the potential to improve early diagnosis and treatment of ophthalmic pathologies such as diabetic retinopathy or age-related macular degeneration.

Dynamics modeling and advanced metaheuristics based LQG controller design for a Quad Tilt Wing UAV

This paper deals with the modelling and advanced metaheuristics-based tuning of a Linear Quadratic Gaussian (LQG) controller for a particular class of convertible Unmanned Aerial Vehicles (UAVs), called Quad Tilt-Wing (QTW). A dynamical model for the vertical, transition and horizontal flights is firstly derived using the Newton–Euler formalism. Since the main design parameters in the LQG control approach are the weighting matrices which are usually selected by classical and hard trials-errors based procedures, an optimization problem under operating constraints is formulated for systematically tuning these decision variables. Such an optimization-based LQG control problem is systematically solved using advanced and recent metaheuristics such as the Harmonic Search Algorithm, Water Cycle Algorithm (WCA) and Fractional Particle Swarm Optimization-based Memetic Algorithm (FPSOMA). To assess the performance of the proposed algorithms and conclude about their control parameters choices, an empirical comparison study is performed for solving different test functions from the single-objective optimization literature. The classical Particle Swarm Optimization and Ant Bee Colony algorithms are retained as comparison tools. Value- and Friedmans rank-based statistical analyses are presented. After that, experiments are conducted to achieve optimal coefficients of weighting matrices of LQG controllers in solving QTW position and heading stabilization problem. Demonstrative simulations show that the WCA, outperforming each one of other metaheuristics, provides the best results in terms of robustness and performance for the problem in hand. Results verify the efficiency the proposed metaheuristics-tuned LQG control structures.

A Monte Carlo study of traditional and rank-based picked-points analyses

ABSTRACTIn analysis of covariance with heteroscedastic slopes a picked-points analysis is often performed. Least-squares based picked-points analyses often lose efficiency (at times substantial) for nonnormal error distributions. Robust rank-based picked-points analyses are developed which are optimizable for heavy-tailed and/or skewed error distributions. The results of a Monte Carlo investigation of these analyses are presented. The situations include the normal model and models which violate it in one or several ways. Empirically the rank-based analyses appear to be valid over all these situations and more powerful than the least squares analysis for all the nonnormal models, while losing little efficiency at the normal model.

Rlme: An R Package for Rank-Based Estimation and Prediction in Random Effects Nested Models

There is a lack of robust statistical analyses for random effects linear models. In practice, statistical analyses, including estimation, prediction and inference, are not reliable when data are unbalanced, of small size, contain outliers, or not normally distributed. It is fortunate that rank-based regression analysis is a robust nonparametric alternative to likelihood and least squares analysis. We propose an R package that calculates rank-based statistical analyses for two- and three-level random effects nested designs. In this package, a new algorithm which recursively obtains robust predictions for both scale and random effects is used, along with three rank-based fitting methods. Rank-based procedures retain distribution-free estimation and testing properties. These procedures are much less sensitive to outliers than the traditional analyses when random errors are not normally distributed. Alternative robust score functions can be accommodated with the rank-based methods to protect analyses from influential observations in factor and response spaces. Also, the choice of these score functions could depend on the prior knowledge on error distributions. The Wilcoxon score function is fairly efficient for moderate to heavy-tailed error distributions. For example, rank-based procedures with Wilcoxon scores achieve up to 95% efficiency relative to least squares methods when the data are normal and are much more efficient than least squares methods for heavy tailed error distributions. These properties make the rank-based methods appealing. However, to our knowledge, statistical analyses for random effects models using the rank-based methodology have not yet been considered in any statistical package. This article proposes an R package with three rank-based fitting methods that estimate fixed effects and predict random effects in two- and three-level random effects nested models. The rank-based norm, analogous to the least squares norm, is briefly defined as

Nonparametric Approaches to the Analysis of Crossover Studies

We illustrate nonparametric, and particularly rank-based analyses of crossover studies, designs in which each subject receives more than one treatment over time. Principles involved in using the Wilcoxon rank sum test in the simple two-period, two-treatment crossover are described through theory and example. We then extend the ideas to two-treatment designs with more than two periods and to three-treatment, three-period designs. When more than one nonparametric approach is available, we consider the issue of statistical power in choosing an appropriate test.

Phylogenetic balance and ecological evenness.

The frequency distribution of numbers of species in taxonomic groups, where many species belong to a few very diverse higher taxa, is mirrored by that of species in most communities, where many individuals belong to a few very abundant species. Various hypotheses mechanistically link a species' community abundance with the diversity of the higher level taxon (genus, family, order) to which it belongs, but empirical data are equivocal about general trends in the relation between rank-taxon diversity and mean abundance. One reason for this inconclusive result may be the effect of the semisubjective nature of rank-based classification. We assessed the relationship between clade diversity and mean species abundance for two diverse tropical tree communities, using both traditional rank-based analysis and two new phylogenetic analyses (based on the ratio of individuals to taxa at each node in the phylogeny). Both rank-based and phylogenetic analyses using taxonomic ranks above the species level as terminal taxa detected a trend associating common species with species-rich families. In contrast, phylogenetic analyses using species as terminal taxa could not distinguish the observed distribution of species abundances from a random distribution with respect to clade diversity. The difference between these results might be due to (1) the absence of a real phylogeny-wide relationship between clade abundance and diversity, (2) the influence of poor phylogenetic resolution within families in our phylogenies, or (3) insufficient sensitivity of our metrics to subtle tree-wide effects. Further development and application of phylogeny-based methods for testing abundance-diversity relationships is needed.

Historical evolutionary information in the traditional Linnean hierarchy

The first two ranks above the species level in the traditional Linnean hierarchy — the genus and family — are species based: genera have been erected to unify groups of morphologically similar, closely related species and families have been erected to group genera recognized as closely related because of the shared morphologic characteristics of their species. Diversity patterns of traditional genera and families thus appear congruent with those of species in (a) the Recent (e. g., latitudinal gradients in many groups), (b) compilations of all marine taxa for the entire Phanerozoic (including the stage level), (c) comparisons through time within individual taxa (e. g., Foraminifera, Rugosa, Conodonta), and (d) simulation studies. Genera and families often have a more robust fossil record of diversity than species, especially for poorly sampled groups (e. g., echinoids), because of the range-through record of these polytypic taxa. Simulation studies indicate that paraphyly among traditionally defined taxa is not a fatal problem for diversity studies; in fact, when degradation of the quality of the fossil record is modelled, both diversity and rates of origination and extinction are better represented by including paraphyletic taxa than by restricting data to monophyletic clades. This result underscores the utility of traditional rank-based analyses of the history of diversity.In contrast, the three higher ranks of the Linnean hierarchy — orders, classes and phyla — are defined and recognized by key character complexes assumed to be rooted deep in the developmental program and, therefore, considered to be of special significance. These taxa are unified on the basis of body plan and function, not species morphology. Even if paraphyletic, recognition of such taxa is useful because they represent different functional complexes that reflect biological organization and major evolutionary innovations, often with different ecological capacities. Phanerozoic diversity patterns of orders, classes and phyla are not congruent with those of lower taxa; the higher groups each increased rapidly in the early Paleozoic, during the explosive diversification of body plans in the Cambrian, and then remained stable or declined slightly after the Ordovician. The diversity history of orders superficially resembles that of lower taxa, but this is a result only of ordinal turnover among the Echinodermata coupled with ordinal radiation in the Chordata; it is not a highly damped signal derived from the diversity of species, genera, or families. Despite the stability of numbers among post-Ordovician Linnean higher taxa, the diversity of lower taxa within many of these Bauplan groups fluctuated widely, and these diversity patterns signal embedded ecologic information, such as differences in flexibility in filling or utilizing ecospace.Phylogenetic analysis is vital for understanding the origins and genealogical structure of higher taxa. Only in such fashion can convergence and its implications for ecological constraints and/or opportunities be understood. But blind insistence on the use of monophyletic classifications in all studies would obscure some of the important information contained in traditional taxonomic groupings. The developmental modifications that characterize Linnean higher taxa (and traditionally separate them from their paraphyletic ancestral taxa) provide keys to understanding the role of shifting ecology in macroevolutionary success.

Statistical methods for analyzing developmental toxicity data

A description and review of methods for performing per-litter analyses involving extrabinomial proportion response is provided. It is stressed that the litter should be regarded as the appropriate experimental unit for quantitative analysis in studies for teratogenic or heritable mutagenic effects. Attention is directed at statistical identification of possible treatment effects, such as a positive dose response to a chemical stimulus. The methods range from distribution-free, nonparametric analyses to models involving parametric distributions such as the beta-binomial density. It is seen that most current methods require computer implementation. When concern is raised over misspecification of assumptions critical to the statistical analysis, it is argued that relatively parameter-free methods are appropriate for use. These include statistical bootstrapping and rank-based analyses.