Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org
 .

/pdf/gsva-gene-set-variation-analysis-for-microarray-and-rna-seq-1bwfkmry7e.pdf

GSVA: gene set variation analysis for microarray and RNA-seq data.

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Modern Applied Statistics With S

In the past decade, extracellular vesicles (EVs) have been recognized as potent vehicles of intercellular communication, both in prokaryotes and eukaryotes. This is due to their capacity to transfer proteins, lipids and nucleic acids, thereby influencing various physiological and pathological functions of both recipient and parent cells. While intensive investigation has targeted the role of EVs in different pathological processes, for example, in cancer and autoimmune diseases, the EV-mediated maintenance of homeostasis and the regulation of physiological functions have remained less explored. Here, we provide a comprehensive overview of the current understanding of the physiological roles of EVs, which has been written by crowd-sourcing, drawing on the unique EV expertise of academia-based scientists, clinicians and industry based in 27 European countries, the United States and Australia. This review is intended to be of relevance to both researchers already working on EV biology and to newcomers who will encounter this universal cell biological system. Therefore, here we address the molecular contents and functions of EVs in various tissues and body fluids from cell systems to organs. We also review the physiological mechanisms of EVs in bacteria, lower eukaryotes and plants to highlight the functional uniformity of this emerging communication system.

/pdf/biological-properties-of-extracellular-vesicles-and-their-bef7axtqvb.pdf

Biological properties of extracellular vesicles and their physiological functions

The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Convergence of probability measures

Permutation inference for the general linear model.

Longitudinal data from factorial experiments frequently arise in various fields of study, ranging from medicine and biology to public policy and sociology In most practical situations, the distribution of observed data is unknown and there may exist a number of atypical measurements and outliers Hence, use of parametric and semi-parametric procedures that impose restrictive distributional assumptions on observed longitudinal samples becomes questionable This, in turn, has led to a substantial demand for statistical procedures that enable us to accurately and reliably analyze longitudinal measurements in factorial experiments with minimal conditions on available data, and robust nonparametric methodology offering such a possibility becomes of particular practical importance In this article, we introduce a new R package nparLD which provides statisticians and researchers from other disciplines an easy and user-friendly access to the most up-to-date robust rank-based methods for the analysis of longitudinal data in factorial settings We illustrate the implemented procedures by case studies from dentistry, biology, and medicine

nparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments

Preface. Acknowledgments. Introduction. Models. Effects and Hypotheses. Estimators for Relative Effects. Test Statistics. Software. Experiments for One Group of Subjects. Experiements for Several Groups of Subjects. Dependent Replications. Multifactorial Experiments. Numerous Time Points. References. Appendix A: Original Data. Appendix B: Results from the SAS Macros. Glossary. Index.

Nonparametric analysis of longitudinal data in factorial experiments

A generalization of the Behrens-Fisher problem for two samples is examined in a nonparametric model. It is not assumed that the underlying distribution functions are continuous so that data with arbitrary ties can be handled. A rank test is considered where the asymptotic variance is estimated consistently by using the ranks over all observations as well as the ranks within each sample. The consistency of the estimator is derived in the appendix. For small samples (n 1 , n 2 > 10), a simple approximation by a central t-distribution is suggested where the degrees of freedom are taken from the Satterthwaite-Smith-Welch approximation in the parametric Behrens-Fisher problem. It is demonstrated by means of a simulation study that the Wilcoxon-Mann-Whitney-test may be conservative or liberal depending on the ratio of the sample sizes and the variances of the underlying distribution functions. For the suggested approximation, however, it turns out that the nominal level is maintained rather accurately. The suggested nonparametric procedure is applied to a data set from a clinical trial. Moreover, a confidence interval for the nonparametric treatment effect is given.

The Nonparametric Behrens-Fisher Problem: Asymptotic Theory and a Small-Sample Approximation

Background: To evaluate the impact of psychopathological comorbidity with oppositional defiant/conduct disorder (ODD/CD) on brain electrical correlates in children with attention deficit hyperactivity disorder (ADHD) and to study the pathophysiological background of comorbidity of ADHD+ODD/CD.



Method: Event-related potentials (ERPs) were recorded during a cued continuous performance test (CPT-A-X) in children (aged 8 to 14 years) with ICD-10 diagnoses of either hyperkinetic disorder (HD; n=15), hyperkinetic conduct disorder (HCD; n=16), or ODD/CD (n=15) and normal children (n=18). HD/HCD diagnoses in all children were fully concordant with the DSM-IV diagnosis of ADHD-combined type. ERP-microstates, i.e., time segments with stable brain electrical map topography were identified by adaptive segmentation. Their characteristic parameters and behavioral measures were further analyzed.



Results: Children with HD but not comorbid children showed slower and more variable reaction times compared to control children. Children with HD and ODD/CD-only but not comorbid children displayed reduced P3a amplitudes to cues and certain distractors (distractor-X) linked to attentional orienting. Correspondingly, global field power of the cue-CNV microstate related to anticipation and preparation was reduced in HD but not in HCD. Topographical alterations of the HD occurred already in the cue-P2/N2 microstate. In sum, the comorbid group was less deviant than both the HD-group and the ODD/CD-group.



Conclusions: The findings suggest that HD children (ADHD-combined type without ODD/CD) suffer from a more general deficit (e.g., suboptimal energetical state regulation) including deficits of attentional orienting and response preparation than just a response inhibitory deficit, backing the hypothesis of an involvement of a dysregulation of the central noradrenergic networks. The results contradict the hypothesis that ADHD+ODD/CD represents an additive co-occurrence of ADHD and ODD/CD and strongly suggest that it represents a separate pathological entity as considered in the ICD-10 classification system, which differs from both HD and ODD/CD-only.

Association of ADHD and conduct disorder – brain electrical evidence for the existence of a distinct subtype

In this paper, we summarize some recent developments in the analysis of nonparametric models where the classical models of ANOVA are generalized in such a way that not only the assumption of normality is relaxed but also the structure of the designs is introduced in a broader framework and also the concept of treatment effects is redefined. The continuity of the distribution functions is not assumed so that not only data from continuous distributions but also data with ties are included in this general setup. In designs with independent observations as well as in repeated measures designs, the hypotheses are formulated by means of the distribution functions. The main results are given in a unified form. Some applications to special designs are considered, where in simple designs, some well known statistics (such as the Kruskal-Wallis statistic and the χ2-statistic for dichotomous data) come out as special cases. The general framework presented here enables the nonparametric analysis of data with continuous distribution functions as well as arbitrary discrete data such as count data, ordered categorical and dichotomous data.

Edgar Brunner

Papers

nparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments

Nonparametric analysis of longitudinal data in factorial experiments

The Nonparametric Behrens-Fisher Problem: Asymptotic Theory and a Small-Sample Approximation

Association of ADHD and conduct disorder – brain electrical evidence for the existence of a distinct subtype

Nonparametric methods in factorial designs