In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.

Here we detail the use of an R package, \'EcoCountHelper\', and an associated analytical pipeline aimed at making generalized linear mixed-effects model (GLMM)-based analysis of ecological count data more accessible. We recommend a GLMM-based analysis workflow that allows the user to (1) employ selection of distributional forms (Poisson vs negative binomial) and zero-inflation (ZIP and ZINB, respectively) using AIC and variance-mean plots, (2) examine models for goodness-of-fit using simulated residual diagnostics, (3) interpret model results via easy to understand outputs of changes in predicted responses, and (4) compare the magnitude of predictor variable effects via effects plots. Our package uses a series of easy-to-use functions that can accept both wide- and long-form multi-taxa count data without the need for programming experience. To demonstrate the utility of this approach, we use our package to model acoustic bat activity data relative to multiple landscape characteristics in a protected area (Grand Teton National Park), which is threatened by encroaching disease-white nose syndrome. Global threats to bat conservation such as disease and deforestation have prompted extensive research to better understand bat ecology. Notwithstanding these efforts, managers operating on lands crucial to the persistence of bat populations are often equipped with too little information regarding local bat activity to make informed land-management decisions. In our case study in the Tetons, we found that an increased prevalence of porous buildings increases activity levels of Eptesicus fuscus and Myotis volans; Myotis lucifugus activity decreases as distance to water increases; and Myotis volans activity increases with the amount of forested area. By using GLMMs in tandem with \'EcoCountHelper\', managers without advanced programmatic or statistical expertise can assess the effects of landscape characteristics on wildlife in a statistically-robust framework.

Phase II trials that evaluate target therapies based on a biomarker must be well designed in order to assess anti-tumor activity as well as clinical utility of the biomarker. Classical phase II designs do not deal with this molecular heterogeneity and can lead to an erroneous conclusion in the whole population, whereas a subgroup of patients may well benefit from the new therapy. Moreover, the target population to be evaluated in a phase III trial may be incorrectly specified. Alternative approaches are proposed in the literature that make it possible to include two subgroups according to biomarker status (negative/positive) in the same study. Jones, Parashar and Tournoux et al. propose different stratified adaptive two-stage designs to identify a subgroup of interest in a heterogeneous population that could possibly benefit from the experimental treatment at the end of the first or second stage. Nevertheless, these designs are rarely used in oncology research. After introducing these stratified adaptive designs, we present an R package (ph2hetero) implementing these methods. A case study is provided to illustrate both the designs and the use of the R package. These stratified adaptive designs provide a useful alternative to classical two-stage designs and may also provide options in contexts other than biomarker studies.

The attributable fraction (or attributable risk) is a widely used measure that quantifies the public health impact of an exposure on an outcome. Even though the theory for AF estimation is well developed, there has been a lack of up-to-date software implementations. The aim of this article is to present a new R package for AF estimation with binary exposures. The package AF allows for confounder-adjusted estimation of the AF for the three major study designs: cross-sectional, (possibly matched) case-control and cohort. The article is divided into theoretical sections and applied sections. In the theoretical sections we describe how the confounder-adjusted AF is estimated for each specific study design. These sections serve as a brief but self-consistent tutorial in AF estimation. In the applied sections we use real data examples to illustrate how the AF package is used. All datasets in these examples are publicly available and included in the AF package, so readers can easily replicate all analyses.

Bridging ImmunoGenomic Data-Analysis Workflow Gaps (BIGDAWG) is an integrated data-analysis pipeline designed for the standardized analysis of highly-polymorphic genetic data, specifically for the HLA and KIR genetic systems. Most modern genetic analysis programs are designed for the analysis of single nucleotide polymorphisms, but the highly polymorphic nature of HLA and KIR data require specialized methods of data analysis. BIGDAWG performs case-control data analyses of highly polymorphic genotype data characteristic of the HLA and KIR loci. BIGDAWG performs tests for Hardy-Weinberg equilibrium, calculates allele frequencies and bins low-frequency alleles for k×2 and 2×2 chi-squared tests, and calculates odds ratios, confidence intervals and p-values for each allele. When multi-locus genotype data are available, BIGDAWG estimates user-specified haplotypes and performs the same binning and statistical calculations for each haplotype. For the HLA loci, BIGDAWG performs the same analyses at the individual amino-acid level. Finally, BIGDAWG generates figures and tables for each of these comparisons. BIGDAWG obviates the error-prone reformatting needed to traffic data between multiple programs, and streamlines and standardizes the data-analysis process for case-control studies of highly polymorphic data. BIGDAWG has been implemented as the bigdawg R package and as a free web application at bigdawg.immunogenomics.org.