Jan De Neve

The more, the merrier: heterotroph richness stimulates methanotrophic activity

Although microorganisms coexist in the same environment, it is still unclear how their interaction regulates ecosystem functioning. Using a methanotroph as a model microorganism, we determined how methane oxidation responds to heterotroph diversity. Artificial communities comprising of a methanotroph and increasing heterotroph richness, while holding equal starting cell numbers were assembled. We considered methane oxidation rate as a functional response variable. Our results showed a significant increase of methane oxidation with increasing heterotroph richness, suggesting a complex interaction in the cocultures leading to a stimulation of methanotrophic activity. Therefore, not only is the methanotroph diversity directly correlated to methanotrophic activity for some methanotroph groups as shown before, but also the richness of heterotroph interacting partners is relevant to enhance methane oxidation too. In this unprecedented study, we provide direct evidence showing how heterotroph richness exerts a response in methanotroph–heterotroph interaction, resulting in increased methanotrophic activity. Our study has broad implications in how methanotroph and heterotroph interact to regulate methane oxidation, and is particularly relevant in methane-driven ecosystems.

ddpcRquant: threshold determination for single channel droplet digital PCR experiments

Digital PCR is rapidly gaining interest in the field of molecular biology for absolute quantification of nucleic acids. However, the first generation of platforms still needs careful validation and requires a specific methodology for data analysis to distinguish negative from positive signals by defining a threshold value. The currently described methods to assess droplet digital PCR (ddPCR) are based on an underlying assumption that the fluorescent signal of droplets is normally distributed. We show that this normality assumption does not likely hold true for most ddPCR runs, resulting in an erroneous threshold. We suggest a methodology that does not make any assumptions about the distribution of the fluorescence readouts. A threshold is estimated by modelling the extreme values in the negative droplet population using extreme value theory. Furthermore, the method takes shifts in baseline fluorescence between samples into account. An R implementation of our method is available, allowing automated threshold determination for absolute ddPCR quantification using a single fluorescent reporter.

Comparison of the Abiotic Preferences of Macroinvertebrates in Tropical River Basins

We assessed and compared abiotic preferences of aquatic macroinvertebrates in three river basins located in Ecuador, Ethiopia and Vietnam. Upon using logistic regression models we analyzed the relationship between the probability of occurrence of five macroinvertebrate families, ranging from pollution tolerant to pollution sensitive, (Chironomidae, Baetidae, Hydroptilidae, Libellulidae and Leptophlebiidae) and physical-chemical water quality conditions. Within the investigated physical-chemical ranges, nine out of twenty-five interaction effects were significant. Our analyses suggested river basin dependent associations between the macroinvertebrate families and the corresponding physical-chemical conditions. It was found that pollution tolerant families showed no clear abiotic preference and occurred at most sampling locations, i.e. Chironomidae were present in 91%, 84% and 93% of the samples taken in Ecuador, Ethiopia and Vietnam. Pollution sensitive families were strongly associated with dissolved oxygen and stream velocity, e.g. Leptophlebiidae were only present in 48%, 2% and 18% of the samples in Ecuador, Ethiopia and Vietnam. Despite some limitations in the study design, we concluded that associations between macroinvertebrates and abiotic conditions can be river basin-specific and hence are not automatically transferable across river basins in the tropics.

A Regression Framework for Rank Tests Based on the Probabilistic Index Model

We demonstrate how many classical rank tests, such as the Wilcoxon–Mann–Whitney, Kruskal–Wallis, and Friedman test, can be embedded in a statistical modeling framework and how the method can be used to construct new rank tests. In addition to hypothesis testing, the method allows for estimating effect sizes with an informative interpretation, resulting in a better understanding of the data. Supplementary materials for this article are available online.

An extension of the Wilcoxon-Mann-Whitney test for analyzing RT-qPCR data.

Abstract Classical approaches for analyzing reverse transcription quantitative polymerase chain reaction (RT-qPCR) data commonly require normalization before assessing differential expression (DE). Normalization often has a substantial effect on the interpretation and validity of the subsequent analysis steps, but at the same time it causes a reduction in variance and introduces dependence among the normalized outcomes. These effects can be substantial, however, they are typically ignored. Most normalization techniques and methods for DE focus on mean expression and are sensitive to outliers. Moreover, in cancer studies, for example, oncogenes are often only expressed in a subsample of the populations during sampling. This primarily affects the skewness and the tails of the distribution and the mean is therefore not necessarily the best effect size measure within these experimental setups. In our contribution, we propose an extension of the Wilcoxon-Mann-Whitney test which incorporates a robust normalization, and the uncertainty associated with normalization is propagated into the final statistical summaries for DE. Our method relies on semiparametric regression models that focus on the probability P{Y≤Y′}, where Y and Y′ denote independent responses for different subject groups. This effect size is robust to outliers, while remaining informative and intuitive when DE affects the shape of the distribution instead of only the mean. We also extend our approach for assessing DE for multiple features simultaneously. Simulation studies show that the test has a good performance, and that it is very competitive with standard methods for this platform. The method is illustrated on two neuroblastoma studies.

unifiedWMWqPCR: the unified Wilcoxon–Mann–Whitney test for analyzing RT-qPCR data in R

MOTIVATION Recently, De Neve et al. proposed a modification of the Wilcoxon-Mann-Whitney (WMW) test for assessing differential expression based on RT-qPCR data. Their test, referred to as the unified WMW (uWMW) test, incorporates a robust and intuitive normalization and quantifies the probability that the expression from one treatment group exceeds the expression from another treatment group. However, no software package for this test was available yet. RESULTS We have developed a Bioconductor package for analyzing RT-qPCR data with the uWMW test. The package also provides graphical tools for visualizing the effect sizes. AVAILABILITY AND IMPLEMENTATION The unifiedWMWqPCR package and its user documentation can be obtained through Bioconductor.

Robust regression methods for real-time polymerase chain reaction.

Current real-time polymerase chain reaction (PCR) data analysis methods implement linear least squares regression methods for primer efficiency estimation based on standard curve dilution series. This method is sensitive to outliers that distort the outcome and are often ignored or removed by the end user. Here, robust regression methods are shown to provide a reliable alternative because they are less affected by outliers and often result in more precise primer efficiency estimators than the linear least squares method.

Pitfalls in Prediction Modeling for Normal Tissue Toxicity in Radiation Therapy: An Illustration With the Individual Radiation Sensitivity and Mammary Carcinoma Risk Factor Investigation Cohorts

PURPOSE To identify the main causes underlying the failure of prediction models for radiation therapy toxicity to replicate. METHODS AND MATERIALS Data were used from two German cohorts, Individual Radiation Sensitivity (ISE) (n=418) and Mammary Carcinoma Risk Factor Investigation (MARIE) (n=409), of breast cancer patients with similar characteristics and radiation therapy treatments. The toxicity endpoint chosen was telangiectasia. The LASSO (least absolute shrinkage and selection operator) logistic regression method was used to build a predictive model for a dichotomized endpoint (Radiation Therapy Oncology Group/European Organization for the Research and Treatment of Cancer score 0, 1, or ≥2). Internal areas under the receiver operating characteristic curve (inAUCs) were calculated by a naïve approach whereby the training data (ISE) were also used for calculating the AUC. Cross-validation was also applied to calculate the AUC within the same cohort, a second type of inAUC. Internal AUCs from cross-validation were calculated within ISE and MARIE separately. Models trained on one dataset (ISE) were applied to a test dataset (MARIE) and AUCs calculated (exAUCs). RESULTS Internal AUCs from the naïve approach were generally larger than inAUCs from cross-validation owing to overfitting the training data. Internal AUCs from cross-validation were also generally larger than the exAUCs, reflecting heterogeneity in the predictors between cohorts. The best models with largest inAUCs from cross-validation within both cohorts had a number of common predictors: hypertension, normalized total boost, and presence of estrogen receptors. Surprisingly, the effect (coefficient in the prediction model) of hypertension on telangiectasia incidence was positive in ISE and negative in MARIE. Other predictors were also not common between the 2 cohorts, illustrating that overcoming overfitting does not solve the problem of replication failure of prediction models completely. CONCLUSIONS Overfitting and cohort heterogeneity are the 2 main causes of replication failure of prediction models across cohorts. Cross-validation and similar techniques (eg, bootstrapping) cope with overfitting, but the development of validated predictive models for radiation therapy toxicity requires strategies that deal with cohort heterogeneity.

Goodness-of-Fit Methods for Probabilistic Index Models

A class of semiparametric regression models, called probabilistic index models, has been recently proposed. Because these models are semiparametric, inference is only valid when the proposed model is consistent with the underlying data-generating model. However, no formal goodness-of-fit methods for these probabilistic index models exist yet. We propose a test and a graphical tool for assessing the model adequacy. Simulation results indicate that both methods succeed in detecting lack-of-fit. The methods are also illustrated on a case study.

High-dimensional prediction of binary outcomes in the presence of between-study heterogeneity

Many prediction methods have been proposed in the literature, but most of them ignore heterogeneity between populations. Either only data from a single study or population is available for model building and evaluation, or when data from multiple studies make up the training dataset, studies are pooled before model building. As a result, prediction models might perform less than expected when applied to new subjects from new study populations. We propose a linear method for building prediction models with high-dimensional data from multiple studies. Our method explicitly addresses between-population variability and tends to select predictors that are predictive in most of the study populations. We employ empirical Bayes estimators and hence avoid selection bias during the variable selection process. Simulation results demonstrate that the new method works better than other linear prediction methods that ignore the between-study variability. Our method is developed for classification into two groups.