Synergy detection: A practical guide to statistical assessment of potential drug combinations.

Combination treatments have been of increasing importance in drug development across therapeutic areas to improve treatment response, minimize the development of resistance, and/or minimize adverse events. Pre-clinical in-vitro combination experiments aim to explore the potential of such drug combinations during drug discovery by comparing the observed effect of the combination with the expected treatment effect under the assumption of no interaction (i.e., null model). This tutorial will address important design aspects of such experiments to allow proper statistical evaluation. Additionally, it will highlight the Biochemically Intuitive Generalized Loewe methodology (BIGL R package available on CRAN) to statistically detect deviations from the expectation under different null models. A clear advantage of the methodology is the quantification of the effect sizes, together with confidence interval while controlling the directional false coverage rate. Finally, a case study will showcase the workflow in analyzing combination experiments.

Benchmarking digital PCR partition classification methods with empirical and simulated duplex data.

Digital PCR (dPCR) is a highly accurate technique for the quantification of target nucleic acid(s). It has shown great potential in clinical applications, like tumor liquid biopsy and validation of biomarkers. Accurate classification of partitions based on end-point fluorescence intensities is crucial to avoid biased estimators of the concentration of the target molecules. We have evaluated many clustering methods, from general-purpose methods to specific methods for dPCR and flowcytometry, on both simulated and real-life data. Clustering method performance was evaluated by simulating various scenarios. Based on our extensive comparison of clustering methods, we describe the limits of these methods, and formulate guidelines for choosing an appropriate method. In addition, we have developed a novel method for simulating realistic dPCR data. The method is based on a mixture distribution of a Poisson point process and a skew-$t$ distribution, which enables the generation of irregularities of cluster shapes and randomness of partitions between clusters ('rain') as commonly observed in dPCR data. Users can fine-tune the model parameters and generate labeled datasets, using their own data as a template. Besides, the database of experimental dPCR data augmented with the labeled simulated data can serve as training and testing data for new clustering methods. The simulation method is available as an R Shiny app.

Neutralise: An open science initiative for neutral comparison of two-sample tests.

The two-sample problem is one of the earliest problems in statistics: given two samples, the question is whether or not the observations were sampled from the same distribution. Many statistical tests have been developed for this problem, and many tests have been evaluated in simulation studies, but hardly any study has tried to set up a neutral comparison study. In this paper, we introduce an open science initiative that potentially allows for neutral comparisons of two-sample tests. It is designed as an open-source R package, a repository, and an online R Shiny app. This paper describes the principles, the design of the system and illustrates the use of the system.

Measuring DNA quality by digital PCR using probability calculations.

BACKGROUND: Accurate methods to assess DNA integrity are needed for many biomolecular methods. A multiplex digital PCR (dPCR) method designed for interspaced target sequences can be used to assess sequence integrity of large DNA strands. The ratio of single positive partitions versus double positive partitions is then used to calculate the sheared DNA strands. However, this simple calculation is only valid with low DNA concentration. We here describe a method based on probability calculations which enables DNA quality analysis in a large dynamic range of DNA concentrations. RESULTS: Known DNA integrity percentages were mimicked using artificial double stranded DNA in low, intermediate and high DNA concentration scenarios, respectively 600, 12500 and 30000 copies of DNA per reaction. At low concentrations both methods were similar. However, at the intermediate concentration (12500 copies per reaction) the ratio based method started producing a larger error than the proposed probability calculation method with a mean relative error of 20.7 and 16.7 for the Bruner and the proposed method respectively. At the high concentration (30000 copies per reaction) only the proposed method provided accurate measurements with a mean relative error of 60.9 and 9.3 for the ratio based and the proposed method respectively. Furthermore, while both methods have a bias, it is constant for the proposed method, while it decreases with the integrity of the DNA for the ratio based method. The probability calculation equation was extended to 4 dimensions and a proof of concept experiment was performed, the data suggested that the 4 dimensional equation is valid. SIGNIFICANCE AND NOVELTY: We here validate a method of estimating DNA integrity with dPCR using multiple probe combinations, allowing fast and flexible DNA integrity analysis. Additionally, we extend the method from 2 to 4 plex for more accurate DNA integrity measurements.

Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies.

Microbiome data obtained with amplicon sequencing are considered as compositional data. It has been argued that these data can be analysed after appropriate transformation to log-ratios, but ratios and logarithms cause problems with the many zeroes in typical microbiome experiments. We demonstrate that some well chosen sign and rank transformations also allow for valid inference with compositional data, and we show how logistic regression and probabilistic index models can be used for testing for differential abundance, while inheriting the flexibility of a statistical modelling framework. The results of a simulation study demonstrate that the new methods perform better than most other methods, and that it is comparable with ANCOM-BC. These methods are implemented in an R-package 'signtrans' and can be installed from Github (https://github.com/lucp9827/signtrans).

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision.

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.

Digital PCR Partition Classification.

BACKGROUND: Partition classification is a critical step in the digital PCR data analysis pipeline. A range of partition classification methods have been developed, many motivated by specific experimental setups. An overview of these partition classification methods is lacking and their comparative properties are often unclear, likely impacting the proper application of these methods. CONTENT: This review provides a summary of all available digital PCR partition classification approaches and the challenges they aim to overcome, serving as a guide for the digital PCR practitioner wishing to apply them. We additionally discuss strengths and weaknesses of these methods, which can further guide practitioners in vigilant application of these existing methods. This review provides method developers with ideas for improving methods or designing new ones. The latter is further stimulated by our identification and discussion of application gaps in the literature, for which there are currently no or few methods available. SUMMARY: This review provides an overview of digital PCR partition classification methods, their properties, and potential applications. Ideas for further advances are presented and may bolster method development.

Determining Tipping Points and Responses of Macroinvertebrate Traits to Abiotic Factors in Support of River Management.

Although the trait concept is increasingly used in research, quantitative relations that can support in determining ecological tipping points and serve as a basis for environmental standards are lacking. This study determines changes in trait abundance along a gradient of flow velocity, turbidity and elevation, and develops trait-response curves, which facilitate the identification of ecological tipping points. Aquatic macroinvertebrates and abiotic conditions were determined at 88 different locations in the streams of the Guayas basin. After trait information collection, a set of trait diversity metrics were calculated. Negative binomial regression and linear regression were applied to relate the abundance of each trait and trait diversity metrics, respectively, to flow velocity, turbidity and elevation. Tipping points for each environmental variable in relation to traits were identified using the segmented regression method. The abundance of most traits increased with increasing velocity, while they decreased with increasing turbidity. The negative binomial regression models revealed that from a flow velocity higher than 0.5 m/s, a substantial increase in abundance occurs for several traits, and this is even more substantially noticed at values higher than 1 m/s. Furthermore, significant tipping points were also identified for elevation, wherein an abrupt decline in trait richness was observed below 22 m a.s.l., implying the need to focus water management in these altitudinal regions. Turbidity is potentially caused by erosion; thus, measures that can reduce or limit erosion within the basin should be implemented. Our findings suggest that measures mitigating the issues related to turbidity and flow velocity may lead to better aquatic ecosystem functioning. This quantitative information related to flow velocity might serve as a good basis to determine ecological flow requirements and illustrates the major impacts that hydropower dams can have in fast-running river systems. These quantitative relations between invertebrate traits and environmental conditions, as well as related tipping points, provide a basis to determine critical targets for aquatic ecosystem management, achieve improved ecosystem functioning and warrant trait diversity.

Generalized pairwise comparisons for censored data: An overview.

The method of generalized pairwise comparisons (GPC) is an extension of the well-known nonparametric Wilcoxon-Mann-Whitney test for comparing two groups of observations. Multiple generalizations of Wilcoxon-Mann-Whitney test and other GPC methods have been proposed over the years to handle censored data. These methods apply different approaches to handling loss of information due to censoring: ignoring noninformative pairwise comparisons due to censoring (Gehan, Harrell, and Buyse); imputation using estimates of the survival distribution (Efron, Péron, and Latta); or inverse probability of censoring weighting (IPCW, Datta and Dong). Based on the GPC statistic, a measure of treatment effect, the "net benefit," can be defined. It quantifies the difference between the probabilities that a randomly selected individual from one group is doing better than an individual from the other group. This paper aims at evaluating GPC methods for censored data, both in the context of hypothesis testing and estimation, and providing recommendations related to their choice in various situations. The methods that ignore uninformative pairs have comparable power to more complex and computationally demanding methods in situations of low censoring, and are slightly superior for high proportions (>40%) of censoring. If one is interested in estimation of the net benefit, Harrell's c index is an unbiased estimator if the proportional hazards assumption holds. Otherwise, the imputation (Efron or Peron) or IPCW (Datta, Dong) methods provide unbiased estimators in case of proportions of drop-out censoring up to 60%.

Individual reference intervals for personalised interpretation of clinical and metabolomics measurements.

The Population Reference Interval (PRI) refers to the range of outcomes that are expected in a healthy population for a clinical or a diagnostic measurement. It is widely used in daily clinical practice and is essential for assisting clinical decision-making in diagnostics and treatment. In this manuscript, we start from the observation that each healthy individual has its own range for a given variable, depending on personal biological traits. This Individual Reference Interval (IRI) can be calculated and be utilised in clinical practice, in combination with the PRI for improved decision making. Nonparametric estimation of IRIs would require quite long time series. To circumvent this problem, we propose methods based on quantile models in combination with penalised parameter estimation methods that allow for information-sharing among the subjects. Our approach considers the calculation of an IRI as a prediction problem rather than an estimation problem. We perform a simulation study designed to benchmark the methods under different assumptions. From the simulation study we conclude that the new methods are robust and provide empirical coverages close to the nominal level. Finally, we evaluate the methods on real-life data consisting of eleven clinical tests and metabolomics measurements from the VITO IAM Frontier study.

Statistical detection of synergy: New methods and a comparative study.

Combination therapies are increasingly adopted as the standard of care for various diseases to improve treatment response, minimise the development of resistance and/or minimise adverse events. Therefore, synergistic combinations are screened early in the drug discovery process, in which their potential is evaluated by comparing the observed combination effect to that expected under a null model. Such methodology is implemented in the BIGL R-package which allows for a quick screening of drug combinations. We extend the meanR and maxR tests from this package by allowing non-constant variance of the responses and by extending the list of null models (Loewe, Loewe2, HSA, Bliss). These new tests are evaluated in a comprehensive simulation study under various models for additivity and synergy, various monotherapeutic dose-response models (complete, partial and incomplete responders) and various types of deviation from the constant variance assumption. In addition, the BIGL package is extended with bootstrap confidence intervals for the individual off-axis points and for the overall synergy strength, which were demonstrated to have reliable coverage and can complement the existing tests. We conclude that the differences in performance between the different null models are small and depend on the simulation scenario. As a result, the choice of null model should be driven by expert knowledge on the particular problem. Finally, we demonstrate the new features of the BIGL package and the difference between the synergy models on a real dataset from drug discovery. The BIGL package is available at CRAN (https://CRAN.R-project.org/package=BIGL) and as a Shiny app (https://synergy.openanalytics.eu/app).

Seasonal Variation in Prevalence of Mycoplasma hyopneumoniae and Other Respiratory Pathogens in Peri-Weaned, Post-Weaned, and Fattening Pigs with Clinical Signs of Respiratory Diseases in Belgian and Dutch Pig Herds, Using a Tracheobronchial Swab Sampling Technique, and Their Associations with Local Weather Conditions.

Besides Mycoplasma hyopneumoniae (M. hyopneumoniae), many other viruses and bacteria can concurrently be present in pigs. These pathogens can provoke clinical signs, known as porcine respiratory disease complex (PRDC). A sampling technique on live animals, namely tracheobronchial swab (TBS) sampling, was applied to detect different PRDC pathogens in pigs using PCR. The objective was to determine prevalence of different PRDC pathogens and their variations during different seasons, including correlations with local weather conditions. A total of 974 pig farms and 22,266 pigs were sampled using TBS over a 5-year period. TBS samples were analyzed using mPCR and results were categorized and analyzed according to the season of sampling and local weather data. In samples of peri-weaned and post-weaned piglets, influenza A virus in swine (IAV-S), porcine reproductive and respiratory syndrome virus-European strain (PRRSV1), and M. hyopneumoniae were found as predominant pathogens. In fattening pigs, M. hyopneumoniae, porcine circovirus type 2 (PCV-2) and PRRSV1 were predominant pathogens. Pathogen prevalence in post-weaned and finishing pigs was highest during winter, except for IAV-S and A. pleuropneumoniae, which were more prevalent during autumn. Associations between prevalence of several PRDC pathogens, i.e., M. hyopneumoniae, PCV-2 and PRRSV, and specific weather conditions could be demonstrated. In conclusion, the present study showed that many respiratory pathogens are present during the peri-weaning, post-weaning, and fattening periods, which may complicate the clinical picture of respiratory diseases. Interactions between PRDC pathogens and local weather conditions over the 5-year study period were demonstrated.

Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review.

Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.

Development of a 3':5' digital PCR assay to determine horse mRNA integrity.

Accurate tools to measure RNA integrity are essential to obtain reliable gene expression data. The reverse transcription quantitative PCR (RT-qPCR) based 3':5' assay permits a direct determination of messenger RNA (mRNA) integrity. However, the use of standard curves and the possible effect of PCR inhibitors make this method cumbersome and prone to variation, especially in small samples. Here we developed a triplex digital PCR (dPCR) 3':5' assay for assessing RNA integrity in equine samples as rapid and simple alternative to RT-qPCR. This dPCR assay not only provides a straight forward analysis of the mRNA integrity, but also of its quantity.

Supplementation of a ß-mannanase enzyme reduces post-weaning diarrhea and antibiotic use in piglets on an alternative diet with additional soybean meal.

Enzyme supplementation with a ß-mannanase to degrade ß-mannan fibers present in the diet has been shown to restore and improve performance in swine. The current study was conducted on a farm which had historical episodes of post-weaning diarrhea. In total, 896 newly weaned piglets were enrolled in two consecutive trials. Each trial consisted of 32 pens of 14 piglets housed in one large post-weaning compartment. Piglets at the same feeder were randomly assigned to the two treatment groups. The study compared the performance of post-weaned piglets fed either a commercial 3-phase nursery diet (Control) or an adapted diet supplemented with a ß-mannanase (Hemicell HT; Elanco) (Enzyme), with some of the more expensive proteins replaced by soy bean meal in phase 1 and 2, and net energy (NE) content reduced by 65 kcal/kg in phase 3. All data analyses were performed using R version 3.6.3 (R Core Team, 2020). All tests were performed at the 5% level of significance. When multiple testing was involved, the nominal 5% Familywise Error Rate (FWER) was used. The study showed similar performance on the alternative diet with ß-mannanase and the common commercial diets (P >  0.05). However, the Enzyme treated group had a significantly better general clinical score. Moreover, the number of individual treatments was a factor exp(0.69441) or 2 (CI 95% [1.46; 2.74]) higher (P < 0.001) in the Control group as compared to the Enzyme treated group. The number of treated animals was a factor exp(0.62861) or 1.87 (CI 95% [1.43; 2.53]) higher (P < 0.001) and the number of pigs with a repeated treatment was a factor exp(0.9293) or 2.53 (CI 95% [1.26; 5.09]) higher (P = 0.009) in the Control group as compared to the Enzyme treated group. In total, 7 (1.56%) piglets died in the Control group, whereas only 2 (0.45%) piglets died in the Enzyme treated group. The hazard ratio for mortality in the Control group relative to the Enzyme treated group was and estimated as 1.74 (CI 95% [0.51; 5.96]). Thus, the Control group had a non-significantly (P = 0.375) increased mortality. In conclusion, the results suggest that the use of an exogenous heat-tolerant ß-mannanase allowed reduced levels of expensive protein sources to be used in the first two diets fed post-weaning, and 65 kcal/kg lower net energy content to be used in the third diet without adverse effects on intestinal health or overall performance. In fact, the occurrence of PWD and number of individual treatments during the post-weaning period were significantly reduced on the ß-mannanase supplemented diets.

Exploring the Microbiome Analysis and Visualization Landscape.

Research on the microbiome has boomed recently, which resulted in a wide range of tools, packages, and algorithms to analyze microbiome data. Here we investigate and map currently existing tools that can be used to perform visual analysis on the microbiome, and associate the including methods, visual representations and data features to the research objectives currently of interest in microbiome research. The analysis is based on a combination of a literature review and workshops including a group of domain experts. Both the reviewing process and workshops are based on domain characterization methods to facilitate communication and collaboration between researchers from different disciplines. We identify several research questions related to microbiomes, and describe how different analysis methods and visualizations help in tackling them.

Unbiasedness and efficiency of non-parametric and UMVUE estimators of the probabilistic index and related statistics.

In reliability theory, diagnostic accuracy, and clinical trials, the quantity P(X>Y)+1/2P(X=Y), also known as the Probabilistic Index (PI), is a common treatment effect measure when comparing two groups of observations. The quantity P(X>Y)-P(Y>X), a linear transformation of PI known as the net benefit, has also been advocated as an intuitively appealing treatment effect measure. Parametric estimation of PI has received a lot of attention in the past 40 years, with the formulation of the Uniformly Minimum-Variance Unbiased Estimator (UMVUE) for many distributions. However, the non-parametric Mann-Whitney estimator of the PI is also known to be UMVUE in some situations. To understand this seeming contradiction, in this paper a systematic comparison is performed between the non-parametric estimator for the PI and parametric UMVUE estimators in various settings. We show that the Mann-Whitney estimator is always an unbiased estimator of the PI with univariate, completely observed data, while the parametric UMVUE is not when the distribution is misspecified. Additionally, the Mann-Whitney estimator is the UMVUE when observations belong to an unrestricted family. When observations come from a more restrictive family of distributions, the loss in efficiency for the non-parametric estimator is limited in realistic clinical scenarios. In conclusion, the Mann-Whitney estimator is simple to use and is a reliable estimator for the PI and net benefit in realistic clinical scenarios.

Correction to: On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments.

An amendment to this paper has been published and can be accessed via the original article.

Sequence count data are poorly fit by the negative binomial distribution.

Sequence count data are commonly modelled using the negative binomial (NB) distribution. Several empirical studies, however, have demonstrated that methods based on the NB-assumption do not always succeed in controlling the false discovery rate (FDR) at its nominal level. In this paper, we propose a dedicated statistical goodness of fit test for the NB distribution in regression models and demonstrate that the NB-assumption is violated in many publicly available RNA-Seq and 16S rRNA microbiome datasets. The zero-inflated NB distribution was not found to give a substantially better fit. We also show that the NB-based tests perform worse on the features for which the NB-assumption was violated than on the features for which no significant deviation was detected. This gives an explanation for the poor behaviour of NB-based tests in many published evaluation studies. We conclude that nonparametric tests should be preferred over parametric methods.

On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments.

BACKGROUND: In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets. RESULT: The data generating model in pooled experiments is defined mathematically to evaluate the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined. CONCLUSION: For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power.