Novel statistical approaches for non-normal censored immunological data: analysis of cytokine and gene expression data

Background: For several immune-mediated diseases, immunological analysis will become more complex in the future with datasets in which cytokine and gene expression data play a major role. These data have certain characteristics that require sophisticated statistical analysis such as strategies for non-normal distribution and censoring. Additionally, complex and multiple immunological relationships need to be adjusted for potential confounding and interaction effects. Objective: We aimed to introduce and apply different methods for statistical analysis of non-normal censored cytokine and gene expression data. Furthermore, we assessed the performance and accuracy of a novel regression approach in order to allow adjusting for covariates and potential confounding. Methods: For non-normally distributed censored data traditional means such as the Kaplan-Meier method or the generalized Wilcoxon test are described. In order to adjust for covariates the novel approach named Tobit regression on ranks was introduced. Its performance and accuracy for analysis of non-normal censored cytokine/gene expression data was evaluated by a simulation study and a statistical experiment applying permutation and bootstrapping. Results: If adjustment for covariates is not necessary traditional statistical methods are adequate for non-normal censored data. Comparable with these and appropriate if additional adjustment is required, Tobit regression on ranks is a valid method. Its power, type-I error rate and accuracy were comparable to the classical Tobit regression. Conclusion: Non-normally distributed censored immunological data require appropriate statistical methods. Tobit regression on ranks meets these requirements and can be used for adjustment for covariates and potential confounding in large and complex immunological datasets

Deep imputation on large‐scale drug discovery data

More accurate predictions of the biological properties of chemical compounds would guide the selection and design of new compounds in drug discovery and help to address the enormous cost and low success-rate of pharmaceutical R&D. However this domain presents a significant challenge for AI methods due to the sparsity of compound data and the noise inherent in results from biological experiments. In this paper, we demonstrate how data imputation using deep learning provides substantial improvements over quantitative structure-activity relationship (QSAR) machine learning models that are widely applied in drug discovery. We present the largest-to-date successful application of deep-learning imputation to datasetswhich arecomparablein sizetothe corporate data repository of a pharmaceutical company (678,994 compounds by 1166 endpoints). We demonstrate this improvement for three areas of practical application linked to distinct use cases; i) target activity data compiled from a range of drug discovery projects, ii) a high value and heterogeneous datasetcovering complex absorption, distribution, metabolism and elimination properties and, iii) high throughput screeningdata, testing thealgorithm’slimits on early-stage noisy and very sparse data.Achieving median coefficients of determination, 2, of 0.69, 0.36 and 0.43 respectively across these applications, the deep learning imputation method offers an unambiguous improvement over random forest QSAR methods, which achieve median 2 values of 0.28, 0.19 and 0.23 respectively.We also demonstrate that robust estimates of the uncertainties in the predicted values correlate strongly with the accuracies in prediction, enabling greater confidence in decision-making based on the imputed values.Optibrium Ltd, Intellegens Ltd, Takeda, Royal Societ

Heritable Differences in Schooling Behavior among Threespine Stickleback Populations Revealed by a Novel Assay

Identifying the proximate and ultimate mechanisms of social behavior remains a major goal of behavioral biology. In particular, the complex social interactions mediating schooling behavior have long fascinated biologists, leading to theoretical and empirical investigations that have focused on schooling as a group-level phenomenon. However, methods to examine the behavior of individual fish within a school are needed in order to investigate the mechanisms that underlie both the performance and the evolution of schooling behavior. We have developed a technique to quantify the schooling behavior of an individual in standardized but easily manipulated social circumstances. Using our model school assay, we show that threespine sticklebacks (Gasterosteus aculeatus) from alternative habitats differ in behavior when tested in identical social circumstances. Not only do marine sticklebacks show increased association with the model school relative to freshwater benthic sticklebacks, they also display a greater degree of parallel swimming with the models. Taken together, these data indicate that marine sticklebacks exhibit a stronger tendency to school than benthic sticklebacks. We demonstrate that these population-level differences in schooling tendency are heritable and are shared by individuals within a population even when they have experienced mixed-population housing conditions. Finally, we begin to explore the stimuli that elicit schooling behavior in these populations. Our data suggest that the difference in schooling tendency between marine and benthic sticklebacks is accompanied by differential preferences for social vs. non-social and moving vs. stationary shelter options. Our study thus provides novel insights into the evolution of schooling behavior, as well as a new experimental approach to investigate the genetic and neural mechanisms that underlie this complex social behavior

Conspicuous Female Ornamentation and Tests of Male Mate Preference in Threespine Sticklebacks (Gasterosteus aculeatus)

Sexual selection drives the evolution of exaggerated male ornaments in many animal species. Female ornamentation is now acknowledged also to be common but is generally less well understood. One example is the recently documented red female throat coloration in some threespine stickleback (Gasterosteus aculeatus) populations. Although female sticklebacks often exhibit a preference for red male throat coloration, the possibility of sexual selection on female coloration has been little studied. Using sequential and simultaneous mate choice trials, we examined male mate preferences for female throat color, as well as pelvic spine color and standard length, using wild-captured threespine sticklebacks from the Little Campbell River, British Columbia. In a multivariate analysis, we found no evidence for a population-level mate preference in males, suggesting the absence of directional sexual selection on these traits arising from male mate choice. Significant variation was detected among males in their preference functions, but this appeared to arise from differences in their mean responsiveness across mating trials and not from variation in the strength (i.e., slope) of their preference, suggesting the absence of individual-level preferences as well. When presented with conspecific intruder males, male response decreased as intruder red throat coloration increased, suggesting that males can discriminate color and other aspects of phenotype in our experiment and that males may use these traits in intrasexual interactions. The results presented here are the first to explicitly address male preference for female throat color in threespine sticklebacks.Open Access Publishing Fun

No hormonal response in tied fights

Inexistent

TDP-43 Identified from a Genome Wide RNAi Screen for SOD1 Regulators

Amyotrophic Lateral Sclerosis (ALS) is a late-onset, progressive neurodegenerative disease affecting motor neurons in the brain stem and spinal cord leading to loss of voluntary muscular function and ultimately, death due to respiratory failure. A subset of ALS cases are familial and associated with mutations in superoxide dismutase 1 (SOD1) that destabilize the protein and predispose it to aggregation. In spite of the fact that sporadic and familial forms of ALS share many common patho-physiological features, the mechanistic relationship between SOD1-associated and sporadic forms of the disease if any, is not well understood. To better understand any molecular connections, a cell-based protein folding assay was employed to screen a whole genome RNAi library for genes that regulate levels of soluble SOD1. Statistically significant hits that modulate SOD1 levels, when analyzed by pathway analysis revealed a highly ranked network containing TAR DNA binging protein (TDP-43), a major component of aggregates characteristic of sporadic ALS. Biochemical experiments confirmed the action of TDP-43 on SOD1. These results highlight an unexpected relationship between TDP-43 and SOD1 which may have implications in disease pathogenesis