Biology subjects

Senior, A. M.

Publications and source records attributed to Senior, A. M..

4 recordsLinked to original sources

bioRxiv2023

eNODAL: an experimentally guided nutriomics data clustering method to unravel complex drug-diet interactions

Unraveling the complex interplay between nutrients and drugs via their effects on omics features could revolutionize our fundamental understanding of nutritional physiology, personalized nutrition and ultimately human health-span. Experimental studies in nutrition are starting to use large-scale omics experiments to pick apart the effects of such interacting factors. However, the high dimensionality of the omics features, coupled with complex fully-factorial experimental designs together pose a challenge to the analysis. Current strategies for analyzing such types of data are based on between-feature correlations. However, these techniques risk overlooking important signals that arise from the experimental design and produce clusters that are hard to interpret. We present a novel approach for analyzing high-dimensional outcomes in nutriomics experiments, termed experiment-guided NutriOmics DatA cLustering (eNODAL). This three-step hybrid framework takes advantage of both ANOVA-type analyses and unsupervised learning methods to extract maximum information from experimental nutriomics studies. First, eNODAL categorizes the omics features into interpretable groups based on the significance of response to the different experimental variables using an ANOVA-like test. Such groups may include the main effects of a nutritional intervention, and drug exposure, or their interaction. Second, consensus clustering is performed within each interpretable group to further identify subclusters of features with similar response profiles to these experimental factors. Third, eNODAL annotates these subclusters based on their experimental responses and biological pathways enriched within the subcluster. We validate eNODAL using data from a mouse experiment to test for the interaction effects of macronutrient intake and drugs that target aging mechanisms in mice.

bioinformatics↗

bioRxiv2021

Engaging the complexity of diet and healthy aging in humans

Little is known about how normal variation in dietary patterns in humans affects the aging process, largely because both nutrition and the physiology of aging are highly complex and multidimensional. Here, we apply the nutritional geometry framework to data from 1560 older adults followed over four years to assess how nutrient intake patterns affect the aging process. Aging was quantified via blood biomarkers integrated to measure loss of homeostasis. Additionally, we extend nutritional geometry to 19 micronutrients. Salient results include benefits of intermediate protein and vitamin E intake. Broadly, we show that there are few simple answers of "good" or "bad" nutrients - optimal levels are generally intermediate, but dependent on other nutrients. Simpler linear/univariate analytical approaches are insufficient to capture such associations. We present an interactive tool to explore the results, and our approach presents a roadmap for future studies to explore the full complexity of the nutrition-aging landscape. Impact StatementMultidimensional nutritional analyses reveal how the association between diet and healthy aging is hard to untangle, as most nutrients have non-linear and interactive effects in humans.

bioinformatics↗

bioRxiv2020

Embrace heterogeneity to improve reproducibility: A perspective from meta-analysis of variation in preclinical research

The reproducibility of research results has been a cause of increasing concern to the scientific community. The long-held belief that experimental standardization begets reproducibility has also been recently challenged, with the observation that the reduction of variability within studies can lead to idiosyncratic, lab-specific results that are irreproducible. An alternative approach is to, instead, deliberately introduce heterogeneity; known as "heterogenization" of experimental design. Here, we explore a novel perspective in the heterogenization program in a meta-analysis of variability in observed phenotypic outcomes in both control and experimental animal models of ischaemic stroke. First, by quantifying inter-individual variability across control groups we illustrate that the samount of heterogeneity in disease-state (infarct volume) differs according to methodological approach, for example, in disease-induction methods and disease models. We argue that such methods may improve reproducibility by creating diverse and representative distribution of baseline disease-state in the reference group, against which treatment efficacy is assessed. Second, we illustrate how meta-analysis can be used to simultaneously assess efficacy and stability (i.e., mean effect and among-individual variability). We identify treatments that have efficacy and are generalizable to the population level (i.e. low inter-individual variability), as well as those where there is high inter-individual variability in response; for these latter treatments translation to a clinical setting may require nuance. We argue that by embracing rather than seeking to minimise variability in phenotypic outcomes, we can motivate the shift towards heterogenization and improve both the reproducibility and generalizability of preclinical research.

neuroscience↗

bioRxiv2020

Revisiting and expanding the meta-analysis of variation: The log coefficient of variation ratio, lnCVR

Meta-analyses are frequently used to quantify the difference in the average values of two groups (e.g., control and experimental treatment groups), but examine the difference in the variability (variance) of two groups. For such comparisons, the two relatively new effect size statistics, namely the log-transformed variability ratio (the ratio of two standard deviations; lnVR) and the log-transformed CV ratio (the ratio of two coefficients of variation; lnCVR) are useful. In practice, lnCVR may be of most use because a treatment may affect the mean and the variance simultaneously. We review current, and propose new, estimators for lnCVR and lnVR. We also present methods for use when the two groups are dependent (e.g., for cross-over and pre-test-post-test designs). A simulation study evaluated the performance of these estimators and we make recommendations about which estimators one should use to minimise bias. We also present two worked examples that illustrate the importance of accounting for the dependence of the two groups. We found that the degree to which dependence is accounted for in the sampling variance estimates can impact heterogeneity parameters such as{tau} 2 (i.e., the between-study variance) and I2 (i.e., the proportion of the total variability due to between-study variance), and even the overall effect, and in turn qualitative interpretations. Meta-analytic comparison of the variability between two groups enables us to ask completely new questions and to gain fresh insights from existing datasets. We encourage researchers to take advantage of these convenient new effect size measures for the meta-analysis of variation.

bioinformatics↗