On triangle inequalities of correlation-based distances for gene expression profiles

Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data

Capturing and tracking large-scale brain activity dynamics holds the potential to deepen our understanding of cognition. Previously, tools from topological data analysis, especially Mapper, have been successfully used to mine brain activity dynamics at the highest spatiotemporal resolutions. Even though it is a relatively established tool within the field of topological data analysis, Mapper results are highly impacted by parameter selection. Given that noninvasive human neuroimaging data (e.g., from fMRI) is typically fraught with artifacts and no gold standards exist regarding "true" state transitions, we argue for a thorough examination of Mapper parameter choices to better reveal their impact. Using synthetic data (with known transition structure) and real fMRI data, we explore a variety of parameter choices for each Mapper step, thereby providing guidance and heuristics for the field. We also release our parameter exploration toolbox as a software package to make it easier for scientists to investigate and apply Mapper to any dataset.

Variable selection for nonlinear dimensionality reduction of biological datasets through bootstrapping of correlation networks

Identifying the most relevant variables or features in massive datasets for dimensionality reduction can lead to improved and more informative display, faster computation times, and more explainable models of complex systems. Despite significant advances and available algorithms, this task generally remains challenging, especially in unsupervised settings. In this work, we propose a method that constructs correlation networks using all intervening variables and then selects the most informative ones based on network bootstrapping. The method can be applied in both supervised and unsupervised scenarios. We demonstrate its functionality by applying Uniform Manifold Approximation and Projection for dimensionality reduction to several high-dimensional biological datasets, derived from 4D live imaging recordings of hundreds of morpho-kinetic variables, describing the dynamics of thousands of individual leukocytes at sites of prominent inflammation. We compare our method with other standard ones in the field, such as Principal Component Analysis and Elastic Net, showing that it outperforms them. The proposed method can be employed in a wide range of applications, encompassing data analysis and machine learning.

Hot and heavy: Responses of ragworms (Hediste diversicolor) to copper-spiked sediments and elevated temperature

Sediment contamination and seawater warming are two major stressors to macrobenthos in estuaries. However, little is known about their combined effects on infaunal organisms. Here we investigated the responses of an estuarine polychaete Hediste diversicolor to metal-contaminated sediment and increased temperature. Ragworms were exposed to sediments spiked with 10 and 20 mg kg^-1 of copper at 12 and 20 °C for three weeks. No considerable changes were observed in the expression of genes related to copper homeostasis and in the accumulation of oxidative stress damage. Dicarbonyl stress was attenuated by warming exposure. Whole-body energy reserves in the form of carbohydrates, lipids and proteins were little affected, but the energy consumption rate increased with copper exposure and elevated temperature indicating higher basal maintenance costs of ragworms. The combined effects of copper and warming exposures were mostly additive, with copper being a weak stressor and warming a more potent stressor. These results were replicable, as confirmed by two independent experiments of similar settings conducted at two different months of the year. This study suggests the higher sensitivity of energy-related biomarkers and the need to search for more conserved molecular markers of metal exposure in H. diversicolor.