Single-cell transcriptomics has enhanced our understanding of the brain’s cellular composition. Biologists now analyze complex datasets to explore how marker genes influence biological processes, genetic variations, and phenotypic traits. A challenge is comparing these datasets across species to detect subtle differences or similarities to evolutionary development. Here, we present Cytosplore EvoViewer to facilitate examining relationships between transcriptomic datasets across species, simplifying the analysis of marker gene regulation and its impact on biological functions and integrating these findings with prior evolutionary knowledge or species-specific traits. We conducted a design study, including domain analysis, implementation of the results into Cytosplore EvoViewer, and an expert evaluation. Cytosplore EvoViewer offers valuable insights into genetic variations and evolutionary dynamics, helping to understand the diversity and the unity within diversity across species and their evolutionary development.The Cytosplore EvoViewer installer application can be downloaded from the Cytosplore Viewer website1, and its source code is available on the ManiVault Studio GitHub2.1https://viewer.cytosplore.org2https://github.com/ManiVaultStudio/CytosploreEvoViewer

Exploration and analysis of high-dimensional data are important tasks in many fields that produce large and complex data, like the financial sector, systems biology, or cultural heritage. Tailor-made visual analytics software is developed for each specific application, limiting their applicability in other fields. However, as diverse as these fields are, their characteristics and requirements for data analysis are conceptually similar. Many applications share abstract tasks and data types and are often constructed with similar building blocks. Developing such applications, even when based mostly on existing building blocks, requires significant engineering efforts. We developed ManiVault, a flexible and extensible open-source visual analytics framework for analyzing high-dimensional data. The primary objective of ManiVault is to facilitate rapid prototyping of visual analytics workflows for visualization software developers and practitioners alike. ManiVault is built using a plugin-based architecture that offers easy extensibility. While our architecture deliberately keeps plugins self-contained, to guarantee maximum flexibility and re-usability, we have designed and implemented a messaging API for tight integration and linking of modules to support common visual analytics design patterns. We provide several visualization and analytics plugins, and ManiVault's API makes the integration of new plugins easy for developers. ManiVault facilitates the distribution of visualization and analysis pipelines and results for practitioners through saving and reproducing complete application states. As such, ManiVault can be used as a communication tool among researchers to discuss workflows and results. A copy of this paper and all supplemental material is available at osf.io/9k6jw, and source code at github.com/ManiVaultStudio.

Stochastically solving the rendering integral (particularly visibility) is the de-facto standard for physically-based light transport but it is computationally expensive, especially when displaying heterogeneous volumetric data. In this work, we present efficient techniques to speed-up the rendering process via a novel visibility-estimation method in concert with an unbiased importance sampling (involving environmental lighting and visibility inside the volume), filtering, and update techniques for both static and animated scenes. Our major contributions include a progressive estimate of partial occlusions based on a fast sweeping-plane algorithm. These occlusions are stored in an octahedral representation, which can be conveniently transformed into a quadtree-based hierarchy suited for a joint importance sampling. Further, we propose sweep-space filtering, which suppresses the occurrence of fireflies and investigate different update schemes for animated scenes. Our technique is unbiased, requires little precomputation, is highly parallelizable, and is applicable to a various volume data sets, dynamic transfer functions, animated volumes and changing environmental lighting.