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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., Computer Graphics and Visualisation, Pattern Recognition and Bioinformatics

We present a set of interactive visual analysis techniques aiming at explaining data patterns in multidimensional projections. Our novel techniques include a global value-based encoding that highlights point groups having outlier values in any dimension as well as several local tools that provide details on the statistics of all dimensions for a user-selected projection area. Our techniques generically apply to any projection algorithm and scale computationally well to hundreds of thousands of points and hundreds of dimensions. We describe a user study that shows that our visual tools can be quickly learned and applied by users to obtain non-trivial insights in real-world multidimensional datasets.

In spatial transcriptomics (ST) data, biologically relevant features such as tissue compartments or cell-state transitions are reflected by gene expression gradients. Here, we present SpaceWalker, a visual analytics tool for exploring the local gradient structure of 2D and 3D ST data. The user can be guided by the local intrinsic dimensionality of the high-dimensional data to define seed locations, from which a flood-fill algorithm identifies transcriptomically similar cells on the fly, based on the high-dimensional data topology. In several use cases, we demonstrate that the spatial projection of these flooded cells highlights tissue architectural features and that interactive retrieval of gene expression gradients in the spatial and transcriptomic domains confirms known biology. We also show that SpaceWalker generalizes to several different ST protocols and scales well to large, multi-slice, 3D whole-brain ST data while maintaining real-time interaction performance., Pattern Recognition and Bioinformatics, Computer Graphics and Visualisation

Companies, institutions and researchers around the world are collecting enormous sets of high-dimensional data at breakneck speed. However, our understanding of the collected data is not nearly keeping up. One of the main approaches to understanding these datasets has been to reduce the data to a low-dimensional representation, called a projection, that can subsequently be visualised. Seeing visible patterns in these projections indicates there are relationships between the dimensions of the high-dimensional data. However, it does not tell us anything about what those relationships are. Several efforts have previously been made to explain the patterns in the projection in terms of their original dimensions. However, they tend to fall short in adequately explaining them, or the techniques don't scale well to a higher number of dimensions. Therefore, this thesis aims to answer the question how to adequately explain these patterns in projections of high-dimensional data, while simultaneously scaling better than previous techniques in the number of data dimensions. We extend the variance-based explanations of previous work with a value-based explanation, that gives insight into, not only why the patterns are there, but what they represent. Furthermore, we introduce a user-driven exploration mechanism that provides significantly more detailed explanations of regions in the projection. In addition, these explanations are augmented by a number of tools that support their function. We integrate all of the above elements into a visualisation solution for exploring high-dimensional data projections. We assess the visualisation system using an evaluation study asking a mix of 23 experts and non-experts to analyze several datasets of increasing dimensionality (12, 31, 58) using the proposed solution, as well as their opinion on the usefulness of each of the elements of the visualisation solution. Participants rated each of the elements of the visualisation system highly in term