Abstract Single‐cell RNA sequencing (scRNA‐seq) has gained prominence as a valuable technique for examining cellular gene expression patterns at the individual cell level. In the analysis of scRNA‐seq datasets, it is common practice to visualise a subset of principal components (PCs), obtained via principal component analysis (PCA), using dimensionality reduction techniques such as t‐stochastic neighbour embedding (t‐SNE). Determining the number of PCs (i.e. dimensionality) is a critical step that influences the outcome of single‐cell analysis, and this process typically requires a labour‐intensive manual assessment involving the inspection of numerous projection plots. To address this challenge, we present a visualisation system that assists analysts in efficiently determining the optimal dimensionality of scRNA‐seq data. The proposed system employs two hull heatmaps, a cell type heatmap and a cluster heatmap, which offer comprehensive representations of target cells of multiple cell types across various dimensionalities through the utilisation of a convex hull‐embedded colour map. The cell type heatmap shows overlaps between cell types, and the cluster heatmap compares cell clustering results. The proposed hull heatmaps effectively alleviate the labourious task of manually evaluating hundreds of projection plots for searching for the optimal dimensionality. Additionally, our system offers interactive visualisation of gene expression levels and an intuitive lasso selection tool, thereby enabling analysts to progressively refine the convex hulls on the hull heatmaps. We validated the usefulness of the proposed system through two quantitative evaluations and three case studies.

Immersive analytics is gaining attention across multiple domains due to its capability to facilitate intuitive data analysis in expansive environments through user interaction with data. However, creating immersive analytics systems for specific tasks is challenging due to the need for programming expertise and significant development effort. Despite the introduction of various immersive visualization authoring toolkits, domain experts still face hurdles in adopting immersive analytics into their workflow, particularly when faced with dynamically changing tasks and data in real time. To lower such technical barriers, we introduce XROps, a web-based authoring system that allows users to create immersive analytics applications through interactive visual programming, without the need for low-level scripting or coding. XROps enables dynamic immersive analytics authoring by allowing users to modify each step of the data visualization process with immediate feedback, enabling them to build visualizations on-the-fly and adapt to changing environments. It also supports the integration and visualization of real-time sensor data from XR devices-a key feature of immersive analytics-facilitating the creation of various analysis scenarios. We evaluated the usability of XROps through a user study and demonstrate its efficacy and usefulness in several example scenarios.

Single-cell RNA sequencing (scRNA-seq) is becoming popular in studying the gene expression of cells at the single-cell level. ScRNA-seq enables analysts to characterize cell types, thereby providing a better understanding of dynamic biological processes. In scRNA-seq data analysis, principal component analysis (PCA) is commonly used to reduce at least thousands of dimensions in the raw data to a manageable size so that analysts can visualize and cluster cells to identify different cell types. The conventional process to determine the optimal dimensionality includes a laborious manual review of hundreds of different projection plots. To address this problem, we introduce a dimensionality explorer for single-cell analysis, which is a visualization system that helps analysts to effectively determine the optimal dimensionality of scRNA-seq data. It employs a hull heatmap, which provides a holistic view of overlaps among multiple cell types across various dimensionalities using a convex hull-embedded color map. The hull heatmap effectively reduces the burden of manually reviewing hundreds of projection plots to determine the optimal dimensionality. Our system also provides interactive gene expression level visualization and intuitive lasso selection, thereby allowing analysts to progressively refine the convex hulls of the hull heatmap. We demonstrate the usefulness of the proposed system through a user study and three case studies conducted by domain experts.

In this paper, we propose a novel machine learning-based voxel classification method for highly-accurate volume rendering. Unlike conventional voxel classification methods that incorporate intensity-based features, the proposed method employs dictionary based features learned directly from the input data using hierarchical multi-scale 3D convolutional sparse coding, a novel extension of the state-of-the-art learning-based sparse feature representation method. The proposed approach automatically generates high-dimensional feature vectors in up to 75 dimensions, which are then fed into an intelligent system built on a random forest classifier for accurately classifying voxels from only a handful of selection scribbles made directly on the input data by the user. We apply the probabilistic transfer function to further customize and refine the rendered result. The proposed method is more intuitive to use and more robust to noise in comparison with conventional intensity-based classification methods. We evaluate the proposed method using several synthetic and real-world volume datasets, and demonstrate the methods usability through a user study.