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Proteomics techniques generate an avalanche of data and promise to satisfy biologists' long-held desire to measure absolute protein abundances on a genome-wide scale. However, can this knowledge be translated into a clearer picture of how cells invest their protein resources? This article aims to give a broad perspective on the composition of proteomes as gleaned from recent quantitative proteomics studies. We describe proteomaps, an approach for visualizing the composition of proteomes with a focus on protein abundances and functions. In proteomaps, each protein is shown as a polygon-shaped tile, with an area representing protein abundance. Functionally related proteins appear in adjacent regions. General trends in proteomes, such as the dominance of metabolism and protein production, become easily visible. We make interactive visualizations of published proteome datasets accessible at www.proteomaps.net . We suggest that evaluating the way protein resources are allocated by various organisms and cell types in different conditions will sharpen our understanding of how and why cells regulate the composition of their proteomes.

Hosts and their symbionts are involved in intimate physiological and ecological interactions. The impact of these interactions on the evolution of each partner depends on the time-scale considered. Short-term dynamics – ‘coevolution’ in the narrow sense – has been reviewed elsewhere. We focus here on the long-term evolutionary dynamics of cospeciation and speciation following host shifts. Whether hosts and their symbionts speciate in parallel, by cospeciation, or through host shifts, is a key issue in host–symbiont evolution. In this review, we first outline approaches to compare divergence between pairwise associated groups of species, their advantages and pitfalls. We then consider recent insights into the long-term evolution of host–parasite and host–mutualist associations by critically reviewing the literature. We show that convincing cases of cospeciation are rare (7%) and that cophylogenetic methods overestimate the occurrence of such events. Finally, we examine the relationships between short-term coevolutionary dynamics and long-term patterns of diversification in host–symbiont associations. We review theoretical and experimental studies showing that short-term dynamics can foster parasite specialization, but that these events can occur following host shifts and do not necessarily involve cospeciation. Overall, there is now substantial evidence to suggest that coevolutionary dynamics of hosts and parasites do not favor long-term cospeciation.

Voronoi treemaps are widely used for hierarchical data visualization. Existing methods calculate the visualization layouts of hierarchical data by combining the proportion optimization of weights and Lloyd’s method of sites. However, this may not only produce results with large area errors but also require more time consumption. Besides, the relative visualization position of the same data element between adjacent frames in dynamic hierarchical data may be changed abruptly, resulting in unclear visual results. To this end, we propose an efficient and topological structure preserved visualization approach, called PowerHierarchy, for visualizing hierarchical data. Firstly, an improved version of the power diagram computing algorithm is introduced to generate the visualization layouts of each data element in the hierarchy. Unlike random initialization, we construct a centroidal Voronoi tessellation as input and then use a Breadth-First traversing strategy to adapt the depth information to produce visual layouts of static hierarchical data. Based on this, an updating scheme is presented for visualizing dynamic hierarchical data, where previous results are iteratively fed as inputs to initialize current layouts. Besides, the external boundary sites and their subsites are projected onto the visual boundary and then moved into the visual region with the relative position preserved. Experimental results on several datasets demonstrate the efficiency, accuracy, and topology preservation advantage of our proposed visualization approach.

The associations between pathogens and their hosts are complex and can result from a variety of evolutionary processes including codivergence, lateral transfer, or duplication. Papillomaviruses (PVs) are double-stranded DNA viruses ubiquitously present in mammals and are a suitable target for rigorous statistical tests of potential virus–host codivergence. We analyze the evolutionary dynamics of PV diversification by comparing robust phylogenies of PVs and their respective hosts using different statistical approaches to assess topological and branch-length congruence. Mammalian PVs segregated into four diverse major clades that overlapped to varying degrees in terms of their mammalian host lineages. The hypothesis that PVs and hosts evolved independently was globally rejected (P = 0.0001), although only 90 of 207 virus–host associations (43%) were significant in individual tests. Virus–host codivergence accounted roughly for one-third of the evolutionary events required to reconcile PV–host evolutionary histories. When virus–host associations were analyzed locally within each of the four viral clades, numerous independent topological congruencies were identified that were incompatible with respect to the global trees. These results support an evolutionary scenario in which early PV radiation was followed by independent codivergence between viruses within each of the major clades and their hosts. Moreover, heterogeneous groups of closely related PVs infecting non-related hosts suggest several interspecies transmission events. Our results argue thus for the importance of alternative events in PV evolution, in contrast to the prevailing opinion that these viruses show a high degree of host specificity and codivergence.

The Treemap is one of the most relevant information visualization (InfoVis) techniques to support the analysis of large hierarchical data structures or data clusters. Despite that, Treemap still presents some challenges for data representation, such as the few options for visual data mappings and the inability to represent zero and negative values. Additionally, visualizing high dimensional data requires many hierarchies, which can impair data visualization. Thus, this paper proposes to add layered glyphs to Treemap’s items to mitigate these issues. Layered glyphs are composed of N partially visible layers, and each layer maps one data dimension to a visual variable. Since the area of the upper layers is always smaller than the bottom ones, the layers can be stacked to compose a multidimensional glyph. To validate this proposal, we conducted a user study to compare three scenarios of visual data mappings for Treemaps: only Glyphs (G), Glyphs and Hierarchy (GH), and only Hierarchy (H). Thirty-six volunteers with a background in InfoVis techniques, organized into three groups of twelve (one group per scenario), performed 8 InfoVis tasks using only one of the proposed scenarios. The results point that scenario GH presented the best accuracy while having a task-solving time similar to scenario H, which suggests that representing more data in Treemaps with layered glyphs enriched the Treemap visualization capabilities without impairing the data readability.

As the diversity and complexity of geographic statistical data continue to increase, it becomes increasingly important to present multi-level information in order to meet a broader range of needs. In response to the limitations of existing visualization methods in representing the geographic distribution of statistical data, this paper proposes a geographical hierarchical data visualization method based on rectangular cartograms. First, a new rectangular cartograms construction algorithm is adopted in this paper, which can effectively preserve relatively accurate orientation and adjacency relationships between geographic regions, while also effectively preserving the statistical data features. Then, a treemap layout algorithm is applied within the rectangular cartogram to further partition the geographic regions, thereby visualizing the hierarchical structure of the data. Through experimental validation using real datasets and usability testing, the results demonstrate that the method presented in this paper excels in geographic distribution representation, hierarchical relationship visualization, and information readability. Compared to traditional thematic map methods, this approach demonstrates significant advantages in terms of information transmission efficiency and shows promising performance in expressive effectiveness, providing strong support for the analysis and decision making of geographical hierarchical data.

Real-time financial monitoring requires operators to interpret large volumes of multidimensional and continuously changing data under conditions that demand rapid, accurate, and repeatable judgment. Conventional tabular interfaces rely heavily on serial scanning and working-memory—intensive comparison, increasing cognitive load and limiting the timely detection of anomalies or the integration of multiple indicators. This study introduces Multiviz, a visualization system designed to support high-density financial analysis through a squarified treemap layout, perceptually separable multivariate encodings, and a rule-based agent layer that applies nonintrusive visual emphasis to contextually relevant changes. A controlled laboratory experiment was conducted with twelve finance-domain participants who completed six analytical tasks using both Multiviz and a baseline tabular interface. Quantitative results showed that Multiviz reduced task completion time from 241.5 ± 42.7 s to 113.1 ± 13.8 s and increased task accuracy from 7.75 ± 0.26% to 9.17 ± 0.27%. Mouse interactions decreased from 52.6 ± 5.4 to 34.9 ± 3.4, indicating reduced interaction overhead. Eye-tracking analysis further revealed shorter fixations (414  vs . 441 ms), reduced scanpath length, and more concentrated attention within semantically coherent regions, suggesting greater perceptual efficiency. Participants reported improved clarity and reduced search effort after brief familiarization with the system. These findings provide empirical evidence that integrating hierarchical layout, multivariate visual encoding, and rule-based perceptual cueing can improve performance and attentional focus in complex, data-rich financial analysis settings.

This paper proposes Treemap-based visualization for supporting cluster analysis of multi-dimensional data. It is important to grasp data distribution in a target dataset for such tasks as machine learning and cluster analysis. When dealing with multi-dimensional data such as statistical data and document datasets, dimensionality reduction algorithms are usually applied to project original data to lower-dimensional space. However, dimensionality reduction tends to lose the characteristics of data in the original space. In particular, the border between different data groups could not be represented correctly in lower-dimensional space. To overcome this problem, the proposed visualization method applies Fuzzy c-Means to target data and visualizes the result on the basis of the highest and the second-highest membership values with Treemap. Visualizing the information about not only the closest clusters but also the second closest ones is expected to be useful for identifying objects around the border between different clusters, as well as for understanding the relationship between different clusters. A prototype interface is implemented, of which the effectiveness is investigated with a user experiment on a news articles dataset. As another kind of text data, a case study of applying it to a word embedding space is also shown.

AbstractTools for intuitive visualization of dynamic datasets are highly demanded for capturing information and revealing potential patterns, especially in understanding the trend of data changes. We propose a novel resolution-independent heuristic algorithm, termed Orthogonal Stable Treemap (OST), to implicitly display dynamic hierarchical data value changes. OST adopts a site-based method as the Voronoi treemap (VT), to preserve the layout stability for diversified data values. Meanwhile, OST partitions the whole canvas with horizontal or vertical lines, instead of the lines with arbitrary orientations in VT. Technical innovations are made in three parts: Initialization of site state to speed up the algorithm and preserve the layout; efficient computation of orthogonal rectangular diagram to partition the empty canvas; self-adaption of site state to quickly reach an equilibrium. The performance of OST is quantitatively evaluated in terms of computation complexity, computation time, convergence rate, visibility, and stability. Moreover, qualitative evaluations (use case and user study) are demonstrated on the dynamic work-in-process dataset in the wafer fab. Evaluation results show that OST combines the advantages of layout stability and tidiness, contributing to easier and faster plot understanding.Graphical Abstract

Food classification is an important part of food safety standards. In this paper, we propose a novel visual comparative analysis method for food classification trees (FCTs) in pesticide maximum residue limit (MRL) standards, called TreeMerge, to lay the foundation for a comprehensive comparison of pesticide MRL standards. First, a union tree is constructed by combining the two FCTs to be compared. Then, sunburst with an embedded chordal graph (SECG) and overlapping circular treemap (OCT), which are two new visualization solutions designed in this paper, are used to show the similarities and differences in a union tree. SECG can express the hierarchical structure and the similarity between corresponding nodes in the union tree at the same time. OCT uses an improved nested Venn diagram (overlapping circle) to express the attribute values in each layer of the union tree and uses a circle-filling layout algorithm based on the testing circle to improve the readability and space utilization of the view. Finally, a visual analysis system for comparing FCT, named FCTvis, is designed and implemented to support the exploration of the structural difference pattern of food classification in the two MRL standards and the quantity or scale of residue limits in various foods. The effectiveness of TreeMerge was verified by case studies on pesticide MRL standards in the Chinese Mainland and Chinese Hong Kong.