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Analyzing large-scale Single-Cell RNA-Seq data Using Coreset

ieee-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第6期21卷 1784-1793页

作者： Usman, Khalid Wan, Fangping Zhao, Dan Peng, Jian Zeng, Jianyang Tsinghua Univ Inst Interdisciplinary Informat Sci Comp Sci Beijing 100084 Peoples R China Univ Penn Penn Inst Computat Sci Philadelphia PA 19104 USA Univ Illinois Dept Comp Sci Champaign IL 61820 USA Westlake Univ Sch Engn Sch Life Sci Hangzhou 310024 Peoples R China

The recent boom in single-cell sequencing technologies provides valuable insights into the transcriptomes of individual cells. Through single-cell data analyses, a number of biological discoveries, such as novel cell types, developmental cell lineage trajectories, and gene regulatory networks, have been uncovered. However, the massive and increasingly accumulated single-cell datasets have also posed a seriously computational and analytical challenge for researchers. To address this issue, one typically applies dimensionality reduction approaches to reduce the large-scale datasets. However, these approaches are generally computationally infeasible for tall matrices. In addition, the downstream data analysis tasks such as clustering still take a large time complexity even on the dimension-reduced datasets. We present single-cell Coreset (scCoreset), a data summarization framework that extracts a small weighted subset of cells from a huge sparse single-cell RNA-seq data to facilitate the downstream data analysis tasks. Single-cell data analyses run on the extracted subset yield similar results to those derived from the original uncompressed data. Tests on various single-cell datasets show that scCoreset outperforms the existing data summarization approaches for common downstream tasks such as visualization and clustering. We believe that scCoreset can serve as a useful plug-in tool to improve the efficiency of current single-cell RNA-seq data analyses.

关键词： data analysis Cells (biology) Task analysis Computer science Biology data mining Clustering algorithms Coreset large-scale datasets ScCoreset single - cell RNA-seq data

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Precise Embodied data Selection with Haptic Feedback while Retaining Room-scale Visualisation Context

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2025年第1期31卷 602-612页

作者： Dai, Shaozhang Li, Yi Ens, Barrett Besancon, Lonni Dwyer, Tim Monash Univ Clayton Vic 3800 Australia Univ British Columbia Vancouver BC Canada Linkoping Univ Linkoping Sweden

Room-scale immersive data visualisations provide viewers a wide-scale overview of a large dataset, but to interact precisely with individual data points they typically have to navigate to change their point of view. In traditional screen-based visualisations, focus-and-context techniques allow visualisation users to keep a full dataset in view while making detailed selections. Such techniques have been studied extensively on desktop to allow precise selection within large data sets, but they have not been explored in immersive 3D modalities. In this paper we develop a novel immersive focus-and-context technique based on a "magic portal" metaphor adapted specifically for data visualisation scenarios. An extendable-hand interaction technique is used to place a portal close to the region of interest. The other end of the portal then opens comfortably within the user's physical reach such that they can reach through to precisely select individual data points. Through a controlled study with 12 participants, we find strong evidence that portals reduce overshoots in selection and overall hand trajectory length, reducing arm and shoulder fatigue compared to ranged interaction without the portal. The portals also enable us to use a robot arm to provide haptic feedback for data within the limited volume of the portal region. In a second study with another 12 participants we found that haptics provided a positive experience (qualitative feedback) but did not significantly reduce fatigue. We demonstrate applications for portal-based selection through two use-case scenarios.

关键词： immersive analytics focus-and-context remote interaction portal haptic feedback

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Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第1期30卷 1227-1237页

作者： Floricel, Carla Wentzel, Andrew Mohamed, Abdallah Fuller, C. David Canahuate, Guadalupe Marai, G. Elisabeta Univ Illinois Chicago IL 60607 USA Univ Iowa Iowa City IA USA Univ Texas MD Anderson Canc Ctr Austin TX USA

Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research.

关键词： visualization Cancer Computational modeling data visualization data mining Analytical models data models Temporal data Life Sciences Mixed Initiative Human-Machine analysis data Clustering and Aggregation

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PassViz: An Interactive Visualisation System for Analysing Leaked Passwords

PassViz: An Interactive Visualisation System for Analysing L...

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20th ieee symposium on visualization for Cyber Security (VizSec)

作者： Parker, Sam Yuan, Haiyue Li, Shujun Univ Kent Inst Cyber Secur Soc ICSS Canterbury Kent England Univ Kent Sch Comp Canterbury Kent England

ISBN: (纸本)9798350317930

Passwords remain the most widely used form of user authentication, despite advancements in other methods. However, their limitations, such as susceptibility to attacks, especially weak passwords defined by human users, are well-documented. The existence of weak human-defined passwords has led to repeated password leaks from websites, many of which are of large scale. While such password leaks are unfortunate security incidents, they provide security researchers and practitioners with good opportunities to learn valuable insights from such leaked passwords, in order to identify ways to improve password policies and other security controls on passwords. Researchers have proposed different data visualisation techniques to help analyse leaked passwords. However, many approaches rely solely on frequency analysis, with limited exploration of distance-based graphs. This paper reports PassViz, a novel method that combines the edit distance with the t-SNE (t-distributed stochastic neighbour embedding) dimensionality reduction algorithm [19] for visualising and analysing leaked passwords in a 2-D space. We implemented PassViz as an easy-to-use command-line tool for visualising large-scale password databases, and also as a graphical user interface (GUI) to support interactive visual analytics of small password databases. Using the "000webhost" leaked database as an example, we show how PassViz can be used to visually analyse different aspects of leaked passwords and to facilitate the discovery of previously unknown password patterns. Overall, our approach empowers researchers and practitioners to gain valuable insights and improve password security through effective data visualisation and analysis.

关键词： Human-centered computing visualization visualization techniques Treemaps Human-centered computing visualization visualization design and evaluation methods

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A large-scale Sensitivity analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2025年第1期31卷 305-315页

作者： Atzberger, Daniel Cech, Tim Scheibe, Willy Doelner, Jurgen Behrisch, Michael Schreck, Tobias Univ Potsdam Digital Engn Fac Hasso Plattner Inst Potsdam Germany Univ Utrecht Utrecht Netherlands Graz Univ Technol Graz Austria

The semantic similarity between documents of a text corpus can be visualized using map-like metaphors based on two-dimensional scatterplot layouts. These layouts result from a dimensionality reduction on the document-term matrix or a representation within a latent embedding, including topic models. Thereby, the resulting layout depends on the input data and hyperparameters of the dimensionality reduction and is therefore affected by changes in them. Furthermore, the resulting layout is affected by changes in the input data and hyperparameters of the dimensionality reduction. However, such changes to the layout require additional cognitive efforts from the user. In this work, we present a sensitivity study that analyzes the stability of these layouts concerning (1) changes in the text corpora, (2) changes in the hyperparameter, and (3) randomness in the initialization. Our approach has two stages: data measurement and data analysis. First, we derived layouts for the combination of three text corpora and six text embeddings and a grid-search-inspired hyperparameter selection of the dimensionality reductions. Afterward, we quantified the similarity of the layouts through ten metrics, concerning local and global structures and class separation. Second, we analyzed the resulting 42 817 tabular data points in a descriptive statistical analysis. From this, we derived guidelines for informed decisions on the layout algorithm and highlight specific hyperparameter settings. We provide our implementation as a Git repository at (sic) hpicgs/Topic-Models-and-Dimensionality-Reduction-Sensitivity-Study and results as Zenodo archive at DOI:10.5281/zenodo.12772898.

关键词： Text spatializations text embeddings topic modeling dimensionality reductions stability benchmarking

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Multivariate Probabilistic Range Queries for Scalable Interactive 3D visualization

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 646-656页

作者： Ageeli, Amani Jaspe-Villanueva, Alberto Sicat, Ronell Mannuss, Florian Rautek, Peter Hadwiger, Markus Visual Computing Center Thuwal23955-6900 Saudi Arabia Saudi Aramco Dhahran Saudi Arabia

large-scale scientific data, such as weather and climate simulations, often comprise a large number of attributes for each data sample, like temperature, pressure, humidity, and many more. Interactive visualization and analysis require filtering according to any desired combination of attributes, in particular logical AND operations, which is challenging for large data and many attributes. Many general data structures for this problem are built for and scale with a fixed number of attributes, and scalability of joint queries with arbitrary attribute subsets remains a significant problem. We propose a flexible probabilistic framework for multivariate range queries that decouples all attribute dimensions via projection, allowing any subset of attributes to be queried with full efficiency. Moreover, our approach is output-sensitive, mainly scaling with the cardinality of the query result rather than with the input data size. This is particularly important for joint attribute queries, where the query output is usually much smaller than the whole data set. Additionally, our approach can split query evaluation between user interaction and rendering, achieving much better scalability for interactive visualization than the previous state of the art. Furthermore, even when a multi-resolution strategy is used for visualization, queries are jointly evaluated at the finest data granularity, because our framework does not limit query accuracy to a fixed spatial subdivision. © 2022 ieee.

关键词： data visualization

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Narrative In Situ Visual analysis for large-scale Ocean Eddy Evolution

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2022年第3期42卷 65-73页

作者： Han, Xiaoyang Yu, Xiaomin Li, Guan Liu, Jun Zhao, Ying Shan, Guihua Univ Chinese Acad Sci Beijing 101408 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410017 Peoples R China

The rapid development of high-performance computing systems has led to a rapid increase in the speed of flow field simulation calculations. However, large-scale simulation output data lead to storage bottlenecks and inefficient data analysis. In this work, we used in situ visualization to process the simulation analysis of large-scale flow fields. Combined with narrative visual analysis, we designed a large-scale ocean flow field eddy evolution analysis system based on in situ visualization. Our system can generate high-precision eddy streamline structures in real time and supports eddy statistical analysis and tracking analysis at different ocean regional scales. Through the case data analysis of ocean simulation, we demonstrated the efficiency and effectiveness of the system.

关键词： Oceans visualization data visualization data models Computational modeling Analytical models Real-time systems

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Visual Analytics of Performance of Quantum Computing Systems and Circuit Optimization

Visual Analytics of Performance of Quantum Computing Systems...

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ieee-Computer-Society Annual symposium on VLSI (ISVLSI)

作者： Chae, Junghoon Steed, Chad A. Humble, Travis S. Oak Ridge Natl Lab Oak Ridge TN 37830 USA

ISBN: (纸本)9798350354119

Driven by potential exponential speedups in business, security, and scientific scenarios, interest in quantum computing is surging. This interest feeds the development of quantum computing hardware, but several challenges arise in optimizing application performance for hardware metrics (e.g., qubit coherence and gate fidelity). In this work, we describe a visual analytics approach for analyzing the performance properties of quantum devices and quantum circuit optimization. Our approach allows users to explore spatial and temporal patterns in quantum device performance data and it computes similarities and variances in key performance metrics. Detailed analysis of the error properties characterizing individual qubits is also supported. We also describe a method for visualizing the optimization of quantum circuits. The resulting visualization tool allows researchers to design more efficient quantum algorithms and applications by increasing the interpretability of quantum computations.

关键词： quantum computing visual analytics circuit optimization data visualization

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A Visual Comparison of Silent Error Propagation

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第7期30卷 3268-3282页

作者： Li, Zhimin Menon, Harshitha Mohror, Kathryn Liu, Shusen Guo, Luanzheng Bremer, Peer-Timo Pascucci, Valerio Univ Utah Sci Comp & Imaging Inst Salt Lake City UT 84112 USA Lawrence Livermore Natl Lab Livermore CA 94550 USA Pacific Northwest Natl Lab Richland WA 99354 USA

High-performance computing (HPC) systems play a critical role in facilitating scientific discoveries. Their scale and complexity (e.g., the number of computational units and software stack) continue to grow as new systems are expected to process increasingly more data and reduce computing time. However, with more processing elements, the probability that these systems will experience a random bit-flip error that corrupts a program's output also increases, which is often recognized as silent data corruption. Analyzing the resiliency of HPC applications in extreme-scale computing to silent data corruption is crucial but difficult. An HPC application often contains a large number of computation units that need to be tested, and error propagation caused by error corruption is complex and difficult to interpret. To accommodate this challenge, we propose an interactive visualization system that helps HPC researchers understand the resiliency of HPC applications and compare their error propagation. Our system models an application's error propagation to study a program's resiliency by constructing and visualizing its fault tolerance boundary. Coordinating with multiple interactive designs, our system enables domain experts to efficiently explore the complicated spatial and temporal correlation between error propagations. At the end, the system integrated a nonmonotonic error propagation analysis with an adjustable graph propagation visualization to help domain experts examine the details of error propagation and answer such questions as why an error is mitigated or amplified by program execution.

关键词： Fault tolerance boundary information visualization graph visualization error propagation silent data corruption

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Distributed Volumetric Neural Representation for in situ visualization and analysis 12

Distributed Volumetric Neural Representation for in situ Vis...

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12th ieee symposium on large-scale data analysis and visualization (LDAV)

作者： Wu, Qi Insley, Joseph A. Mateevitsi, Victor A. Rizzi, Silvio Ma, Kwan-Liu Argonne Natl Lab Argonne IL 60439 USA Univ Calif Davis CA 95616 USA Northern Illinois Univ De Kalb IL 60115 USA

ISBN: (数字)9781665491563

ISBN: (纸本)9781665491563

Volumetric grids have recently been used by many recent works for representing complex scenes implicitly. A volumetric neural representation can be several orders of magnitude smaller in size while still preserving most of high-frequency details. However, most volumes used in large-scale in situ visualization and analysis are partitioned and generated directly in parallel. Therefore, a compatible technique to create volumetric neural representations for these situations is much needed. In this project, we explore the possibility of constructing and optimizing such a representation for large-scale distributed volumes. We present our preliminary results in this poster. We also outline our plans to integrate our techniques with existing in situ visualization and analysis pipelines.

关键词： data analysis Pipelines data visualization

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