langevitour:用scRNA-Seq数据演示的高维平滑交互漫游

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS R Journal Pub Date : 2023-11-01 DOI:10.32614/rj-2023-046
Paul Harrison
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引用次数: 0

摘要

langevitour显示高维数据集的交互式动画2D投影。朗格万动力学用于生成平滑的投影路径。预测最初是随机探索的。可以激活“向导”来查找信息投影,或者可以手动定位变量。在找到一个特别感兴趣的投影后,连续的小运动提供了一个静态散点图所没有的视觉信息通道。langevitour是用Javascript实现的,允许高帧率和响应式交互,可以直接从R环境中使用,也可以嵌入到使用R生成的HTML文档中。单细胞rna测序(scRNA-Seq)数据用于演示小部件。langevitour的线性投影比常用的非线性降维(如UMAP)提供了更少失真的数据视图。
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langevitour: Smooth Interactive Touring of High Dimensions, Demonstrated with scRNA-Seq Data
langevitour displays interactive animated 2D projections of high-dimensional datasets. Langevin Dynamics is used to produce a smooth path of projections. Projections are initially explored at random. A "guide" can be activated to look for an informative projection, or variables can be manually positioned. After a projection of particular interest has been found, continuing small motions provide a channel of visual information not present in a static scatter plot. langevitour is implemented in Javascript, allowing for a high frame rate and responsive interaction, and can be used directly from the R environment or embedded in HTML documents produced using R. Single cell RNA-sequencing (scRNA-Seq) data is used to demonstrate the widget. langevitour's linear projections provide a less distorted view of this data than commonly used non-linear dimensionality reductions such as UMAP.
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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