Journal of Data Science, Statistics, and Visualisation https://jdssv.org/index.php/jdssv <p>The journal welcomes contributions to practical aspects of data science, statistics and visualisation, and in particular those which are linking and integrating these subject areas. Papers should thus be oriented towards a very wide scientific audience, and can cover topics such as machine learning and statistical learning, the visualisation and verbalization of data, big data infrastructures and analytics, interactive learning, advanced computing, and other important themes. JDSSV is an open access journal that charges no author fees.</p> The Internation Association for Statistical Computing en-US Journal of Data Science, Statistics, and Visualisation 2773-0689 Casting multiple shadows: interactive data visualisation with tours and embeddings https://jdssv.org/index.php/jdssv/article/view/21 <p>Non-linear dimensionality reduction (NLDR) methods such as t-distributed stochastic neighbour embedding (t-SNE) are ubiquitous in the natural sciences, however, the appropriate use of these methods is difficult because of their complex parameterisations; analysts must make trade-offs in order to identify structure in the visualisation of an NLDR technique. We present visual diagnostics for the pragmatic usage of NLDR methods by combining them with a technique called the tour. A tour is a sequence of interpolated linear projections of multivariate data onto a lower dimensional space. The sequence is displayed as a dynamic visualisation, allowing a user to see the shadows the high-dimensional data casts in a lower dimensional view. By linking the tour to an NLDR view, we can preserve global structure and through user interactions like linked brushing observe where the NLDR view may be misleading. We display several case studies from both simulations and single cell transcriptomics, that shows our approach is useful for cluster orientation tasks. The implementation of our framework is available as an R package called <strong>liminal</strong> available at <a href="https://github.com/sa-lee/liminal">https://github.com/sa-lee/liminal</a>.</p> Stuart Lee Ursula Laa Dianne Cook Copyright (c) 2022 Journal of Data Science, Statistics, and Visualisation 2022-05-30 2022-05-30 2 3 10.52933/jdssv.v2i3.21