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Non-linear dimensionality reduction (NLDR) methods such as t-distributedstochastic neighbour embedding (t-SNE) are ubiquitous in the natural sciences,however, the appropriate use of these methods is difficult because of theircomplex parameterisations; analysts must make trade-offs in order to identifystructure in the visualisation of an NLDR technique. We present visualdiagnostics for the pragmatic usage of NLDR methods by combining them with atechnique called the tour. A tour is a sequence of interpolated linearprojections of multivariate data onto a lower dimensional space. The sequenceis displayed as a dynamic visualisation, allowing a user to see the shadows thehigh-dimensional data casts in a lower dimensional view. By linking the tour toan NLDR view, we can preserve global structure and through user interactionslike linked brushing observe where the NLDR view may be misleading. We displayseveral case studies from both simulations and single cell transcriptomics,that shows our approach is useful for cluster orientation tasks.

Casting Multiple Shadows: High-Dimensional Interactive Data Visualisation with Tours and Embeddings