  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.
