  Recent advances in dimensionality reduction have achieved more accurate
lower-dimensional embeddings of high-dimensional data. In addition to
visualisation purposes, these embeddings can be used for downstream processing,
including batch effect normalisation, clustering, community detection or
trajectory inference. We use the notion of structure preservation at both local
and global levels to create a deep learning model, based on a variational
autoencoder (VAE) and the stochastic quartet loss from the SQuadMDS algorithm.
Our encoder model, called GroupEnc, uses a 'group loss' function to create
embeddings with less global structure distortion than VAEs do, while keeping
the model parametric and the architecture flexible. We validate our approach
using publicly available biological single-cell transcriptomic datasets,
employing RNX curves for evaluation.
