  Dimensional data reduction methods are fundamental to explore and visualize
large data sets. Basic requirements for unsupervised data exploration are
simplicity, flexibility and scalability. However, current methods show complex
parameterizations and strong computational limitations when exploring large
data structures across scales. Here, we focus on the t-SNE algorithm and show
that a simplified parameter setup with a single control parameter, namely the
perplexity, can effectively balance local and global data structure
visualization. We also designed a chunk\&mix protocol to efficiently
parallelize t-SNE and explore data structure across a much wide range of scales
than currently available. Our parallel version of the BH-tSNE, namely pt-SNE,
converges to good global embedding, comparable to state-of-the-art solutions,
though the chunk\&mix protocol adds little noise and decreases the accuracy at
the local scale. Nonetheless, we show that simple post-processing can
efficiently restore local scale visualization, without any loss of precision at
the global scales. We expect the same approach to apply to faster embedding
algorithms other than BH-tSNE, like FIt-SNE or UMAP, thus, extending the
state-of-the-art and leading to more comprehensive data structure visualization
and analysis.
