With the advent of the era of big data, the numbers and the dimensions of data are
increasingly becoming larger. It is very critical to reduce dimensions or visualize data
and then uncover the hidden patterns of characteristic or the mechanism underlying data.
Stochastic Neighbor Embedding (SNE) has been developed for data visualization over
the last ten years. Due to its efficiency in the visualization of data, SNE has been applied
to a wide range of fields. We briefly reviewed the SNE algorithm and its variants,
summarizing application of it in visualizing single-cell sequencing data, single
nucleotide polymorphisms, and mass spectrometry imaging data. We also discussed the
strength and the weakness of the SNE, with a special emphasis on how to set parameters
to promote quality of visualization, and finally indicated potential development of SNE
in the coming future.
Keywords: dimensionality reduction, Stochastic Neighbor Embedding,
bioinformatics, T-SNE, data visualization, m-SNE
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