Stochastic Neighbor Embedding Algorithm and its Application in Molecular Biological Data

Title:Stochastic Neighbor Embedding Algorithm and its Application in Molecular Biological Data

Volume: 15 Issue: 9

Author(s): Pan Wang, Guiyang Zhang, You Li, Ammar Oad and Guohua Huang*

Affiliation:

• Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang 422000,China

Keywords: Dimensionality reduction, stochastic neighbor embedding, bioinformatics, T-SNE, data visualization, m-SNE.

Abstract: 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 characteristics 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.

Cite this article as:

Wang Pan , Zhang Guiyang , Li You , Oad Ammar and Huang Guohua *, Stochastic Neighbor Embedding Algorithm and its Application in Molecular Biological Data, Current Bioinformatics 2020; 15 (9) . https://dx.doi.org/10.2174/1574893615999200414093636