LA2SNE: A novel stochastic neighbor embedding approach for microbiome data visualization

Visualization of large-scale data is the first step to acquire preliminary insight into complex biological data. In recent years, many statistical visualization methods have been designed to support data visualization. Stochastic Neighbor Embedding (SNE) is one of these efficient approaches, which uses the probabilistic distance to model differences among data points within the data space. SNE and its variants (e.g. t-SNE) have demonstrated superiority over other methods in exploring complex data. By using these methods, however, similar data points tend to group together, which prevents the identification of subtle differences. A good visualization method should not only present clear data structure, but distinguish subtle differences. In this paper, we propose a novel extension of SNE. The approach has three innovations: (1) we replaced the Gaussian distribution in SNE with a Laplacian distribution on both high dimensional space and low dimensional space. The Laplace distribution has wider tails than the Gaussian distribution, and thus it can be used to overcome the over-crowding problem noted in SNE and its variants. (2) We used a symmetric modification of Kullback-Leibler divergence measure as the objective function which provides more flexibility to the model. (3) We add a graph Laplacian regularization terms to the objective function which have an advantage to preserve the manifold structure among data points. Experiments on simulation data and human microbiome data indicate that it has better visualization performance than other methods in distinguishing crowding data points.