The analytic results also showed that t-SNE with Aitchison distance led to improvement of classification accuracy in both datasets. The proposed approach was applied to two disease-associated microbiome datasets, achieving better classification performance compared with the classifiers built in the original high-dimensional space. Using the low-dimensional features as input, three commonly used machine learning algorithms, logistic regression (LR), support vector machine (SVM), and decision tree (DT) were considered for classification tasks in this study. To classify a new sample, its low-dimensional features were obtained as the weighted mean vector of its nearest neighbors in the training set. The Aitchison distance was employed to modify the conditional probabilities in t-SNE to account for the compositionality of microbiome data. This study presented a t-SNE based classification approach for compositional microbiome data, which enabled us to build classifiers and classify new samples in the reduced dimensional space produced by t-SNE. In recent years, it has also received increasing attention for classification and regression analysis.

4Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin, Tianjin, ChinaĪs a data-driven dimensionality reduction and visualization tool, t-distributed stochastic neighborhood embedding (t-SNE) has been successfully applied to a variety of fields.

3Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.

2School of Mathematical Sciences, Nankai University, Tianjin, China.

1School of Statistics and Data Science, Nankai University, Tianjin, China.Xueli Xu 1, Zhongming Xie 2, Zhenyu Yang 1, Dongfang Li 3* and Ximing Xu 1,4*

Tsne new method map classification