A PCA-Enhanced t-SNE Plot and Its Application in Biological and Medical Research

In this paper, we apply a two-step dimension reduction method, PCA-t-SNE to a real gene expression dataset as case study. It turns out that the PCA-t-SNE can signigicantly improve the visualization and cluster separation of high-dimensional biological data. While t-SNE alone often fails to reveal clear cluster structures in complex datasets, our approach first applies Principal Component Analysis (PCA) to reduce noise and dimensionality, followed by t-SNE to condense the data into a two-dimensional space and then apply the k-means to clustering the two-dimensional data. We demonstrate that PCA-t-SNE produces more distinct and interpretable clusters compared to the standard t-SNE. Statistical validation via a projected F-test for MANOVA confirms that clusters derived from PCA-t-SNE exhibit significantly greater mean separation, with lower p-values, underscoring the enhanced discriminative power of the method. The proposed PCA-t-SNE plot proves particularly effective for nonlinear data where conventional t-SNE performs poorly, offering a robust visualization tool and supporting the utility of sequential dimension reduction in exploratory data analysis for biological and medical research. Purpose: This study aims to evaluate the effect from a combination of the classical PCA and the modern t-SNE technique for dimension reduction in clustering of high-dimensional gene expression data from the aspects of both visualization and MANOVA. Methods: This paper presents a combined approach to dimension reduction for high-dimensional gene expression data. The effect of the visual approach is re-enhanced by the classical MANOVA method for large sample sizes (n > p) and the newly developed MANOVA method for small sample sizes (n < p). Results: The proposed PCA t-SNE approach significantly improves the pure t-SNE approach for the selected gene expression dataset in the sense of clearer classification of the data from both visual observation and statistical significance tests. This provides a pre-processing of high-dimensional gene expression data before implementing the nonlinear dimension reduction, making the t-SNE approach more effective. Contribution: We carry out a successful application of the two-step dimension reduction method PCA-t-SNE to a real gene expression dataset as case study. The idea of the PCA-t-SNE approach to visualizing high-dimensional gene expression data, enhanced by the projection-type MANOVA tests, opens a new way to discrimination of complex high-dimensional with statistical significance in the case of high dimension with a small sample size (n < p). It enhances the clustering of those nonlinear-type of data where the pure t-SNE almost fails to discriminate the clusters, and provides insight into a two-step dimension reduction approach.