Henry Han

The paper introduces c-UMAP, a cell-driven information fusion framework for single-cell RNA-seq data that improves clustering accuracy and interpretability over traditional methods.

The paper introduces an explainable t-SNE method for scRNA-seq data that improves clustering performance and interpretability while addressing limitations of traditional t-SNE.

This paper introduces a novel feature selection method, NSVA, to improve RNA-seq differential expression analysis by reducing false positives and enhancing robustness.

This study applies t-SNE to human genetic data, showing it can reveal population stratification at different scales, similar to PCA but more robust to outliers.