Assessing Single-Cell Transcriptomic Variability through Density-Preserving Data Visualization

Nonlinear data-visualization methods, such as t-SNE and UMAP, summarize the complex transcriptomic landscape of single cells in 2D or 3D, but they neglect the local density of data points in the original space, often resulting in misleading visualizations where densely populated subsets of cells are given more visual space than warranted by their transcriptional diversity in the dataset. We present den-SNE and densMAP, density-preserving visualization tools based on t-SNE and UMAP, respectively, and demonstrate their ability to accurately incorporate information about transcriptomic variability into the visual interpretation of single-cell RNA-seq data. Applied to recently published datasets, our methods reveal significant changes in transcriptomic variability in a range of biological processes, including heterogeneity in transcriptomic variability of immune cells in blood and tumor, human immune cell specialization, and the developmental trajectory of C. elegans. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains.