Nonlinear data visualization methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), summarize the complex transcriptomic landscape of single cells in two dimensions or three dimensions, 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. Here we present den-SNE and densMAP, which are 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 sequencing 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 Caenorhabditis elegans. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains.

非线性数据可视化方法，如t-distributed stochastic neighbor embedding (t-SNE) 和 uniform manifold approximation and projection (UMAP)，在二维或三维上总结了单个细胞的复杂转录组景观，但它们忽略了原始空间中数据点的局部密度，通常导致误导性的可视化，其中人口稠密的细胞子集被赋予比数据集中转录多样性所保证的更多的视觉空间。在这里，我们介绍了 den-SNE 和 densMAP，它们分别是基于 t-SNE 和 UMAP 的密度保持可视化工具，并证明了它们能够准确地将有关转录组变异性的信息整合到单细胞 RNA 测序数据的视觉解释中。应用于最近发布的数据集，秀丽隐杆线虫。我们的方法很容易适用于可视化其他科学领域的高维数据。