Applied Mathematics Program, Yale University, New Haven, CT, USA.
Department of Mathematics, Yale University, New Haven, CT, USA.
Nat Methods. 2019 Mar;16(3):243-245. doi: 10.1038/s41592-018-0308-4. Epub 2019 Feb 11.
t-distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large datasets. We dramatically accelerate t-SNE, obviating the need for data downsampling, and hence allowing visualization of rare cell populations. Furthermore, we implement a heatmap-style visualization for scRNA-seq based on one-dimensional t-SNE for simultaneously visualizing the expression patterns of thousands of genes. Software is available at https://github.com/KlugerLab/FIt-SNE and https://github.com/KlugerLab/t-SNE-Heatmaps .
t 分布随机邻居嵌入 (t-SNE) 广泛用于可视化单细胞 RNA 测序 (scRNA-seq) 数据，但它在大规模数据集上的扩展效果不佳。我们显著加速了 t-SNE，避免了数据下采样的需要，从而可以可视化稀有细胞群体。此外，我们基于一维 t-SNE 实现了 scRNA-seq 的热图样式可视化，以便同时可视化数千个基因的表达模式。软件可在 https://github.com/KlugerLab/FIt-SNE 和 https://github.com/KlugerLab/t-SNE-Heatmaps 获得。
