Dimensionality reduction for visualizing single-cell data using UMAP
Dimensionality reduction for visualizing single-cell data using UMAP
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DOI：
10.1038/nbt.4314
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发表时间：
2019-01-01
影响因子：
46.9
通讯作者：
Newell, Evan W.
中科院分区：
文献类型：
--
作者：
Becht, Etienne;McInnes, Leland;Newell, Evan W.
单细胞技术的进步使得能够对组织成分进行高分辨率解剖。有几种降维工具可用于分析单细胞研究中产生的大量参数。最近，一种非线性降维技术，均匀流形近似和投影（UMAP），被开发用于任何类型的高维数据的分析。在这里，我们将其应用于生物数据，使用三个充分表征的质谱细胞术和单细胞RNA测序数据集。将UMAP的性能与其他五种工具进行比较，我们发现UMAP提供了最快的运行时间，最高的可重复性和最有意义的细胞簇组织。这项工作突出了使用UMAP来改善单细胞数据的可视化和解释。
Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.