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Dimensionality reduction for visualizing single-cell data using UMAP. ​

文献信息 ​

DOI10.1038/nbt.4314
PMID30531897
期刊Nature biotechnology
影响因子41.7
JCR 分区Q1
发表年份2018
被引次数2286
关键词降维, 单细胞数据, UMAP, 生物数据, 细胞簇
文献类型Journal Article
ISSN1087-0156
作者Etienne Becht, Leland McInnes, John Healy, Charles-Antoine Dutertre, Immanuel W H Kwok, Lai Guan Ng, Florent Ginhoux, Evan W Newell

一句话小结 ​

本研究探讨了均匀流形近似与投影（UMAP）在单细胞数据分析中的应用，发现其在处理高维生物数据时，相较于其他降维工具展现出更快的运行速度、更高的可重复性和更显著的细胞聚类效果。这一发现强调了UMAP在提升单细胞数据可视化和解释能力方面的重要性。

在麦伴科研 (maltsci.com) 搜索更多文献 ​

降维 · 单细胞数据 · UMAP · 生物数据 · 细胞簇

摘要 ​

单细胞技术的进步使得对组织成分的高分辨率剖析成为可能。为了分析单细胞研究中生成的大量参数，现有多种降维工具可供使用。最近，开发了一种非线性降维技术——均匀流形近似与投影（UMAP），用于分析任何类型的高维数据。在这里，我们将其应用于生物数据，使用了三个经过良好表征的质量细胞术和单细胞RNA测序数据集。通过与其他五种工具比较UMAP的性能，我们发现UMAP提供了最快的运行时间、最高的可重复性以及最有意义的细胞簇组织。这项工作突出了UMAP在改善单细胞数据可视化和解释方面的应用。

英文摘要 ​

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.

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主要研究问题 ​

  1. 在应用UMAP进行单细胞数据可视化时，如何选择合适的参数以优化结果？
  2. UMAP与其他降维工具（如t-SNE或PCA）相比，在处理不同类型的单细胞数据时有什么优势和劣势？
  3. 如何评估UMAP在不同生物学背景下的表现，例如在肿瘤微环境与正常组织的单细胞分析中？
  4. 在使用UMAP进行单细胞数据分析时，如何处理噪声和缺失数据，以确保结果的可靠性？
  5. 除了可视化，UMAP在单细胞数据分析中还有哪些潜在的应用领域或功能？

核心洞察 ​

1. 研究背景和目的 ​

随着单细胞技术的不断进步，研究人员能够以高分辨率剖析组织成分。这些技术生成了大量的参数数据，给数据分析带来了挑战。因此，发展高效的降维工具以便于可视化和理解单细胞数据显得尤为重要。本文的目的是评估一种新开发的非线性降维技术——统一流形近似和投影（UMAP），并与现有的五种降维工具进行比较，以探讨其在生物数据分析中的应用。

2. 主要方法和发现 ​

研究团队将UMAP应用于三组经过充分表征的质量细胞术和单细胞RNA测序数据集。通过与其他五种降维工具的性能比较，发现UMAP具备以下优势：在运行时间上最为迅速，重现性最高，并且能够更有意义地组织细胞簇。UMAP通过保留数据的局部结构，提升了对单细胞数据的可视化效果，有助于研究人员更清晰地识别和理解细胞群体之间的关系。

3. 核心结论 ​

UMAP在单细胞数据分析领域展现出了显著的优势，尤其是在可视化与细胞聚类的组织方面。其快速的计算速度和高重现性使其成为研究人员分析复杂生物数据的优选工具。本文的研究表明，UMAP不仅能有效地处理高维数据，还能够提供更直观且有意义的生物学解释。

4. 研究意义和影响 ​

本研究的重要性在于为单细胞数据的分析提供了一种高效且可靠的工具，推动了生物信息学和系统生物学的发展。UMAP的应用将有助于研究人员在生物医学领域中更准确地识别细胞类型、解析细胞间的异质性，进而促进对疾病机制的理解和新疗法的开发。同时，该研究也为后续在高维数据分析领域的深入研究奠定了基础，提升了数据分析方法的科学性和实用性。

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  2. Dissecting Cellular Heterogeneity Using Single-Cell RNA Sequencing. - Yoon Ha Choi;Jong Kyoung Kim - Molecules and cells (2019)
  3. Single cell immune profiling in transplantation research. - Lauren E Higdon;Steven Schaffert;Purvesh Khatri;Jonathan S Maltzman - American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons (2019)
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  5. The accessible chromatin landscape of the murine hippocampus at single-cell resolution. - John R Sinnamon;Kristof A Torkenczy;Michael W Linhoff;Sarah A Vitak;Ryan M Mulqueen;Hannah A Pliner;Cole Trapnell;Frank J Steemers;Gail Mandel;Andrew C Adey - Genome research (2019)
  6. Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics. - Qiwen Hu;Casey S Greene - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (2019)
  7. Hypoxia tolerance in the Norrin-deficient retina and the chronically hypoxic brain studied at single-cell resolution. - Jacob S Heng;Amir Rattner;Genevieve L Stein-O'Brien;Briana L Winer;Bryan W Jones;Hilary J Vernon;Loyal A Goff;Jeremy Nathans - Proceedings of the National Academy of Sciences of the United States of America (2019)
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