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

Literature Information ​

DOI10.1038/nbt.4314
PMID30531897
JournalNature biotechnology
Impact Factor41.7
JCR QuartileQ1
Publication Year2018
Times Cited2286
KeywordsDimensionality Reduction, Single-Cell Data, UMAP, Visualization, Cell Clusters
Literature TypeJournal Article
ISSN1087-0156
AuthorsEtienne Becht, Leland McInnes, John Healy, Charles-Antoine Dutertre, Immanuel W H Kwok, Lai Guan Ng, Florent Ginhoux, Evan W Newell

TL;DR ​

This study demonstrates the application of the uniform manifold approximation and projection (UMAP) technique in analyzing high-dimensional biological data from mass cytometry and single-cell RNA sequencing, revealing that UMAP outperforms other dimensionality-reduction tools in terms of speed, reproducibility, and the meaningful organization of cell clusters. These findings underscore UMAP's potential to enhance visualization and interpretation of single-cell datasets, facilitating advances in tissue composition analysis.

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Dimensionality Reduction · Single-Cell Data · UMAP · Visualization · Cell Clusters

Abstract ​

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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Primary Questions Addressed ​

  1. How does UMAP compare to other dimensionality reduction techniques in terms of handling noise in single-cell data?
  2. What are the specific advantages of using UMAP over PCA and t-SNE for visualizing complex biological datasets?
  3. In what ways can the choice of parameters in UMAP influence the visualization outcomes of single-cell data?
  4. How can UMAP be integrated with other data analysis workflows in single-cell transcriptomics?
  5. What are the limitations of UMAP when applied to very high-dimensional datasets, and how can these be mitigated?

Key Findings ​

Key Insights ​

  1. Research Background and Purpose: Advances in single-cell technologies have revolutionized our ability to investigate the complex composition of tissues at an unprecedented resolution. As these technologies generate vast amounts of high-dimensional data, there is a pressing need for effective tools that can reduce dimensionality while preserving the intrinsic structures of the data. This study aims to evaluate the performance of Uniform Manifold Approximation and Projection (UMAP), a state-of-the-art nonlinear dimensionality reduction technique, in the context of biological data analysis, specifically for mass cytometry and single-cell RNA sequencing datasets.

  2. Main Methods and Findings: The researchers applied UMAP to three well-characterized datasets derived from mass cytometry and single-cell RNA sequencing. They compared UMAP's performance against five other existing dimensionality reduction tools. The assessment focused on several criteria, including run times, reproducibility of results, and the clarity of cell cluster organization. The analysis revealed that UMAP outperformed the other tools by providing significantly faster processing times, demonstrating the highest level of reproducibility across different datasets, and offering a more meaningful arrangement of cell clusters. This indicates UMAP's superior capability to retain relevant biological information during dimensionality reduction.

  3. Core Conclusions: The study concludes that UMAP is an efficient and effective tool for the visualization and interpretation of single-cell data. Its ability to facilitate the identification and organization of cell populations makes it a valuable asset in biological research. The findings suggest that UMAP can enhance the understanding of cellular diversity and dynamics in complex tissues, providing researchers with better insights into cellular behavior and interactions.

  4. Research Significance and Impact: The implications of this research are profound for the field of single-cell biology. By demonstrating the advantages of UMAP over traditional dimensionality reduction methods, the study encourages the adoption of UMAP in various biological studies, potentially leading to new discoveries in cellular biology, immunology, and oncology. The ability to visualize high-dimensional data effectively is crucial for interpreting the biological relevance of complex datasets, thereby influencing future research directions, therapeutic strategies, and personalized medicine approaches. UMAP’s application in this context represents a significant advancement in the analytical capabilities available to researchers exploring the intricate landscape of single-cell data.

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Literatures Citing This Work ​

  1. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. - Malgorzata Nowicka;Carsten Krieg;Helena L Crowell;Lukas M Weber;Felix J Hartmann;Silvia Guglietta;Burkhard Becher;Mitchell P Levesque;Mark D Robinson - F1000Research (2017)
  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)
  4. Evaluating reproducibility of AI algorithms in digital pathology with DAPPER. - Andrea Bizzego;Nicole Bussola;Marco Chierici;Valerio Maggio;Margherita Francescatto;Luca Cima;Marco Cristoforetti;Giuseppe Jurman;Cesare Furlanello - PLoS computational biology (2019)
  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)
  8. The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease. - Herbert B Schiller;Daniel T Montoro;Lukas M Simon;Emma L Rawlins;Kerstin B Meyer;Maximilian Strunz;Felipe A Vieira Braga;Wim Timens;Gerard H Koppelman;G R Scott Budinger;Janette K Burgess;Avinash Waghray;Maarten van den Berge;Fabian J Theis;Aviv Regev;Naftali Kaminski;Jayaraj Rajagopal;Sarah A Teichmann;Alexander V Misharin;Martijn C Nawijn - American journal of respiratory cell and molecular biology (2019)
  9. Mutating chikungunya virus non-structural protein produces potent live-attenuated vaccine candidate. - Yi-Hao Chan;Teck-Hui Teo;Age Utt;Jeslin Jl Tan;Siti Naqiah Amrun;Farhana Abu Bakar;Wearn-Xin Yee;Etienne Becht;Cheryl Yi-Pin Lee;Bernett Lee;Ravisankar Rajarethinam;Evan Newell;Andres Merits;Guillaume Carissimo;Fok-Moon Lum;Lisa Fp Ng - EMBO molecular medicine (2019)
  10. Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. - Geng Chen;Baitang Ning;Tieliu Shi - Frontiers in genetics (2019)

... (2276 more literatures)

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