Brief Report
No access
Published Online: 2 May 2019

Penalized Supervised Star Plots: Example Application in Influenza-Specific CD4+ T Cells

This article has been corrected.
VIEW CORRECTION
Publication: Viral Immunology
Volume 32, Issue Number 2

Abstract

An immune cell's phenotype expresses through its high-dimensional marker signature. Cluster analyses of data from high-throughput mass and flow cytometry marker panels permit discovery of previously undescribed immune cell phenotypes. Impactful reporting of new phenotypes demands low-dimensional visualization tools that preserve with integrity phenotypes' original high-dimensional structure. For this purpose, we introduce penalized supervised star plots. As designed and as we demonstrate, penalized supervised star plots are two-dimensional projections that tend to preserve separation of clusters as well as information on the relative contributions of various markers in differentiating phenotypes. The new method is robust to markers that do not differentiate phenotypes at all, as shown in a challenge data set. Results include comparison with other popular procedures. Penalized supervised star plots incorporate cross-validation to permit portability of estimated optimal projections to new samples. Supervised star plots are further illustrated with a featured influenza-specific T cell data set as well as a peripheral blood mononuclear cell phenotyping data set.

Get full access to this article

View all available purchase options and get full access to this article.

References

1. Abraham Y. Radviz: A R Package for Multi-Dimensional Data Visualization. The Comprehensive R Archive Network (CRAN), 2016. Available at https://cran.cnr.berkeley.edu/ (Last accessed January 23, 2019).
2. Abraham Y. 2016. Visualizing multivariate data with Radviz. Retrieved from: https://cran.r-project.org/web/packages/Radviz/vignettes/single_cell_projections.html (Accessed on December 5, 2018).
3. Amir ED, Davis KL, Tadmor MD, et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 2013;31:545–552.
4. Baker M. 1,500 scientists lift the lid on reproducibility. Nature 2016;533:452–454.
5. Bruggner RV, Bodenmiller B, Dill DL, et al. Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci USA 2014;111:E2770-7.
6. Chiu D, Talhouk A, and Liu J. diceR: Diverse cluster ensemble in R. Comprehensive R Archive Network (CRAN), 2018. (Accessed on July 11, 2017).
7. Duchon J. Splines minimizing rotation-invariant semi-norms in Sobolev spaces. In: Schempp W, Zeller K, eds. Constructive Theory of Functions of Several Variables. Berlin, Heidelberg: Springer-Verlag (Dold A, Eckmann B, editors. Lecture Notes in Mathematics), 1977.
8. Hansen BE. The risk of James–Stein and lasso shrinkage. Econom Rev 2016;35:1456–1470.
9. Hastie T, Tibshirani R, and Friedman JH. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer, 2009:84–95.
10. Hastie T, Tibshirani R, and Friedman JH. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer, 2009:501–528.
11. Hoffman P, Grinstein G, Marx K, et al. DNA visual and analytic data mining. In: Proceedings Visualization'97 (Cat No 97CB36155). Phoenix, AZ: IEEE, 1997:437–441.
12. Hoffman P, Grinstein G, and Pinkney D. Dimensional anchors: a graphic primitive for multidimensional multivariate information visualizations. In: Proceedings of the 1999 Workshop on New Paradigms in Information Visualization and Manipulation in Conjunction with the Eighth ACM International Conference on Information and Knowledge Management—NPIVM'99. New York, NY: ACM Press, 1999:9–16.
13. Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 2008;24:1403–1405.
14. Jombart T, Devillard S, and Balloux F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 2010;11:94.
15. Kandogan E. Visualizing multi-dimensional clusters, trends, and outliers using star coordinates. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD'01. New York, NY: ACM Press, 2001:107–116.
16. Kim J-H. Estimating classification error rate: repeated cross-validation, repeated hold-out and bootstrap. Comput Stat Data Anal 2009;53:3735–3745.
17. Kohonen T. The self-organizing map. Proc IEEE 1990;78:1464–1480.
18. Lemon J. plotrix: a package in the red light district of R. R-News 2006;6:8–12.
19. Lin D, Gupta S, and Maecker HT. Intracellular cytokine staining on PBMCs using CyTOF™ mass cytometry. Bio Protoc 2015;5:e1370.
20. van der Maaten L, and Hinton G. Visualizing data using t-SNE. J Machine Learn Res 2009;9:2579–2605.
21. Maurya A. JPEN: Covariance and Inverse Covariance Matrix Estimation Using Joint Penalty. Comprehensive R Archive Network (CRAN), 2015. Available at https://cran.cnr.berkeley.edu/ (Last accessed January 23, 2019).
22. Novomestky F. matrixcalc: Collection of Functions for Matrix Calculations. Comprehensive R Archive Network (CRAN), 2012. Available at https://cran.cnr.berkeley.edu/ (Last accessed January 23, 2019).
23. Orloci L. An agglomerative method for classification of plant communities. J Ecol 1967;55:193–206.
24. Pearson K. L III. On lines and planes of closest fit to systems of points in space. Philos Mag Ser 1901;6 2:559–572.
25. Peterson LE, and Ford CE. Random matrix theory and covariance matrix filtering for cancer gene expression. In: Peterson LE, Masulli F, Russo G, eds. Computational Intelligence Methods for Bioinformatics and Biostatistics. Berlin, Heidelberg: Springer Berlin Heidelberg (Hutchison D, Kanade T, Kittler J, et al., eds. Lecture Notes in Computer Science), 2013:173–184.
26. Qiu W, and Joe H. clusterGeneration: Random Cluster Generation (with Specified Degree of Separation). Comprehensive R Archive Network (CRAN), 2015. Available at https://cran.cnr.berkeley.edu/ (Last accessed January 23, 2019).
27. Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 1987;20:53–65.
28. Schuetzenmeister A, and Dufey F. VCA: Variance Component Analysis. Comprehensive R Archive Network (CRAN), 2018. Available at https://cran.cnr.berkeley.edu/ (Last accessed January 23, 2019).
29. Sharko J, Grinstein G, and Marx KA. Vectorized Radviz and its application to multiple cluster datasets. IEEE Trans Vis Comput Graph 2008;14:1444–1451.
30. Stanford Medicine ME/CFS Initiative. R Code for a High-Dimensional Cell-Phenotype Visualization Tool. http://med.stanford.edu/chronicfatiguesyndrome/research/Rcode-Visualization-Tool.html
31. Suni MA, Ghanekar SA, Houck DW, et al. CD4(+)CD8(dim) T lymphocytes exhibit enhanced cytokine expression, proliferation and cytotoxic activity in response to HCMV and HIV-1 antigens. Eur J Immunol 2001;31:2512–2520.
32. Tibshirani R, Saunders M, Rosset S, et al. Sparsity and smoothness via the fused lasso. J R Stat Soc Ser B (Stat Method) 2005;67:91–108.
33. Van Long T, and Linsen L. Visualizing high density clusters in multidimensional data using optimized star coordinates. Comput Stat 2011;26:655–678.

Information & Authors

Information

Published In

cover image Viral Immunology
Viral Immunology
Volume 32Issue Number 2March 2019
Pages: 102 - 109
PubMed: 30698511

History

Published online: 2 May 2019
Published in print: March 2019
Published ahead of print: 30 January 2019

Permissions

Request permissions for this article.

Topics

    Authors

    Affiliations

    Tyson H. Holmes [email protected]
    Department of Medicine, Stanford University School of Medicine, Stanford, California.
    Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, California.
    Priyanka B. Subrahmanyam
    Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, California.
    Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California.
    Weiqi Wang
    Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, California.
    Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California.
    Holden T. Maecker
    Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, California.
    Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California.

    Notes

    Address correspondence to: Dr. Tyson H. Holmes, Stanford University School of Medicine, Lane Building, Room L134, 300 Pasteur Drive, Stanford, CA 94305 [email protected]

    Authors' Contributions

    T.H.H. and H.T.M. conceived the study. T.H.H. designed the method, wrote R code, conducted analyses, and wrote the article. H.T.M. critically reviewed the article. P.B.S. processed samples, generated CyTOF data, gated, and analyzed them, performed viSNE analysis, and helped write the article. W.W. performed PSS, PCA, RadViz, and DAPC analyses. W.W. also contributed extensions to the original R script for PSS. All authors have read and approved the final version of the article.

    Author Disclosure Statement

    No competing financial interests exist.

    Ethical Approval

    Samples were collected from human participants under informed consent, through the Institutional Review Board (IRB)-approved protocol at Stanford University (IRB-16390).

    Metrics & Citations

    Metrics

    Citations

    Export citation

    Select the format you want to export the citations of this publication.

    View Options

    Get Access

    Access content

    To read the fulltext, please use one of the options below to sign in or purchase access.

    Society Access

    If you are a member of a society that has access to this content please log in via your society website and then return to this publication.

    Restore your content access

    Enter your email address to restore your content access:

    Note: This functionality works only for purchases done as a guest. If you already have an account, log in to access the content to which you are entitled.

    View options

    PDF/EPUB

    View PDF/ePub

    Full Text

    View Full Text

    Media

    Figures

    Other

    Tables

    Share

    Share

    Copy the content Link

    Share on social media

    Back to Top