Skip to main content
SpringerLink
Log in

Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery

  • Protocol
  • First Online:
Mass Cytometry

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1989))

Abstract

CITRUS is a supervised machine learning algorithm designed to analyze single cell data, identify cell populations, and identify changes in the frequencies or functional marker expression patterns of those populations that are significantly associated with an outcome. The algorithm is a black box that includes steps to cluster cell populations, characterize these populations, and identify the significant characteristics. This chapter describes how to optimize the use of CITRUS by combining it with upstream and downstream data analysis and visualization tools.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kvistborg P, Gouttefangeas C, Aghaeepour N et al (2015) Thinking outside the gate: single-cell assessments in multiple dimensions. Immunity 42(4):591–592. https://doi.org/10.1016/j.immuni.2015.04.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Newell EW, Cheng Y (2016) Mass cytometry: blessed with the curse of dimensionality. Nat Immunol 17(8):890–895. https://doi.org/10.1038/ni.3485

    Article  CAS  PubMed  Google Scholar 

  3. Bruggner RV, Bodenmiller B, Dill DL et al (2014) Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci U S A 111(26):E2770–E2777. https://doi.org/10.1073/pnas.1408792111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Fraietta JA, Lacey SF, Orlando EJ et al (2018) Determinants of response and resistance to CD19 chimeric antigen receptor (CAR) T cell therapy of chronic lymphocytic leukemia. Nat Med 24(5):563–571. https://doi.org/10.1038/s41591-018-0010-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Pelissier Vatter FA, Schapiro D, Chang H et al (2018) High-dimensional phenotyping identifies age-emergent cells in human mammary epithelia. Cell Rep 23(4):1205–1219. https://doi.org/10.1016/j.celrep.2018.03.114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Subrahmanyam PB, Dong Z, Gusenleitner D et al (2018) Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients. J Immunother Cancer 6(1):18. https://doi.org/10.1186/s40425-018-0328-8

    Article  PubMed  PubMed Central  Google Scholar 

  7. Ben-Shaanan TL, Azulay-Debby H, Dubovik T et al (2016) Activation of the reward system boosts innate and adaptive immunity. Nat Med 22(8):940–944. https://doi.org/10.1038/nm.4133

    Article  CAS  PubMed  Google Scholar 

  8. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 57(1):289–300

    Google Scholar 

  9. van der Maaten LJP, Hinton GE (2008) Visualizing high-dimensional data using t-SNE. J Mach Learn Res 9:2579–2605

    Google Scholar 

  10. Amir el AD, Davis KL, Tadmor MD et al (2013) viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 31(6):545–552. https://doi.org/10.1038/nbt.2594

    Article  CAS  Google Scholar 

  11. Knapp D, Kannan N, Pellacani D et al (2017) Mass cytometric analysis reveals viable activated caspase-3(+) luminal progenitors in the normal adult human mammary gland. Cell Rep 21(4):1116–1126. https://doi.org/10.1016/j.celrep.2017.09.096

    Article  CAS  PubMed  Google Scholar 

  12. Hahne F, Khodabakhshi AH, Bashashati A et al (2010) Per-channel basis normalization methods for flow cytometry data. Cytometry A 77(2):121–131. https://doi.org/10.1002/cyto.a.20823

    Article  PubMed  PubMed Central  Google Scholar 

  13. Cytobank (2018) How to configure and run a viSNE analysis. https://support.cytobank.org/hc/en-us/articles/206439707-How-to-Configure-and-Run-a-viSNE-Analysis. Accessed 27 July 2018

  14. Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98(9):5116–5121. https://doi.org/10.1073/pnas.091062498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tibshirani R, Hastie T, Narasimhan B et al (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A 99(10):6567–6572. https://doi.org/10.1073/pnas.082099299

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B 58:267–288

    Google Scholar 

  17. Finak G, Jiang W, Krouse K et al (2014) High-throughput flow cytometry data normalization for clinical trials. Cytometry A 85(3):277–286. https://doi.org/10.1002/cyto.a.22433

    Article  PubMed  Google Scholar 

  18. Van Gassen S, Gaudiliere B, Dhaene T, et al (2017) A cross-sample cell-type specific normalization algorithm for clinical mass cytometry datasets. Paper presented at the 32nd congress of the International Society for Advancement of cytometry, Boston, MA

    Google Scholar 

  19. Hoy T (2006) Rare-event detection. In: Wulff S (ed) Guide to flow cytometry. Dako, Carpinteria, CA, pp 55–58

    Google Scholar 

  20. Baniyash M (2004) TCR zeta-chain downregulation: curtailing an excessive inflammatory immune response. Nat Rev Immunol 4(9):675–687. https://doi.org/10.1038/nri1434

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cytobank, Inc, Santa Clara, CA, USA

    Hannah G. Polikowsky & Katherine A. Drake

Authors
  1. Hannah G. Polikowsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katherine A. Drake
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Discipline of Pathology, The University of Sydney, Camperdown, NSW, Australia

    Helen M. McGuire

  2. Sydney Cytometry Facility, The University of Sydney and Centenary Institute, Camperdown, NSW, Australia

    Thomas M. Ashhurst

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Polikowsky, H.G., Drake, K.A. (2019). Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery. In: McGuire, H., Ashhurst, T. (eds) Mass Cytometry. Methods in Molecular Biology, vol 1989. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9454-0_20

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9454-0_20

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9453-3

  • Online ISBN: 978-1-4939-9454-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation