Cytometry A. 2021 Sep 24; doi: 10.1002/cyto.a.24503. Epub 2021 Sep 24.

Automated identification of maximal differential cell populations in flow cytometry data.

Cytometry. Part A : the journal of the International Society for Analytical Cytology

Alice Yue, Cedric Chauve, Maxwell W Libbrecht, Ryan R Brinkman

PMID: 34559446 DOI: 10.1002/cyto.a.24503

Abstract

We introduce a new cell population score called SpecEnr (specific enrichment) and describe a method that discovers robust and accurate candidate biomarkers from flow cytometry data. Our approach identifies a new class of candidate biomarkers we define as driver cell populations, whose abundance is associated with a sample class (e.g., disease), but not as a result of a change in a related population. We show that the driver cell populations we find are also easily interpretable using a lattice-based visualization tool. Our method is implemented in the R package flowGraph, freely available on GitHub (github.com/aya49/flowGraph) and on BioConductor.

© 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.

Keywords: automated analysis; bioinformatics; exploratory data analysis; flow cytometry; statistical analysis

References

1. Azad A, Rajwa B, Pothen A. Immunophenotype discovery, hierarchical organization, and template-based classification of flow cytometry samples. Front Oncol. 2016;6:188. - PubMed
2. Bruggner RV, Bodenmiller B, Dill DL, Tibshirani RJ, Nolan GP. Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci USA. 2014;111(26):E2770-e2777. - PubMed
3. Chen Y, Calvert RD, Azad A, Rajwa B, Fleet J, Ratliff T, et al. Phenotyping immune cells in tumor and healthy tissue using flow cytometry data. Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics; 2018;73-78. - PubMed
4. Tong DL, Ball GR, Pockley AG. gEM/GANN: a multivariate computational strategy for auto-characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high-dimensional flow cytometry data. Cytometry A. 2015;87(7):616-23. - PubMed
5. Zare H, Shooshtari P, Gupta A, Brinkman RR. Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinform. 2010;11(1):403. - PubMed
6. Hu Z, Glicksberg BS, Butte AJ. Robust prediction of clinical outcomes using cytometry data. Bioinformatics. 2018;35(7):1197-203. - PubMed
7. Lin L, Finak G, Ushey K, Seshadri C, Hawn TR, Frahm N, et al. COMPASS identifies T-cell subsets correlated with clinical outcomes. Nat Biotechnol. 2015;33(6):610-6. - PubMed
8. O'Neill K, Jalali A, Aghaeepour N, Hoos H, Brinkman RR. Enhanced flowType/RchyOptimyx: a bioconductor pipeline for discovery in high-dimensional cytometry data. Bioinformatics. 2014;30(9):1329-30. - PubMed
9. Van Gassen S, Vens C, Dhaene T, Lambrecht BN, Saeys Y. FloReMi: flow density survival regression using minimal feature redundancy. Cytometry A. 2016;89(1):22-9. - PubMed
10. Aghaeepour N, Finak G, Hoos H, Mosmann TR, Brinkman R, Gottardo R, et al. Critical assessment of automated flow cytometry data analysis techniques. Nat Methods. 2013;10(3):228-38. - PubMed
11. Cossarizza A, Chang HD, Radbruch A, Akdis M, Andrä I, Annunziato F, et al. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur J Immunol. 2017;47(10):1584-797. - PubMed
12. Lun AT, Richard AC, Marioni JC. Testing for differential abundance in mass cytometry data. Nat Methods. 2017;14(7):707. - PubMed
13. Weber LM, Nowicka M, Soneson C, Robinson MD. Diffcyt: differential discovery in high-dimensional cytometry via high-resolution clustering. Commun Biol. 2019;2(1):1-11. - PubMed
14. Fristedt BE, Gray LF. A modern approach to probability theory. Minneapolis, MN: Springer Science & Business Media; 2013. - PubMed
15. Bernardo JM, Rueda R. Bayesian hypothesis testing: a reference approach. Int Stat Rev. 2002;70(3):351-72. - PubMed
16. Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ. 1995;310(6973):170. - PubMed
17. Aghaeepour N, Ganio EA, Mcilwain D, Tsai AS, Tingle M, Van Gassen S, et al. An immune clock of human pregnancy. Sci Immunol. 2017;2(15):eaan2946. - PubMed
18. Peterson LS, Stelzer IA, Tsai AS, Ghaemi MS, Han X, Ando K, et al. Multiomic immune clockworks of pregnancy. Seminars in immunopathology. 2020;42(4):397-412. https://doi.org/10.1007/s00281-019-00772-1 - PubMed
19. Ware JH, Mosteller F, Delgado F, Donnelly C, Ingelfinger JA. P values. Medical uses of statistics. Vol. 2. John Wiley & Sons, Hoboken, NJ; 1986. p. 181-200. - PubMed
20. Pomerantz A, Rodríguez-Rodríguez S, Demichelis-Gómez R, Barrera-Lumbreras G, Barrales-Benítez OV, Díaz-Huzar MJ, et al. Importance of CD117 in the assignation of a myeloid lineage in acute leukemias. Arch Med Res. 2017;48(2):212-5. - PubMed
21. Wetzler M, McElwain B, Stewart C, Blumenson L, Mortazavi A, Ford L, et al. HLA-DR antigen-negative acute myeloid leukemia. Leukemia. 2003;17(4):707-15. - PubMed
22. Harrington AM, Olteanu H, Kroft SH. A dissection of the CD45/side scatter “blast gate”. Am J Clin Pathol. 2012;137(5):800-4. - PubMed
23. Quek L, Otto GW, Garnett C, Lhermitte L, Karamitros D, Stoilova B, et al. Genetically distinct leukemic stem cells in human CD34-acute myeloid leukemia are arrested at a hemopoietic precursor-like stage. J Exp Med. 2016;213(8):1513-35. - PubMed

Publication Types

• Journal Article

Grant support

• RGPIN-2020-04903/Natural Sciences and Engineering Research Council of Canada
• Simon Fraser University