Abstract
Identification of cellular populations is the first step in analyzing cytometry data. To identify both abundant and outlying rare cellular populations a density-based preprocessing of data to equalize representations of populations is needed. Density-based downsampling keeps representative points in the cellular space while discarding irrelevant ones. We propose a fast and fully deterministic algorithm for density calculation, based on space partitioning, tree representation and an iterative approach to downsampling utilizing fast calculation of density. We compared our algorithm with SPADE, the most used approach in this area, achieving comparable results in a significantly shorter runtime.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amir, E.A.D., et al.: viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31(6), 545–552 (2013)
Bruggner, R.V., et al.: Automated identification of stratifying signatures in cellular subpopulations. Proc. Natl. Acad. Sci. 111(26), E2770–E2777 (2014)
Fruchterman, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. Softw. Pract. Exper. 21(11), 1129–1164 (1991)
Levine, J., et al.: Data-driven phenotypic dissection of aml reveals progenitor-like cells that correlate with prognosis. Cell 162(1), 184–197 (2015)
Li, H., et al.: Gating mass cytometry data by deep learning. Bioinformatics 33(21), 3423–3430 (2017)
Qiu, P.: Toward deterministic and semiautomated SPADE analysis. Cytometry. Part: J. Int. Soc. Anal. Cytol. 91, 281–289 (2017)
Qiu, P., et al.: Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29(10), 886–891 (2011)
Saeys, Y., et al.: Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat. Rev. Immunol. 16(7), 449–462 (2016)
Spitzer, M., Nolan, G.: Mass cytometry: single cells, many features. Cell 165(4), 780–791 (2016)
Tanner, S.D., et al.: An introduction to mass cytometry: fundamentals and applications. Cancer Immunol. Immunother. 62(5), 955–965 (2013)
Verschoor, C.P., et al.: An introduction to automated flow cytometry gating tools and their implementation. Front. Immunol. 6, 380 (2015)
Weber, L.M., Robinson, M.D.: Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytom. Part A 89(12), 1084–1096 (2016)
Zare, H., et al.: Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinform. 11(1), 403 (2010)
Acknowledgments
This work was partially supported by the Scientific Grant Agency of The Slovak Republic, Grant No. VG 1/0458/18 and APVV-16-0484.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Nemček, M., Jarábek, T., Lucká, M. (2020). Parallel Density-Based Downsampling of Cytometry Data. In: Fdez-Riverola, F., Rocha, M., Mohamad, M., Zaki, N., Castellanos-Garzón, J. (eds) Practical Applications of Computational Biology and Bioinformatics, 13th International Conference. PACBB 2019. Advances in Intelligent Systems and Computing, vol 1005 . Springer, Cham. https://doi.org/10.1007/978-3-030-23873-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-23873-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23872-8
Online ISBN: 978-3-030-23873-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)