Papoutsoglou, G;
Lagani, V;
Schmidt, A;
Tsirlis, K;
Cabrero, D-G;
Tegner, J;
Tsamardinos, I;
(2019)
Challenges in the Multivariate Analysis of Mass Cytometry Data: The Effect of Randomization.
Cytometry Part A
, 95
(11)
pp. 1178-1190.
10.1002/cyto.a.23908.
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Abstract
Cytometry by time-of-flight (CyTOF) has emerged as a high-throughput single cell technology able to provide large samples of protein readouts. Already, there exists a large pool of advanced high-dimensional analysis algorithms that explore the observed heterogeneous distributions making intriguing biological inferences. A fact largely overlooked by these methods, however, is the effect of the established data preprocessing pipeline to the distributions of the measured quantities. In this article, we focus on randomization, a transformation used for improving data visualization, which can negatively affect multivariate data analysis methods such as dimensionality reduction, clustering, and network reconstruction algorithms. Our results indicate that randomization should be used only for visualization purposes, but not in conjunction with high-dimensional analytical tools.
| Type: | Article |
|---|---|
| Title: | Challenges in the Multivariate Analysis of Mass Cytometry Data: The Effect of Randomization |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/cyto.a.23908 |
| Publisher version: | http://dx.doi.org/10.1002/cyto.a.23908 |
| Language: | English |
| Additional information: | © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
| Keywords: | mass cytometry, pre-processing, high dimensional data analysis, randomization, dimensionality reduction, clustering, network reconstruction, method development, FLOW-CYTOMETRY, TIME |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10128413 |
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