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.
|Original language||English (US)|
|Number of pages||13|
|Journal||Cytometry Part A|
|State||Published - Nov 6 2019|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors acknowledge the SciLifeLab National Mass Cytometry Facility services in Stockholm for performing mass cytometry; particularly its members doctors. Tadepally Lakshmikanth, Yang Chen, Jaromir Mikes, and Petter Brodin for the many helpful discussions and feedback regarding sample preparation protocols and CyTOF data generation settings. In addition, the authors would like to thank the anonymous referees for their insightful comments and key suggestions during the review of this manuscript. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. 617393; CAUSALPATH – Next Generation Causal Analysis project. Funding for open access charge: ERC.