Tailor: Targeting heavy tails in flow cytometry data with fast, interpretable mixture modeling

Automated clustering workflows are increasingly used for the analysis of high parameter flow cytometry data. This trend calls for algorithms which are able to quickly process tens of millions of data points, to compare results across subjects or time points, and to provide easily actionable interpre...

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Veröffentlicht in:Cytometry. Part A Jg. 99; H. 2; S. 133 - 144
Hauptverfasser: Ionita, Matei, Schretzenmair, Richard, Jones, Derek, Moore, Jonni, Wang, Li‐San, Rogers, Wade
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 01.02.2021
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Schlagworte:
Algorithms
Annotations
Automation
Clustering
clustering algorithms
computation and informatics
Data points
Flow cytometry
high‐parameter flow cytometry
mixture modeling
Phenotypes
Probabilistic models
Source code
mixture modeling
computation and informatics
high-parameter flow cytometry
clustering algorithms
ISSN:1552-4922, 1552-4930, 1552-4930
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Zusammenfassung:Automated clustering workflows are increasingly used for the analysis of high parameter flow cytometry data. This trend calls for algorithms which are able to quickly process tens of millions of data points, to compare results across subjects or time points, and to provide easily actionable interpretations of the results. To this end, we created Tailor, a model‐based clustering algorithm specialized for flow cytometry data. Our approach leverages a phenotype‐aware binning scheme to provide a coarse model of the data, which is then refined using a multivariate Gaussian mixture model. We benchmark Tailor using a simulation study and two flow cytometry data sets, and show that the results are robust to moderate departures from normality and inter‐sample variation. Moreover, Tailor provides automated, non‐overlapping annotations of its clusters, which facilitates interpretation of results and downstream analysis. Tailor is released as an R package, and the source code is publicly available at www.github.com/matei-ionita/Tailor.
Bibliographie:Funding information
This article is based on a conference presentation at CYTO Virtual 2020.
University of Pennsylvania Comprehensive Cancer Center, Grant/Award Number: NCI P30 CA016520; NIH/NIA, Grant/Award Numbers: U24‐AG041689, U54‐AG052427
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ISSN:1552-4922
1552-4930
1552-4930
DOI:10.1002/cyto.a.24307