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Tailor: Targeting heavy tails in flow cytometry data with fast, interpretable mixture modeling
Author(s) -
Ionita Matei,
Schretzenmair Richard,
Jones Derek,
Moore Jonni,
Wang LiSan,
Rogers Wade
Publication year - 2021
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.24307
Subject(s) - computer science , cluster analysis , mixture model , workflow , benchmark (surveying) , data mining , cytometry , source code , flow cytometry , artificial intelligence , database , biology , geodesy , genetics , geography , operating system
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 .