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Commun Biol. 2019 Jun 20;2:229. doi: 10.1038/s42003-019-0467-6. eCollection 2019.

Automated subset identification and characterization pipeline for multidimensional flow and mass cytometry data clustering and visualization.

Author information

1Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305 USA.
Independent Researcher, Menlo Park, CA 94025 USA.
3Department of Statistics, Stanford University, Stanford, CA 94305 USA.
4Departments of Medicine and Pediatrics, Lowance Center for Human Immunology, Emory Vaccine Center, Children's Healthcare of Atlanta, Emory University, Atlanta, GA 30322 USA.
Contributed equally


When examining datasets of any dimensionality, researchers frequently aim to identify individual subsets (clusters) of objects within the dataset. The ubiquity of multidimensional data has motivated the replacement of user-guided clustering with fully automated clustering. The fully automated methods are designed to make clustering more accurate, standardized and faster. However, the adoption of these methods is still limited by the lack of intuitive visualization and cluster matching methods that would allow users to readily interpret fully automatically generated clusters. To address these issues, we developed a fully automated subset identification and characterization (SIC) pipeline providing robust cluster matching and data visualization tools for high-dimensional flow/mass cytometry (and other) data. This pipeline automatically (and intuitively) generates two-dimensional representations of high-dimensional datasets that are safe from the curse of dimensionality. This new approach allows more robust and reproducible data analysis,+ facilitating the development of new gold standard practices across laboratories and institutions.


Computational platforms and environments; Statistical methods

Conflict of interest statement

Competing interestsThe authors declare no competing financial interests. Stanford University, which employs S.W.M., D.R.P., J.Y., L.A.H., G.W. and D.Y.O. has applied for patents for some of the capabilities included at the AutoGate software which is distributed free to non-commercial users.

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