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Proc Natl Acad Sci U S A. 2014 Jul 1;111(26):E2770-7. doi: 10.1073/pnas.1408792111. Epub 2014 Jun 16.

Automated identification of stratifying signatures in cellular subpopulations.

Author information

Biomedical Informatics Training Program, Stanford University Medical School, Stanford, CA 94305;Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, and.
Institute of Molecular Life Sciences, University of Zurich, CH-8057 Zurich, Switzerland.
Departments of Computer Science.
Health Research and Policy, andStatistics, Stanford University, Stanford, CA 94305; and
Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, and


Elucidation and examination of cellular subpopulations that display condition-specific behavior can play a critical contributory role in understanding disease mechanism, as well as provide a focal point for development of diagnostic criteria linking such a mechanism to clinical prognosis. Despite recent advancements in single-cell measurement technologies, the identification of relevant cell subsets through manual efforts remains standard practice. As new technologies such as mass cytometry increase the parameterization of single-cell measurements, the scalability and subjectivity inherent in manual analyses slows both analysis and progress. We therefore developed Citrus (cluster identification, characterization, and regression), a data-driven approach for the identification of stratifying subpopulations in multidimensional cytometry datasets. The methodology of Citrus is demonstrated through the identification of known and unexpected pathway responses in a dataset of stimulated peripheral blood mononuclear cells measured by mass cytometry. Additionally, the performance of Citrus is compared with that of existing methods through the analysis of several publicly available datasets. As the complexity of flow cytometry datasets continues to increase, methods such as Citrus will be needed to aid investigators in the performance of unbiased--and potentially more thorough--correlation-based mining and inspection of cell subsets nested within high-dimensional datasets.


biomarker discovery; informatics

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