期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 111, 期 26, 页码 E2770-E2777出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1408792111
关键词
informatics; biomarker discovery
资金
- National Library of Medicine Training Grant [T15 LM007033]
- Swiss National Science Foundation
- European Molecular Biology Organization
- Marie Curie International Outgoing Fellowship
- National Cancer Institute Grant [U54CA149145]
- National Science Foundation [DMS-9971405]
- National Institutes of Health (NIH) [N01-HV-28183]
- NIH [U54CA149145, UL1RR025744, 0158 G KB065, 1R01CA130826, 5U54CA143907, HHSN272200700038C, N01-HV-00242, 41000411217, 5 24927, P01 CA034233-22A1, PN2EY018228, RFA CA 09-009, RFA CA 09 011, U19 AI057229]
- California Institute for Regenerative Medicine [DR1-01477, RB2-01592]
- European Commission [HEALTH. 2010.1.2-1]
- U.S. Food and Drug Administration [HHSF223201210194C: BAA-12-00118]
- U.S. Department of Defense [W81XWH-12-1-0591 OCRP-TIA NWC]
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.
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