| Highest density difference region estimation with application to flow cytometric data. | |
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MedLine Citation:
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PMID: 19588456 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Motivated by the needs of scientists using flow cytometry, we study the problem of estimating the region where two multivariate samples differ in density. We call this problem highest density difference region estimation and recognise it as a two-sample analogue of highest density region or excess set estimation. Flow cytometry samples are typically in the order of 10,000 and 100,000 and with dimension ranging from about 3 to 20. The industry standard for the problem being studied is called Frequency Difference Gating, due to Roederer and Hardy (2001). After couching the problem in a formal statistical framework we devise an alternative estimator that draws upon recent statistical developments such as patient rule induction methods. Improved performance is illustrated in simulations. While motivated by flow cytometry, the methodology is suitable for general multivariate random samples where density difference regions are of interest. |
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Authors:
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Tarn Duong; Inge Koch; M P Wand |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Biometrical journal. Biometrische Zeitschrift Volume: 51 ISSN: 1521-4036 ISO Abbreviation: Biom J Publication Date: 2009 Jun |
Date Detail:
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Created Date: 2009-07-15 Completed Date: 2009-09-30 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 7708048 Medline TA: Biom J Country: Germany |
Other Details:
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Languages: eng Pagination: 504-21 Citation Subset: IM |
Affiliation:
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Institut Pasteur, Groupe Imagerie et Modélisation; CNRS, URA 2582, F-75015 Paris, France. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Cell Count
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methods* Cells, Cultured / cytology*, physiology* Data Interpretation, Statistical Flow Cytometry / methods* Image Interpretation, Computer-Assisted / methods* Statistical Distributions |
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