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|Title:||Highest density difference region estimation with application to flow cytometric data|
|Citation:||Biometrical Journal: journal of mathematical methods in biosciences, 2009; 51(3):504-521|
|Tarn Duong, Inge Koch, and M. P. Wand|
|Abstract:||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.|
|Keywords:||Flow cytometry; Frequency difference gating; Highest density region; Multivariate density estimation; Patient rule induction method|
|Rights:||Copyright © 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim|
|Appears in Collections:||Aurora harvest 2|
Mathematical Sciences publications
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