Clustering of cell populations in flow cytometry data using a combination of Gaussian mixtures
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- 作者：Michael Reitera ; rei@caa.tuwien.ac.at" class="auth_mail" title="E-mail the corresponding author ; michael.reiter@stanna.at" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Paolo Rotab ; rota@caa.tuwien.ac.at" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Florian Kleberb ; kleber@caa.tuwien.ac.at" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Markus Diemb ; diem@caa.tuwien.ac.at" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Stefanie Groeneveld-Krentzc ; stefanie.krentz@charite.de" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Michael Dworzaka ; d ; dworzak@stanna.at" class="auth_mail" title="E-mail the corresponding author ; michael.dworzak@ccri.at" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Gaussian mixture model ; Convex non-negative matrix factorization ; L2 distance ; Clustering ; Flow cytometry ; Acute lymphblastic leukemia
- 刊名：Pattern Recognition
- 出版年：2016
- 出版时间：December 2016
- 年：2016
- 卷：60
- 期：Complete
- 页码：1029-1040
- 全文大小：8392 K
文摘
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A density model based on the interpolation of Gaussian mixture models is presented.
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Non-negative matrix factorization is used to compress the model.
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Affine registration of Gaussian mixture models using the L2 distance is performed.
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The applicability of the method is demonstrated on flow cytometry data.