A novel data fusion method for the effective analysis of multiple panels of flow cytometry data-Reference-Cited by-同舟云学术

A novel data fusion method for the effective analysis of multiple panels of flow cytometry data

Published:2019-05-01 Issue:1 Volume:9 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Tinnevelt Gerjen H.^ORCID,van Staveren Selma,Wouters Kristiaan,Wijnands Erwin,Verboven Kenneth,Folcarelli Rita,Koenderman Leo^ORCID,Buydens Lutgarde M. C.,Jansen Jeroen J.

Funder

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://www.nature.com/articles/s41598-019-43166-x.pdf

Reference20 articles.

1. Newell, E. W. & Davis, M. M. Beyond model antigens: high-dimensional methods for the analysis of antigen-specific T cells. Nature biotechnology 32, 149–157 (2014).

2. Wang, D. & Bodovitz, S. Single cell analysis: the new frontier in ‘omics’. Trends in biotechnology 28, 281–290 (2010).

3. Ge, Y. & Sealfon, S. C. flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding. Bioinformatics 28, 2052–2058 (2012).

4. Biehl, M. Admire LVQ—Adaptive Distance Measures in Relevance Learning Vector Quantization. KI-Künstliche Intelligenz 26, 391–395 (2012).

5. Aghaeepour, N. et al. Critical assessment of automated flow cytometry data analysis techniques. Nature Methods 10, 228–238 (2013).

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