Automated high-dimensional flow cytometric data analysis

Pyne, Saumyadipta, Hu, Xinli, Wang, Kui, Rossin, Elizabeth, Lin, Tsung-I, Maier, Lisa, Baecher-Allan, Clare, McLachlan, Geoffrey, Tamayo, Pablo, Hafler, David, De Jager, Philip and Mesirov, Jill (2010). Automated high-dimensional flow cytometric data analysis. In: Bonnie Berger, Research in Computational Molecular Biology: 14th Annual International Conference, RECOMB 2010: Proceedings. 14th Annual International Conference on Research in Computational Molecular Biology, Lisbon, Portugal, (577-577). 25-28 April 2010. doi:10.1007/978-3-642-12683-3_41


Author Pyne, Saumyadipta
Hu, Xinli
Wang, Kui
Rossin, Elizabeth
Lin, Tsung-I
Maier, Lisa
Baecher-Allan, Clare
McLachlan, Geoffrey
Tamayo, Pablo
Hafler, David
De Jager, Philip
Mesirov, Jill
Title of paper Automated high-dimensional flow cytometric data analysis
Conference name 14th Annual International Conference on Research in Computational Molecular Biology
Conference location Lisbon, Portugal
Conference dates 25-28 April 2010
Proceedings title Research in Computational Molecular Biology: 14th Annual International Conference, RECOMB 2010: Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2010
Sub-type Published abstract
DOI 10.1007/978-3-642-12683-3_41
ISBN 9783642126826
3642126820
ISSN 0302-9743
1611-3349
Editor Bonnie Berger
Volume 6044
Start page 577
End page 577
Total pages 1
Language eng
Abstract/Summary Flow cytometry is widely used for single cell interrogation of surface and intracellular protein expression by measuring fluorescence intensity of fluorophore-conjugated reagents. We focus on the recently developed procedure of Pyne et al. (2009, Proceedings of the National Academy of Sciences USA 106, 8519-8524) for automated high- dimensional flow cytometric analysis called FLAME (FLow analysis with Automated Multivariate Estimation). It introduced novel finite mixture models of heavy-tailed and asymmetric distributions to identify and model cell populations in a flow cytometric sample. This approach robustly addresses the complexities of flow data without the need for transformation or projection to lower dimensions. It also addresses the critical task of matching cell populations across samples that enables downstream analysis. It thus facilitates application of flow cytometry to new biological and clinical problems. To facilitate pipelining with standard bioinformatic applications such as high-dimensional visualization, subject classification or outcome prediction, FLAME has been incorporated with the GenePattern package of the Broad Institute. Thereby analysis of flow data can be approached similarly as other genomic platforms. We also consider some new work that proposes a rigorous and robust solution to the registration problem by a multi-level approach that allows us to model and register cell populations simultaneously across a cohort of high-dimensional flow samples. This new approach is called JCM (Joint Clustering and Matching). It enables direct and rigorous comparisons across different time points or phenotypes in a complex biological study as well as for classification of new patient samples in a more clinical setting.
Q-Index Code EX
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Lecture Notes in Bioinformatics: Subseries of Lecture Notes in Computer Science

 
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