Automated high-dimensional flow cytometric data analysis Export

@article{Pyne_et_al09, abstract = {10.1073/pnas.0903028106 Flow cytometric analysis allows rapid single cell interrogation of surface and intracellular determinants by measuring fluorescence intensity of fluorophore-conjugated reagents. The availability of new platforms, allowing detection of increasing numbers of cell surface markers, has challenged the traditional technique of identifying cell populations by manual gating and resulted in a growing need for the development of automated, high-dimensional analytical methods. We present a direct multivariate finite mixture modeling approach, using skew and heavy-tailed distributions, to address the complexities of flow cytometric analysis and to deal with high-dimensional cytometric data without the need for projection or transformation. We demonstrate its ability to detect rare populations, to model robustly in the presence of outliers and skew, and to perform the critical task of matching cell populations across samples that enables downstream analysis. This advance will facilitate the application of flow cytometry to new, complex biological and clinical problems.}, author = {Pyne, Saumyadipta and Hu, Xinli and Wang, Kui and Rossin, Elizabeth and Lin, Tsung-I and Maier, Lisa M. and Baecher-Allan, Clare and Mclachlan, Geoffrey J. and Tamayo, Pablo and Hafler, David A. and De Jager, Philip L. and Mesirov, Jill P.}, citeulike-article-id = {4894671}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0903028106}, citeulike-linkout-1 = {http://www.pnas.org/content/0903028106v1//.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/0903028106v1//.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19443687}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19443687}, day = {26}, doi = {10.1073/pnas.0903028106}, journal = {Proceedings of the National Academy of Sciences}, keywords = {facs}, month = {May}, number = {21}, pages = {8519--8524}, posted-at = {2009-07-23 16:02:25}, priority = {2}, title = {Automated high-dimensional flow cytometric data analysis}, url = {http://dx.doi.org/10.1073/pnas.0903028106}, volume = {106}, year = {2009} }

TY - JOUR ID - Pyne_et_al09 L3 - citeulike-article-id:4894671 N2 - 10.1073/pnas.0903028106 Flow cytometric analysis allows rapid single cell interrogation of surface and intracellular determinants by measuring fluorescence intensity of fluorophore-conjugated reagents. The availability of new platforms, allowing detection of increasing numbers of cell surface markers, has challenged the traditional technique of identifying cell populations by manual gating and resulted in a growing need for the development of automated, high-dimensional analytical methods. We present a direct multivariate finite mixture modeling approach, using skew and heavy-tailed distributions, to address the complexities of flow cytometric analysis and to deal with high-dimensional cytometric data without the need for projection or transformation. We demonstrate its ability to detect rare populations, to model robustly in the presence of outliers and skew, and to perform the critical task of matching cell populations across samples that enables downstream analysis. This advance will facilitate the application of flow cytometry to new, complex biological and clinical problems. IS - 21 JF - Proceedings of the National Academy of Sciences EP - 8524 TI - Automated high-dimensional flow cytometric data analysis VL - 106 SP - 8519 KW - facs AU - Pyne, Saumyadipta AU - Hu, Xinli AU - Wang, Kui AU - Rossin, Elizabeth AU - Lin, Tsung-I AU - Maier, Lisa AU - Baecher-Allan, Clare AU - Mclachlan, Geoffrey AU - Tamayo, Pablo AU - Hafler, David AU - De Jager, Philip AU - Mesirov, Jill PY - 2009/05/26/ UR - http://dx.doi.org/10.1073/pnas.0903028106 ER -