Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.
In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used f...
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2014
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oai:doaj.org-article:dd52fd1a30804c99a0e09c02ef3b258c2021-11-25T06:10:04ZJoint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.1932-620310.1371/journal.pone.0100334https://doaj.org/article/dd52fd1a30804c99a0e09c02ef3b258c2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24983991/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template--used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.Saumyadipta PyneSharon X LeeKui WangJonathan IrishPablo TamayoMarc-Danie NazaireTarn DuongShu-Kay NgDavid HaflerRonald LevyGarry P NolanJill MesirovGeoffrey J McLachlanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e100334 (2014) |
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Medicine R Science Q |
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Medicine R Science Q Saumyadipta Pyne Sharon X Lee Kui Wang Jonathan Irish Pablo Tamayo Marc-Danie Nazaire Tarn Duong Shu-Kay Ng David Hafler Ronald Levy Garry P Nolan Jill Mesirov Geoffrey J McLachlan Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. |
| description |
In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template--used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/. |
| format |
article |
| author |
Saumyadipta Pyne Sharon X Lee Kui Wang Jonathan Irish Pablo Tamayo Marc-Danie Nazaire Tarn Duong Shu-Kay Ng David Hafler Ronald Levy Garry P Nolan Jill Mesirov Geoffrey J McLachlan |
| author_facet |
Saumyadipta Pyne Sharon X Lee Kui Wang Jonathan Irish Pablo Tamayo Marc-Danie Nazaire Tarn Duong Shu-Kay Ng David Hafler Ronald Levy Garry P Nolan Jill Mesirov Geoffrey J McLachlan |
| author_sort |
Saumyadipta Pyne |
| title |
Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. |
| title_short |
Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. |
| title_full |
Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. |
| title_fullStr |
Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. |
| title_full_unstemmed |
Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. |
| title_sort |
joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2014 |
| url |
https://doaj.org/article/dd52fd1a30804c99a0e09c02ef3b258c |
| work_keys_str_mv |
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| _version_ |
1718414133562441728 |