Transfer Learning for Auto-gating of Flow Cytometry Data

Gyemin Lee, Lloyd Stoolman, Clayton Scott
Proceedings of ICML Workshop on Unsupervised and Transfer Learning, PMLR 27:155-165, 2012.

Abstract

Flow cytometry is a technique for rapidly quantifying physical and chemical properties of large numbers of cells. In clinical applications, flow cytometry data must be manually “gated” to identify cell populations of interest. While several researchers have investigated statistical methods for automating this process, most of them falls under the framework of unsupervised learning and mixture model fitting. We view the problem as one of transfer learning, which can leverage existing datasets previously gated by experts to automatically gate a new flow cytometry dataset while accounting for biological variation. We illustrate our proposed method by automatically gating lymphocytes from peripheral blood samples.

Cite this Paper


BibTeX
@InProceedings{pmlr-v27-lee12a, title = {Transfer Learning for Auto-gating of Flow Cytometry Data}, author = {Lee, Gyemin and Stoolman, Lloyd and Scott, Clayton}, booktitle = {Proceedings of ICML Workshop on Unsupervised and Transfer Learning}, pages = {155--165}, year = {2012}, editor = {Guyon, Isabelle and Dror, Gideon and Lemaire, Vincent and Taylor, Graham and Silver, Daniel}, volume = {27}, series = {Proceedings of Machine Learning Research}, address = {Bellevue, Washington, USA}, month = {02 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v27/lee12a/lee12a.pdf}, url = { http://proceedings.mlr.press/v27/lee12a.html }, abstract = {Flow cytometry is a technique for rapidly quantifying physical and chemical properties of large numbers of cells. In clinical applications, flow cytometry data must be manually “gated” to identify cell populations of interest. While several researchers have investigated statistical methods for automating this process, most of them falls under the framework of unsupervised learning and mixture model fitting. We view the problem as one of transfer learning, which can leverage existing datasets previously gated by experts to automatically gate a new flow cytometry dataset while accounting for biological variation. We illustrate our proposed method by automatically gating lymphocytes from peripheral blood samples.} }
Endnote
%0 Conference Paper %T Transfer Learning for Auto-gating of Flow Cytometry Data %A Gyemin Lee %A Lloyd Stoolman %A Clayton Scott %B Proceedings of ICML Workshop on Unsupervised and Transfer Learning %C Proceedings of Machine Learning Research %D 2012 %E Isabelle Guyon %E Gideon Dror %E Vincent Lemaire %E Graham Taylor %E Daniel Silver %F pmlr-v27-lee12a %I PMLR %P 155--165 %U http://proceedings.mlr.press/v27/lee12a.html %V 27 %X Flow cytometry is a technique for rapidly quantifying physical and chemical properties of large numbers of cells. In clinical applications, flow cytometry data must be manually “gated” to identify cell populations of interest. While several researchers have investigated statistical methods for automating this process, most of them falls under the framework of unsupervised learning and mixture model fitting. We view the problem as one of transfer learning, which can leverage existing datasets previously gated by experts to automatically gate a new flow cytometry dataset while accounting for biological variation. We illustrate our proposed method by automatically gating lymphocytes from peripheral blood samples.
RIS
TY - CPAPER TI - Transfer Learning for Auto-gating of Flow Cytometry Data AU - Gyemin Lee AU - Lloyd Stoolman AU - Clayton Scott BT - Proceedings of ICML Workshop on Unsupervised and Transfer Learning DA - 2012/06/27 ED - Isabelle Guyon ED - Gideon Dror ED - Vincent Lemaire ED - Graham Taylor ED - Daniel Silver ID - pmlr-v27-lee12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 27 SP - 155 EP - 165 L1 - http://proceedings.mlr.press/v27/lee12a/lee12a.pdf UR - http://proceedings.mlr.press/v27/lee12a.html AB - Flow cytometry is a technique for rapidly quantifying physical and chemical properties of large numbers of cells. In clinical applications, flow cytometry data must be manually “gated” to identify cell populations of interest. While several researchers have investigated statistical methods for automating this process, most of them falls under the framework of unsupervised learning and mixture model fitting. We view the problem as one of transfer learning, which can leverage existing datasets previously gated by experts to automatically gate a new flow cytometry dataset while accounting for biological variation. We illustrate our proposed method by automatically gating lymphocytes from peripheral blood samples. ER -
APA
Lee, G., Stoolman, L. & Scott, C.. (2012). Transfer Learning for Auto-gating of Flow Cytometry Data. Proceedings of ICML Workshop on Unsupervised and Transfer Learning, in Proceedings of Machine Learning Research 27:155-165 Available from http://proceedings.mlr.press/v27/lee12a.html .

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