ICML 2011 workshop on unsupervised
and transfer learning
Transfer Learning for Auto-gating of Flow
Lee, Lloyd Stoolman, and Clayton Scott
University of Michigan, Ann Arbor.
cytometry is a technique for rapidly quantifying physical and chemical
properties of large numbers of cells. In clinical applications, ow
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 tting. We view
the problem as one of transfer learning, which can leverage existing
datasets previously gated by experts to automatically gate a new ow
cytometry dataset while accounting for biological variation. We
illustrate our proposed method by automatically gating lymphocytes from
peripheral blood samples.