title: Scalable clustering algorithms for continuous environmental flow cytometry.
abstract: BACKGROUND
  Recent technological innovations in flow cytometry now allow oceanographers to collect high-frequency flow cytometry data from particles in aquatic environments on a scale far surpassing conventional flow cytometers. The SeaFlow cytometer continuously profiles microbial phytoplankton populations across thousands of kilometers of the surface ocean. The data streams produced by instruments such as SeaFlow challenge the traditional sample-by-sample approach in cytometric analysis and highlight the need for scalable clustering algorithms to extract population information from these large-scale, high-frequency flow cytometers.
  
  
  RESULTS
  We explore how available algorithms commonly used for medical applications perform at classification of such a large-scale, environmental flow cytometry data. We apply large-scale Gaussian mixture models to massive datasets using Hadoop. This approach outperforms current state-of-the-art cytometry classification algorithms in accuracy and can be coupled with manual or automatic partitioning of data into homogeneous sections for further classification gains. We propose the Gaussian mixture model with partitioning approach for classification of large-scale, high-frequency flow cytometry data.
  
  
  METHODS
  Source code available for download at https://github.com/jhyrkas/seaflow_cluster, implemented in Java for use with Hadoop.
  
  
  BACKGROUND
  hyrkas@cs.washington.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science and Engineering.
authors.forename: Jeremy
authors.initials: J
authors.lastname: Hyrkas
authors.affiliation: School of Oceanography and.
authors.forename: Sophie
authors.initials: S
authors.lastname: Clayton
authors.affiliation: School of Oceanography and.
authors.forename: Francois
authors.initials: F
authors.lastname: Ribalet
authors.affiliation: Department of Computer Science and Engineering, eScience Institute, University of Washington, Seattle, WA 98195, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Halperin
authors.affiliation: School of Oceanography and eScience Institute, University of Washington, Seattle, WA 98195, USA.
authors.forename: E Virginia
authors.initials: EV
authors.lastname: Armbrust
authors.affiliation: Department of Computer Science and Engineering, eScience Institute, University of Washington, Seattle, WA 98195, USA.
authors.forename: Bill
authors.initials: B
authors.lastname: Howe
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D004777:Environment; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D016011:Normal Distribution; D012984:Software