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COPASutils: An R Package for Reading, Processing, and Visualizing Data from COPAS Large-Particle Flow Cytometers

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  • Tyler C Shimko
  • Erik C Andersen

Abstract

The R package COPASutils provides a logical workflow for the reading, processing, and visualization of data obtained from the Union Biometrica Complex Object Parametric Analyzer and Sorter (COPAS) or the BioSorter large-particle flow cytometers. Data obtained from these powerful experimental platforms can be unwieldy, leading to difficulties in the ability to process and visualize the data using existing tools. Researchers studying small organisms, such as Caenorhabditis elegans, Anopheles gambiae, and Danio rerio, and using these devices will benefit from this streamlined and extensible R package. COPASutils offers a powerful suite of functions for the rapid processing and analysis of large high-throughput screening data sets.

Suggested Citation

  • Tyler C Shimko & Erik C Andersen, 2014. "COPASutils: An R Package for Reading, Processing, and Visualizing Data from COPAS Large-Particle Flow Cytometers," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-5, October.
  • Handle: RePEc:plo:pone00:0111090
    DOI: 10.1371/journal.pone.0111090
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    1. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
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