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High Content Screening pp 89–112Cite as

Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1683))

Abstract

Robust high-content screening of visual cellular phenotypes has been enabled by automated microscopy and quantitative image analysis. The identification and removal of common image-based aberrations is critical to the screening workflow. Out-of-focus images, debris, and auto-fluorescing samples can cause artifacts such as focus blur and image saturation, contaminating downstream analysis and impairing identification of subtle phenotypes. Here, we describe an automated quality control protocol implemented in validated open-source software, leveraging the suite of image-based measurements generated by CellProfiler and the machine-learning functionality of CellProfiler Analyst.

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Acknowledgements

This work was funded by the National Science Foundation (RIG DB-1119830 to M.A.B..) and the National Institutes of Health (R01 GM089652 to A.E.C.). We also thank Jane Hung and David Dao for offering helpful comments and suggestions during manuscript preparation.

Author information

Authors and Affiliations

  1. Novartis Institutes for BioMedical Research, Cambridge, MA, USA

    Mark-Anthony Bray

  2. Imaging Platform, Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA

    Mark-Anthony Bray & Anne E. Carpenter

Authors
  1. Mark-Anthony Bray
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  2. Anne E. Carpenter
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Corresponding author

Correspondence to Anne E. Carpenter .

Editor information

Editors and Affiliations

  1. Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Paul A. Johnston

  2. Bahama Bio, LLC, Bahama, North Carolina, USA

    Oscar J. Trask

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Bray, MA., Carpenter, A.E. (2018). Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler. In: Johnston, P., Trask, O. (eds) High Content Screening. Methods in Molecular Biology, vol 1683. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7357-6_7

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  • DOI: https://doi.org/10.1007/978-1-4939-7357-6_7

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7355-2

  • Online ISBN: 978-1-4939-7357-6

  • eBook Packages: Springer Protocols

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