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

  • Mark-Anthony Bray
  • Anne E. Carpenter
Part of the Methods in Molecular Biology book series (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.

Key words

Cell-based assays High-content screening Image analysis Microscopy Quality control Machine learning Open-source software 

Notes

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.

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Copyright information

© Springer Science+Business Media LLC 2018

Authors and Affiliations

  1. 1.Novartis Institutes for BioMedical ResearchCambridgeUSA
  2. 2.Imaging Platform, Broad Institute of MIT and HarvardCambridgeUSA

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