Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler
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 wordsCell-based assays High-content screening Image analysis Microscopy Quality control Machine learning Open-source software
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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