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.
CITATION STYLE
Bray, M. A., & Carpenter, A. E. (2018). Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler. In Methods in Molecular Biology (Vol. 1683, pp. 89–112). Humana Press Inc. https://doi.org/10.1007/978-1-4939-7357-6_7
Mendeley helps you to discover research relevant for your work.