Quality control for high-throughput imaging experiments using machine learning in CellProfiler

Summary: 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 impair 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 gener

Citation:Mark-Anthony Bray and Anne E Carpenter, “Quality control for high-throughput imaging experiments using machine learning in CellProfiler,” Methods in Molecular Biology, in press.

Pipelines and materials

Image data

Raw image data from a compound-profiling experiment applied to human MCF7 cells (doi). [LINK]

Pipelines

A pipeline for measuring quality control image features from the image data set above and exporting them to a MySQL database: [ZIP, 4 KB]