![]() Its tools can help identify complex and subtle phenotypes, improve quality control and provide single-cell and population-level information from experiments. Using data from feature extraction software such as CellProfiler ( Kamentsky et al., 2011), CellProfiler Analyst offers easy-to-use tools for exploration and mining of image data, which is being generated in ever increasing amounts, particularly in high-content screens (HCS). We implemented an automatic build process that supports nightly updates and regular release cycles for the software.Ĭontact: information: Supplementary data are available at Bioinformatics online.ĬellProfiler Analyst is open-source software for biological image-based classification, data exploration and visualization with an interactive user interface designed for biologists and data scientists. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. ![]() CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery).Īvailability and Implementation: CellProfiler Analyst 2.0 is free and open source, available at and from GitHub ( ) under the BSD license. ![]() Summary: CellProfiler Analyst allows the exploration and visualization of image-based data, together with the classification of complex biological phenotypes, via an interactive user interface designed for biologists and data scientists. ![]()
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