High-content, imaging-based screens now routinely generate data on a scale that precludes
manual verification and interrogation. Software applying machine learning has become
an essential tool to automate analysis, but these methods require annotated examples
to learn from. Efficiently exploring large datasets to find relevant examples remains
a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical
software package for phenotypic analysis that addresses these difficulties. ACC applies
machine-learning and image-analysis methods to high-content data generated by large-scale,
cell-based experiments. It features methods to mine microscopic image data, discover
new phenotypes, and improve recognition performance. We demonstrate that these features
substantially expedite the training process, successfully uncover rare phenotypes,
and improve the accuracy of the analysis. ACC is extensively documented, designed
to be user-friendly for researchers without machine-learning expertise, and distributed
as a free open-source tool at www.cellclassifier.org.
