The authors present an unsupervised, scalable, and interpretable cell profiling framework that is compatible with data gathered from high-content screening. They demonstrate the effectiveness of their framework by modeling drug differential effects of IC-21 macrophages treated with microtubule and actin disrupting drugs. They identify significant features of cell phenotypes for unsupervised learning based on maximum relevancy and minimum redundancy criteria. A 2-stage clustering approach annotates, clusters cells, and then merges them together to form super-clusters. An interpretable cell profile consisting of super-cluster proportions profiled at each drug treatment, concentration, or duration is obtained. Differential changes in super-cluster profiles are the basis for understanding the drug's differential effect and biology. The authors' method is validated by significant chi-squared statistics obtained from similar drug-treated super-cluster profiles from a 5-fold cross-validation. In addition, drug profiles of 2 microtubule drugs with equivalent mechanisms of action are statistically similar. Several distinct trends are identified for the 5 cytoskeletal drugs profiled under different conditions.