Determination of Quantal Dose-Response Characteristics in Phenotypic Assays using Supervised Classification

We have designed and implemented a fully automatic, high-throughput screen against the causative parasite of the devastating illness, schistosomiasis, using computer vision and machine learning. The computer vision component segments (individually recognizes and delineates) schistosomula in brightfield micrographs, including touching and partially overlapping parasites. A learning model employing support vector machines to identify schistosomula which differ significantly from controls. Classification is performed in a high-dimensional feature space, the dimensions of which correspond to measurements of appearance, shape and texture. Because variation between different populations of schistosomula unavoidably creates different baselines for different experiments, classification is conducted in two stages: one in which putatively “normal” parasites are identified within each control image and used to derive an estimated control centroid, and one in which all parasites are classified as “normal” or “degenerate” on the basis of tuples composed of a given parasite’s feature vector and corresponding control centroid. Finally, a continuous measurement of the phenotypic response to a particular experimental condition (such as the concentration of a certain drug) is produced using the notion of quantal response, or the proportion of individuals which differ from controls in a particular subpopulation. The learning model is demonstrated to be highly effective, and for test data has an accuracy of 0.89. Dose-response curves produced for four compounds (fluvastatin, niclosamide, praziquantel and simvastatin) with the automated method are also tightly correlated to those produced by human experts, with high statistical significance. Correlation values are > 0.97 in all cases with p-values << 10 -3 . _____________________________________________________________________