title: Machine learning improves the precision and robustness of high-content screens: using nonlinear multiparametric methods to analyze screening results.
abstract: Imaging-based high-content screens often rely on single cell-based evaluation of phenotypes in large data sets of microscopic images. Traditionally, these screens are analyzed by extracting a few image-related parameters and use their ratios (linear single or multiparametric separation) to classify the cells into various phenotypic classes. In this study, the authors show how machine learning-based classification of individual cells outperforms those classical ratio-based techniques. Using fluorescent intensity and morphological and texture features, they evaluated how the performance of data analysis increases with increasing feature numbers. Their findings are based on a case study involving an siRNA screen monitoring nucleoplasmic and nucleolar accumulation of a fluorescently tagged reporter protein. For the analysis, they developed a complete analysis workflow incorporating image segmentation, feature extraction, cell classification, hit detection, and visualization of the results. For the classification task, the authors have established a new graphical framework, the Advanced Cell Classifier, which provides a very accurate high-content screen analysis with minimal user interaction, offering access to a variety of advanced machine learning methods.
authors.affiliation: Light Microscopy Centre, ETH Zurich, Zurich, Switzerland. peter.horvath@lmc.biol.ethz.ch
authors.forename: Peter
authors.initials: P
authors.lastname: Horvath
authors.forename: Thomas
authors.initials: T
authors.lastname: Wild
authors.forename: Ulrike
authors.initials: U
authors.lastname: Kutay
authors.forename: Gabor
authors.initials: G
authors.lastname: Csucs
mesh_terms: D001185:Artificial Intelligence; D057166:High-Throughput Screening Assays; D015233:Models, Statistical; D016571:Neural Networks, Computer; D034741:RNA, Small Interfering; D059010:Single-Cell Analysis
chemical_list: D034741:RNA, Small Interfering