Abstract
Principal component analysis enables dimensional reduction of multivariate datasets that are typical in high-content screening. A common analysis utilizing principal components is a distance measurement between a perturbagen—such as small-molecule treatment or shRNA knockdown—and a negative control. This method works well to identify active perturbagens, though it cannot discern between distinct phenotypic responses. Here, we describe an extension of the principal component analysis approach to multivariate high-content screening data to enable quantification of differences in direction in principal component space. The theta comparative cell scoring method can identify and quantify differential phenotypic responses between panels of cell lines to small-molecule treatment to support in vitro pharmacogenomics and drug mechanism-of-action studies.
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Acknowledgments
This work was supported by a Cancer Research UK Ph.D. Studentship award to the Cancer Research UK Edinburgh Centre.
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Warchal, S.J., Dawson, J.C., Carragher, N.O. (2018). High-Dimensional Profiling: The Theta Comparative Cell Scoring Method. In: Wagner, B. (eds) Phenotypic Screening. Methods in Molecular Biology, vol 1787. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7847-2_13
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DOI: https://doi.org/10.1007/978-1-4939-7847-2_13
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