Janna Hutz1, Thomas Nelson1, Hua Wu1, Gregory McAllister1, Ioannis Moutsatsos1, Savina Jaeger1, Somnath Bandyopadhyay1, Jeremy Jenkins1, Douglas Selinger1
Novartis Institutes for Biomedical Research, Cambridge, MA, United States
The use of high-throughput, information-rich technologies such as microarrays, high-content screening (HCS) and next-generation sequencing (NGS) has become increasingly widespread in pharmaceutical research and development. Compared to single-readout assays, these methods can produce a more comprehensive look at the biological activities of screened treatments. However, interpreting multidimensional readouts can be a challenge. Univariate statistics such as t-tests and Z-factors cannot easily be applied to multidimensional data, requiring the development of other methods to answer common screening questions like “Is treatment X active in this assay?”, “Is treatment X a hit?”, and “Is treatment X different from treatment Y?” We have developed a simple, straightforward metric, the multidimensional perturbation value (mp-value), which can be used to answer these questions. The mp-value has successfully distinguished active from inactive treatments using data from a variety of platforms. We believe the mp-value has great potential to simplify the analysis of multidimensional data while taking full advantage of its richness.
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