Statistical techniques for handling high content screening data
publication date: Sep 21, 2007
One of the chief incentives for the use of high content screening (HCS) approaches is the data rich return one gets from an individual assay. However, conventional methods for hit selection and activity determination are not well suited to handling multi-parametric data. Tools borrowed from the genomics area have been applied to HCS data, but there are important differences between the two data types that are driving the development of novel statistical approaches for HCS data analysis. This article will describe the use of techniques such as principal component analysis, classification trees, neural networks and random forests, as well as recently published approaches for the identification and classification of compound profiles resulting from HCS assays.
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