The viability criteria Ibrutinib ic50 for accepting a cell culture for use in the assay is set to >85%. Instructions on how to gate cells for phenotypic quality control and viability analysis are provided in Fig. 1B and C, respectively. The GARD input concentration of chemicals to be assayed is determined as described in the material & methods section. Following 24 h incubation, cells are harvested, RNA is isolated, cDNA is prepared and arrays are hybridized, washed and scanned as described. Once the array data is acquired,

it should be merged with a training data set, which consists of measurements of all 38 reference chemicals run during assay development (Johansson et al., 2011). The data is normalized with Affymetrix’s RMA algorithm. A data set consisting of both train data and any new samples that are to be assayed is now available for analysis. At this point, an SVM is trained on the training data. The trained SVM is a model, or an equation, that describes the hyperplane that best separates sensitizers from non-sensitizers in the train data. This model can then be applied to predict any unknown samples, i.e. the test data, as either sensitizers or non-sensitizers. The trained data is shown in a 3D PCA plot based on the GARD Prediction Signature in Fig. 1D, with a hyperplane represented as a 2D plane. This illustrates

the classifications performed by the SVM, visible and interpretable by the human eye, as unknown Isoconazole samples of a hypothetical test set (dark red) that group together with sensitizers click here of the training data (bright red) on one side of the hyperplane would be classified as sensitizers, while unknown samples that group together with non-sensitizers of the training data (green) on the other side of the hyperplane would be classified as non-sensitizers. The actual

SVM output is displayed as prediction values, corresponding to the Euclidean distance between the sample to be classified and the hyperplane. Thus, the decision value for any given sample represents the position of the sample in comparison to the hyperplane. Consequently, a positive prediction value denotes a sensitizer, and a negative value denotes a non-sensitizer. In addition, potency of a predicted sensitizer will be determined by the absolute value of the decision value, i.e. the actual distance to the hyperplane. A large decision value corresponds to a strong sensitizer, while a small decision value corresponds to a weaker sensitizer. In this section, the assessment of two chemicals will be exemplified, step by step. We will study the two compounds 2-nitro-1,4-phenylendiamine, a strong sensitizer according to the LLNA, and methyl salicylate, a non-sensitizer. Both of these compounds were used for the development of GARD, but for the sake of this exercise, they will be removed from the available data set and treated as unknown samples.