Fields with less than 50 cells were filtered out after image segmentation. All cell features extracted from the image analysis step were normalized within each plate by subtracting the median value of control wells containing DMSO and biotin and dividing by the median absolute deviation of the same controls.

PCA was applied to normalized data for each dataset separately as a denoising method. From this PCA, the coordinates of wells in the subspace, defined by principal component with eigenvalue greater than one, were used to compute the Euclidean distance between wells. Hierarchical clustering with Ward’s agglomerative criterion was then applied on these distances. Two groups were identified from the clustering. The group with the positive controls was defined as the hits list. All analyses were performed using R statistical software.

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