Reducing the multidimensionality of high-content screening into versatile powerful descriptors - Julie Gorenstein; Ben Zack; Joseph R. Marszalek; Ansu Bagchi; Sai Subramaniam; Pamela Carroll; Cem Elbi (BioTechniques)
High-content image analysis captures many cellular parameters, but current methods of interpretation of acquired multiple dimensions assume a normal distribution, which is rarely seen in biological data sets. We describe a novel statistically based approach that collapses a set of cellular measurements into a single value, permitting a simplified and unbiased comparison of heterogeneous cellular populations. Differences in multiple cellular responses across two populations are measured using nonparametric Kolmogorov-Smirnov (KS) statistics. This method can be used to study cellular functions, to identify novel target genes and pharmacodynamic biomarkers, and to characterize drug mechanisms of action.