title: Reducing the multidimensionality of high-content screening into versatile powerful descriptors.
abstract: 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.
authors.affiliation: Department of Oncology, Merck Research Laboratories, Boston, MA 02115, USA.
authors.forename: Julie
authors.initials: J
authors.lastname: Gorenstein
authors.forename: Ben
authors.initials: B
authors.lastname: Zack
authors.forename: Joseph R
authors.initials: JR
authors.lastname: Marszalek
authors.forename: Ansu
authors.initials: A
authors.lastname: Bagchi
authors.forename: Sai
authors.initials: S
authors.lastname: Subramaniam
authors.forename: Pamela
authors.initials: P
authors.lastname: Carroll
authors.forename: Cem
authors.initials: C
authors.lastname: Elbi
mesh_terms: D003584:Cytological Techniques / Q000191:economics / Q000379:methods*; D055808:Drug Discovery / Q000191:economics / Q000379:methods; D005786:Gene Expression Regulation; D057166:High-Throughput Screening Assays / Q000191:economics / Q000379:methods*; D015233:Models, Statistical