Open AccessReducing the multidimensionality of high-content screening into versatile powerful descriptors
Author(s) -
Julie Gorenstein,
Ben Zack,
Joseph R. Marszalek,
Ansu Bagchi,
Sai Subramaniam,
Pamela M. Carroll,
Cem Elbi
Publication year - 2010
Publication title -
biotechniques/biotechniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.617
H-Index - 131
eISSN - 1940-9818
pISSN - 0736-6205
DOI - 10.2144/000113492
Subject(s) - nonparametric statistics , high content screening , set (abstract data type) , biological system , computational biology , computer science , interpretation (philosophy) , action (physics) , statistics , biology , mathematics , pattern recognition (psychology) , artificial intelligence , genetics , cell , physics , quantum mechanics , programming language
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.