Reducing the multidimensionality of high-content screening into versatile powerful descriptors.
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.
Gorenstein
Julie
J
Department of Oncology, Merck Research Laboratories, Boston, MA 02115, USA.
Zack
Ben
B
Marszalek
Joseph R
JR
Bagchi
Ansu
A
Subramaniam
Sai
S
Carroll
Pamela
P
Elbi
Cem
C
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