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 measureme...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2010-09-01
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| Series: | BioTechniques |
| Subjects: | |
| Online Access: | https://www.future-science.com/doi/10.2144/000113492 |
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| Summary: | 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. |
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| ISSN: | 0736-6205 1940-9818 |