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 |
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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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| _version_ | 1850153869750829056 |
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| author | Julie Gorenstein Ben Zack Joseph R. Marszalek Ansu Bagchi Sai Subramaniam Pamela Carroll Cem Elbi |
| author_facet | Julie Gorenstein Ben Zack Joseph R. Marszalek Ansu Bagchi Sai Subramaniam Pamela Carroll Cem Elbi |
| author_sort | Julie Gorenstein |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4fa1b3545c734e788e4da312f12406bc |
| institution | OA Journals |
| issn | 0736-6205 1940-9818 |
| language | English |
| publishDate | 2010-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | BioTechniques |
| spelling | doaj-art-4fa1b3545c734e788e4da312f12406bc2025-08-20T02:25:37ZengTaylor & Francis GroupBioTechniques0736-62051940-98182010-09-0149366366510.2144/000113492Reducing the multidimensionality of high-content screening into versatile powerful descriptorsJulie Gorenstein0Ben Zack1Joseph R. Marszalek2Ansu Bagchi3Sai Subramaniam4Pamela Carroll5Cem Elbi61Department of Oncology, Merck Research Laboratories, Boston, MA, USA2Department of Information Systems/Information Technology, Merck Research Laboratories, Boston, MA, USA1Department of Oncology, Merck Research Laboratories, Boston, MA, USA3Applied Computer Science and Mathematics, Informatics IT, Global Services, Merck & Co., Inc., Rahway, NJ, USA2Department of Information Systems/Information Technology, Merck Research Laboratories, Boston, MA, USA1Department of Oncology, Merck Research Laboratories, Boston, MA, USA1Department of Oncology, Merck Research Laboratories, Boston, MA, USAHigh-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.https://www.future-science.com/doi/10.2144/000113492high-content high-throughput drug screeninghigh-content imagingRNA interference screen |
| spellingShingle | Julie Gorenstein Ben Zack Joseph R. Marszalek Ansu Bagchi Sai Subramaniam Pamela Carroll Cem Elbi Reducing the multidimensionality of high-content screening into versatile powerful descriptors BioTechniques high-content high-throughput drug screening high-content imaging RNA interference screen |
| title | Reducing the multidimensionality of high-content screening into versatile powerful descriptors |
| title_full | Reducing the multidimensionality of high-content screening into versatile powerful descriptors |
| title_fullStr | Reducing the multidimensionality of high-content screening into versatile powerful descriptors |
| title_full_unstemmed | Reducing the multidimensionality of high-content screening into versatile powerful descriptors |
| title_short | Reducing the multidimensionality of high-content screening into versatile powerful descriptors |
| title_sort | reducing the multidimensionality of high content screening into versatile powerful descriptors |
| topic | high-content high-throughput drug screening high-content imaging RNA interference screen |
| url | https://www.future-science.com/doi/10.2144/000113492 |
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