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...
Saved in:
| Main Authors: | Julie Gorenstein, Ben Zack, Joseph R. Marszalek, Ansu Bagchi, Sai Subramaniam, Pamela Carroll, Cem Elbi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2010-09-01
|
| Series: | BioTechniques |
| Subjects: | |
| Online Access: | https://www.future-science.com/doi/10.2144/000113492 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Next-generation 9216-microwell cell arrays for high-content screening microscopy
by: Jürgen Reymann, et al.
Published: (2009-10-01) -
Towards a Survival-Based Cellular Assay for the Selection of Protease Inhibitors in <i>Escherichia coli</i>
by: William Y. Oyadomari, et al.
Published: (2025-03-01) -
Novel high-content and open-source image analysis tools for profiling mitochondrial morphology in neurological cell models
by: Marcus Y. Chin, et al.
Published: (2025-03-01) -
Rapid-response RNA-fluorescence in situ hybridization (FISH) assay platform for coronavirus antiviral high-throughput screening
by: Ryan Chan, et al.
Published: (2024-12-01) -
Single-plate kinome screening in live-cells to enable highly cost-efficient kinase inhibitor profiling
by: Martin P. Schwalm, et al.
Published: (2025-03-01)