AI Article Synopsis
- Researchers aim to quantify the variability in protein expression and haven't systematically measured cell-to-cell differences in subcellular localization patterns.
- A new local measure has been developed to quantify this cell-to-cell variability using high-throughput microscope images, facilitating comparisons among proteins with various localization styles.
- The study demonstrated that automated image analysis can effectively assess these variabilities, specifically analyzing the yeast GFP collection.
Article Abstract
Motivation: Quantifying variability in protein expression is a major goal of systems biology and cell-to-cell variability in subcellular localization pattern has not been systematically quantified.
Results: We define a local measure to quantify cell-to-cell variability in high-throughput microscope images and show that it allows comparable measures of variability for proteins with diverse subcellular localizations. We systematically estimate cell-to-cell variability in the yeast GFP collection and identify examples of proteins that show cell-to-cell variability in their subcellular localization.
Conclusions: Automated image analysis methods can be used to quantify cell-to-cell variability in microscope images.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4380034 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btu759 | DOI Listing |
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