Louis-François Handfield1, Bob Strome1, Yolanda T Chong1, Alan M Moses2. 1. Department of Computer Science, Department of Cell & Systems Biology and Department of Molecular Genetics, University of Toronto, Ontario M5S 3B2, Canada. 2. Department of Computer Science, Department of Cell & Systems Biology and Department of Molecular Genetics, University of Toronto, Ontario M5S 3B2, Canada Department of Computer Science, Department of Cell & Systems Biology and Department of Molecular Genetics, University of Toronto, Ontario M5S 3B2, Canada.
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.
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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