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. Contact: [email protected] Availability and Implementation: Software and data are available at http://www.moseslab.csb.utoronto.ca/louis-f/ Supplementary information: Supplementary data are available at Bioinformatics online.

Protein subcellular localization has been systematically characterized in budding yeast using fluorescently tagged proteins. Based on the fluorescence microscopy images, subcellular localization of many proteins can be classified automatically using supervised machine learning approaches that have been trained to recognize predefined image classes based on statistical features. Here, we present an unsupervised analysis of protein expression patterns in a set of high-resolution, high-throughput microscope images. Our analysis is based on 7 biologically interpretable features which are evaluated on automatically identified cells, and whose cell-stage dependency is captured by a continuous model for cell growth. We show that it is possible to identify most previously identified localization patterns in a cluster analysis based on these features and that similarities between the inferred expression patterns contain more information about protein function than can be explained by a previous manual categorization of subcellular localization. Furthermore, the inferred cell-stage associated to each fluorescence measurement allows us to visualize large groups of proteins entering the bud at specific stages of bud growth. These correspond to proteins localized to organelles, revealing that the organelles must be entering the bud in a stereotypical order. We also identify and organize a smaller group of proteins that show subtle differences in the way they move around the bud during growth. Our results suggest that biologically interpretable features based on explicit models of cell morphology will yield unprecedented power for pattern discovery in high-resolution, high-throughput microscopy images.