ArticlePLoS computational biology2013
Unsupervised clustering of subcellular protein expression patterns in high-throughput microscopy images reveals protein complexes and functional relationships between proteins.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Partially shared multi-modal embedding learns holistic representation of cell state.Nature computational science · 2026Article
- ML-based Models as a Strategy to Discover Novel Antiepileptic Drugs Targeting Sodium Receptor Channel.Current topics in medicinal chemistry · 2025Article
- Using Dimensionality Reduction to Visualize Phenotypic Changes in High-Throughput Microscopy.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC.BMC biology · 2022Article
- Article
- YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells.Bioinformatics (Oxford, England) · 2019Article
- Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.PLoS computational biology · 2019Article
- Deep learning is combined with massive-scale citizen science to improve large-scale image classification.Nature biotechnology · 2018Article
- Extracting and Integrating Protein Localization Changes from Multiple Image Screens of Yeast Cells.Bio-protocol · 2018Article
- Article
- Selection maintains signaling function of a highly diverged intrinsically disordered region.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Machine learning and computer vision approaches for phenotypic profiling.The Journal of cell biology · 2017Review
- An Unsupervised kNN Method to Systematically Detect Changes in Protein Localization in High-Throughput Microscopy Images.PloS one · 2016Article
- Single-cell phenomics in budding yeast.Molecular biology of the cell · 2015Review
- Synthetic physical interactions map kinetochore regulators and regions sensitive to constitutive Cdc14 localization.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Local statistics allow quantification of cell-to-cell variability from high-throughput microscope images.Bioinformatics (Oxford, England) · 2015Article
- The emergence of proteome-wide technologies: systematic analysis of proteins comes of age.Nature reviews. Molecular cell biology · 2014Review
- Quantitative protein localization signatures reveal an association between spatial and functional divergences of proteins.PLoS computational biology · 2014Article
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5 authors.
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Abstract
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.
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