ArticleBioinformatics (Oxford, England)2015
Local statistics allow quantification of cell-to-cell variability from high-throughput microscope images.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability.Molecular systems biology · 2020Article
- Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.PLoS computational biology · 2019Article
- YeastRGB: comparing the abundance and localization of yeast proteins across cells and libraries.Nucleic acids research · 2019Article
- Article
- Computational biology: deep learning.Emerging topics in life sciences · 2017Article
- Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.G3 (Bethesda, Md.) · 2017Article
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Authors and funding
4 authors.
Funding
Abstract
motivationQuantifying 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.
resultsWe 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.
conclusionsAutomated image analysis methods can be used to quantify cell-to-cell variability in microscope images.
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