ArticleBMC bioinformatics2011
Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
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- Confocal microscopy analysis reveals that only a small proportion of extracellular vesicles are successfully labelled with commonly utilised staining methods.Scientific reports · 2022Article
- Co-Density Distribution Maps for Advanced Molecule Colocalization and Co-Distribution Analysis.Sensors (Basel, Switzerland) · 2021Article
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- YIPF2 is a novel Rab-GDF that enhances HCC malignant phenotypes by facilitating CD147 endocytic recycle.Cell death & disease · 2019Article
- APOL1 risk allele RNA contributes to renal toxicity by activating protein kinase R.Communications biology · 2018Article
- Functional characterisation of the YIPF protein family in mammalian cells.Histochemistry and cell biology · 2017Article
- A systematic High-Content Screening microscopy approach reveals key roles for Rab33b, OATL1 and Myo6 in nanoparticle trafficking in HeLa cells.Scientific reports · 2016Article
- Implementation of the Rank-Weighted Co-localization (RWC) algorithm in multiple image analysis platforms for quantitative analysis of microscopy images.Source code for biology and medicine · 2016Article
- Registration of whole immunohistochemical slide images: an efficient way to characterize biomarker colocalization.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Immunofluorescence and fluorescent-protein tagging show high correlation for protein localization in mammalian cells.Nature methods · 2013Article
- A method for improved clustering and classification of microscopy images using quantitative co-localization coefficients.BMC research notes · 2012Article
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4 authors.
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Abstract
backgroundAccurate quantitative co-localization is a key parameter in the context of understanding the spatial co-ordination of molecules and therefore their function in cells. Existing co-localization algorithms consider either the presence of co-occurring pixels or correlations of intensity in regions of interest. Depending on the image source, and the algorithm selected, the co-localization coefficients determined can be highly variable, and often inaccurate. Furthermore, this choice of whether co-occurrence or correlation is the best approach for quantifying co-localization remains controversial.
resultsWe have developed a novel algorithm to quantify co-localization that improves on and addresses the major shortcomings of existing co-localization measures. This algorithm uses a non-parametric ranking of pixel intensities in each channel, and the difference in ranks of co-localizing pixel positions in the two channels is used to weight the coefficient. This weighting is applied to co-occurring pixels thereby efficiently combining both co-occurrence and correlation. Tests with synthetic data sets show that the algorithm is sensitive to both co-occurrence and correlation at varying levels of intensity. Analysis of biological data sets demonstrate that this new algorithm offers high sensitivity, and that it is capable of detecting subtle changes in co-localization, exemplified by studies on a well characterized cargo protein that moves through the secretory pathway of cells.
conclusionsThis algorithm provides a novel way to efficiently combine co-occurrence and correlation components in biological images, thereby generating an accurate measure of co-localization. This approach of rank weighting of intensities also eliminates the need for manual thresholding of the image, which is often a cause of error in co-localization quantification. We envisage that this tool will facilitate the quantitative analysis of a wide range of biological data sets, including high resolution confocal images, live cell time-lapse recordings, and high-throughput screening data sets.
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