ArticleSource code for biology and medicine2016
Implementation of the Rank-Weighted Co-localization (RWC) algorithm in multiple image analysis platforms for quantitative analysis of microscopy images.
Article in Source code for biology and medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Predicting DNA damage response in non-small cell lung cancer organoids via simultaneous label-free autofluorescence multiharmonic microscopy.Redox biology · 2024Article
- Targeting transgenic proteins to alpha granules for platelet-directed gene therapy.Molecular therapy. Nucleic acids · 2022Article
- Article
- Integrative network and transcriptomics-based approach predicts genotype- specific drug combinations for melanoma.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 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
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2 authors.
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Abstract
backgroundQuantitative co-localization studies strengthen the analysis of fluorescence microscopy-based assays and are essential for illustrating and understanding many cellular processes and interactions. In our earlier study, we presented a rank-based intensity weighting scheme for the quantification of co-localization between structures in fluorescence microscopy images. This method, which uses a combined pixel co-occurrence and intensity correlation approach, is superior to conventional algorithms and provides a more accurate quantification of co-localization.
findingsIn this brief report we provide the source code and implementation of the rank-weighted co-localization (RWC) algorithm in three (two open source and one proprietary) image analysis platforms. The RWC algorithm has been implemented as a plugin for ImageJ, a module for CellProfiler and an Acapella script for Columbus image analysis software tools.
conclusionsWe have provided with a web resource from which users can download plugins and modules implementing the RWC algorithm in various commonly used image analysis platforms. The implementations have been designed for easy incorporation into existing tools in a 'ready-for-use' format. The resources can be accessed through the following web link: http://simpsonlab.pbworks.com/w/page/48541482/Bioinformatic_Tools.
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