Implementation of the Rank-Weighted Co-localization (RWC) algorithm in multiple image analysis platforms for quantitative analysis of microscopy images. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Singan, Vasanth; Simpson, Jeremy
• Is Part Of: Source code for biology and medicine. Volume 11:Issue 1(2016)
• Page Start: 1
• Page End: 3
• Publication Date: 2016-12
• Subjects: Biology -- Data processing -- Periodicals; Medicine -- Data processing -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1186/s13029-016-0048-8
• Languages: English
• ISSNs: 1751-0473

Abstract

Abstract Background Quantitative 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. Findings In 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. Conclusions We 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 .