ColocalizR: An open-source application for cell-based high-throughput colocalization analysis

Allan Sauvat, Marion Leduc, Kevin Müller, Oliver Kepp, Guido Kroemer

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)

    Abstract

    The microscopic assessment of the colocalization of fluorescent signals has been widely used in cell biology. Although imaging techniques have drastically improved over the past decades, the quantification of colocalization by measures such as the Pearson correlation coefficient or Manders overlap coefficient, has not changed. Here, we report the development of an R-based application that allows to (i) automatically segment cells and subcellular compartments, (ii) measure morphology and texture features, and (iii) calculate the degree of colocalization within each cell. Colocalization can thus be studied on a cell-by-cell basis, permitting to perform statistical analyses of cellular populations and subpopulations. ColocalizR has been designed to parallelize tasks, making it applicable to the analysis of large data sets. Its graphical user interface makes it suitable for researchers without specific knowledge in image analysis. Moreover, results can be exported into a wide range of formats rendering post-analysis adaptable to statistical requirements. This application and its source code are freely available at https://github.com/kroemerlab/ColocalizR.

    Original languageEnglish
    Pages (from-to)227-234
    Number of pages8
    JournalComputers in Biology and Medicine
    Volume107
    DOIs
    Publication statusPublished - 1 Apr 2019

    Keywords

    • Cellular imaging
    • Co-distribution
    • Co-occurrence
    • Fluorescence microscopy
    • Systems biology

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