TY - JOUR
T1 - ColocalizR
T2 - An open-source application for cell-based high-throughput colocalization analysis
AU - Sauvat, Allan
AU - Leduc, Marion
AU - Müller, Kevin
AU - Kepp, Oliver
AU - Kroemer, Guido
N1 - Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
KW - Cellular imaging
KW - Co-distribution
KW - Co-occurrence
KW - Fluorescence microscopy
KW - Systems biology
UR - http://www.scopus.com/inward/record.url?scp=85062437707&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2019.02.024
DO - 10.1016/j.compbiomed.2019.02.024
M3 - Article
C2 - 30852249
AN - SCOPUS:85062437707
SN - 0010-4825
VL - 107
SP - 227
EP - 234
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
ER -