scholarly journals Peer Review #1 of "colocr: an R package for conducting co-localization analysis on fluorescence microscopy images (v0.1)"

2019 ◽  
Keyword(s):  
Fluorescence Microscopy ◽  
Peer Review ◽  
R Package ◽  
Localization Analysis ◽  
Microscopy Images

scholarly journals colocr: an R package for conducting co-localization analysis on fluorescence microscopy images

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7255
Author(s):  
Mahmoud Ahmed ◽  
Trang Huyen Lai ◽  
Deok Ryong Kim
Keyword(s):  
Fluorescence Microscopy ◽  
R Package ◽  
Regions Of Interest ◽  
Biological Research ◽  
Microscopy Image ◽  
Shiny App ◽  
Localization Analysis ◽  
Microscopy Image Analysis ◽  
Microscopy Images ◽  
Selection Of

Background The co-localization analysis of fluorescence microscopy images is a widely used technique in biological research. It is often used to determine the co-distribution of two proteins inside the cell, suggesting that these two proteins could be functionally or physically associated. The limiting step in conducting microscopy image analysis in a graphical interface tool is the selection of the regions of interest for the co-localization of two proteins. Implementation This package provides a simple straightforward workflow for loading fluorescence images, choosing regions of interest and calculating co-localization measurements. Included in the package is a shiny app that can be invoked locally to interactively select the regions of interest where two proteins are co-localized. Availability colocr is available on the comprehensive R archive network, and the source code is available on GitHub under the GPL-3 license as part of the ROpenSci collection, https://github.com/ropensci/colocr.

scholarly journals colocr: An R package for conducting co-localization analysis on fluorescence microscopy images

2019 ◽  
Author(s):  
Mahmoud Ahmed ◽  
Trang Huyen Lai ◽  
Deok Ryong Kim
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
R Package ◽  
Regions Of Interest ◽  
Biological Research ◽  
Microscopy Image ◽  
Shiny App ◽  
Localization Analysis ◽  
Microscopy Images ◽  
Selection Of

Background The co-localization analysis of fluorescence microscopy images is a widely used tech- nique in biological research. It is often used to determine the co-distribution of two proteins inside the cell, suggesting that these two proteins could be functionally or physically associated. The limiting step in conducting microscopy image analysis in a graphical interface tool is the selection of the regions of interest for the co-localization of two proteins. Implementation This package provides a simple straight forward workflow for loading fluorescence images, choosing regions of interest and calculating co-localization statistics. Included in the package is a shiny app that can be invoked locally to select the regions of interest where two proteins are interactively co-localized. Availability colocr is available on the comprehensive R archive network, and the source code is available on GitHub as part of the ROpenSci collection, https://github.com/ropensci/colocr. Keywords: R package, co-localization, image-analysis, fluorescence microscopy, statistics

scholarly journals colocr: An R package for conducting co-localization analysis on fluorescence microscopy images

2019 ◽  
Author(s):  
Mahmoud Ahmed ◽  
Trang Huyen Lai ◽  
Deok Ryong Kim
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
R Package ◽  
Regions Of Interest ◽  
Biological Research ◽  
Microscopy Image ◽  
Shiny App ◽  
Localization Analysis ◽  
Microscopy Images ◽  
Selection Of

Background The co-localization analysis of fluorescence microscopy images is a widely used tech- nique in biological research. It is often used to determine the co-distribution of two proteins inside the cell, suggesting that these two proteins could be functionally or physically associated. The limiting step in conducting microscopy image analysis in a graphical interface tool is the selection of the regions of interest for the co-localization of two proteins. Implementation This package provides a simple straight forward workflow for loading fluorescence images, choosing regions of interest and calculating co-localization statistics. Included in the package is a shiny app that can be invoked locally to select the regions of interest where two proteins are interactively co-localized. Availability colocr is available on the comprehensive R archive network, and the source code is available on GitHub as part of the ROpenSci collection, https://github.com/ropensci/colocr. Keywords: R package, co-localization, image-analysis, fluorescence microscopy, statistics

scholarly journals Multiframe sure-let denoising of timelapse fluorescence microscopy images

2008 ◽  
Author(s):  
Saskia Delpretti ◽  
Florian Luisier ◽  
Sathish Ramani ◽  
Thierry Blu ◽  
Michael Unser
Keyword(s):  
Fluorescence Microscopy ◽  
Microscopy Images

Data Fusion and Smoothing for Probabilistic Tracking of Viral Structures in Fluorescence Microscopy Images

2021 ◽  
pp. 102168
Author(s):  
C. Ritter ◽  
T. Wollmann ◽  
J.-Y. Lee ◽  
A. Imle ◽  
B. Müller ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Data Fusion ◽  
Probabilistic Tracking ◽  
Microscopy Images ◽  
Viral Structures

scholarly journals Quantitative Segmentation of Fluorescence Microscopy Images of Heterogeneous Tissue: Application to the Detection of Residual Disease in Tumor Margins

PLoS ONE ◽  
2013 ◽  
Vol 8 (6) ◽  
pp. e66198 ◽  
Author(s):  
Jenna L. Mueller ◽  
Zachary T. Harmany ◽  
Jeffrey K. Mito ◽  
Stephanie A. Kennedy ◽  
Yongbaek Kim ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Residual Disease ◽  
Tumor Margins ◽  
Heterogeneous Tissue ◽  
Microscopy Images
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