This plugin contains most of existing colocalization methods in 2D and 3D fluorescence microscopy images: Pearson and Manders coefficients, Image Cross Correlation Spectroscopy (ICCS) and Object-based methods.
Institution: Institut Pasteur
Presentation & Litterature
Correlation & Overlap methods, and Parametric analysis (fit) of Ripley K function are described in
Lagache T, Sauvonnet N, Danglot L, Olivo-Marin JC. Statistical analysis of molecule colocalization in bioimaging. Cytometry A. 2015
SODA (object-based, non-parametric Ripley K function analysis) is described in
Lagache, T., Grassart, A., Dallongeville, S., Faklaris, O., Sauvonnet, N., Dufour, A., … & Olivo-Marin, J. C. (2018). Mapping molecular assemblies with fluorescence microscopy and object-based spatial statistics. Nature communications, 9(1), 698.
This plugin contains most of the existing colocalization methods. Before using it, you can find here an introduction to colocalization methods.
This plugin is decomposed into two main method classes:
1- Correlation & Overlap methods.
These methods are based either on the quantification of Pearson and cross-correlation (ICCS) between fluoresecent images or between the overlap of segmented objects (detections) through Manders, or Overlap (% of segmented objects 1 that overlap more than T% with objects 2, T is a free parameter (50% by default)) analysis
Detections for overlap analysis are output of spot detector plugin. (be sure to check the “Export to Swimming-pool” box in the “Output” menu)
For each coefficient (except ICCS), a p-value and its log are provided. For Pearson correlation coefficient, we compute a closed formula based on pixel scrambling and Central Limit theorem. For overlap analysis (Manders&Overlap), we use Monte-Carlo randomizations of detections’masks are used (number of MC simulations is a free parameter (10 by default)).
These methods are also implemented as a block: ColocalizationStudio_correlation
An exemple of protocol can be found here
1- “Distance between objects”-based methods.
These methods also use 2 sequences and detections sets from spot detector plugin.
3 methods are proposed:
–Distance analysis (centers of mass 1 inside masks 2): It counts the % of detections 1 whose center of mass (position) is inside a detection 2 mask. p-value and its log are computed analytically using binomial probabilities.
–Parametric analysis (fit) of the Ripley’s K function (see Lagache T, Sauvonnet N, Danglot L, Olivo-Marin JC. Statistical analysis of molecule colocalization in bioimaging. Cytometry A. 2015).
Here, we compute the Ripley’s K function (Step and Maximal distance for the analysis are free parameters) and fit it (avec zero-mean and unit-variance normalization) with the expected mean curve obtained when p% of detections 2 are colocalized around detections 1, at mean distance mu. These two fitting parameters appear in % of coloc. spots 2 and Mean coloc distance in the output section Ripley’s analysis (parametric).
The quality of the fit can be checked (Plot K function graph to check the fit box)
–Non-parametric Ripley’s analysis (SODA) (see Lagache, T., Grassart, A., Dallongeville, S., Faklaris, O., Sauvonnet, N., Dufour, A., … & Olivo-Marin, J. C. (2018). Mapping molecular assemblies with fluorescence microscopy and object-based spatial statistics. Nature communications, 9(1), 698.)
SODA performs a non-parametric analysis of Ripley’s function (statistical thresholding) and is more robust than parametric analysis. Based on maximum of the Ripley’s function it computes analytically the p-value and its log.
These methods are also implemented as a block: ColocalizationStudio_object
An example of protocol can be found here