Imagej colocalization analysis6/2/2023 – Non-parametric Ripley’s analysis (SODA) (see Lagache, T., Grassart, A., Dallongeville, S., Faklaris, O., Sauvonnet, N., Dufour, A., … & Olivo-Marin, J. The quality of the fit can be checked ( Plot K function graph to check the fit box) spots 2 and Mean coloc distancein the output section Ripley’s analysis (parametric). These two fitting parameters appear in % of coloc. 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. Statistical analysis of molecule colocalization in bioimaging. – Parametric analysis (fit) of the Ripley’s K function (see Lagache T, Sauvonnet N, Danglot L, Olivo-Marin JC. p-value and its log are computed analytically using binomial probabilities. – 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. These methods also use 2 sequences and detections sets from spot detector plugin. WARNING: input ROIs should have their Z and T position set to 0 otherwise it won’t work !ġ- “Distance between objects”-based methods. These methods are also implemented as a block: ColocalizationStudio_correlation 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)). For Pearson correlation coefficient, we compute a closed formula based on pixel scrambling and Central Limit theorem. 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)) analysisĭetections for overlap analysis are output of spot detector plugin. (be sure to check the “Export to Swimming-pool” box in the “Output” menu)įor each coefficient (except ICCS), a p-value and its log are provided. This plugin is decomposed into two main method classes: Before using it, you can find here an introduction to colocalization methods. ![]() This plugin contains most of the existing colocalization methods. Mapping molecular assemblies with fluorescence microscopy and object-based spatial statistics. Lagache, T., Grassart, A., Dallongeville, S., Faklaris, O., Sauvonnet, N., Dufour, A., … & Olivo-Marin, J. SODA (object-based, non-parametric Ripley K function analysis) is described in Lagache T, Sauvonnet N, Danglot L, Olivo-Marin JC. Correlation & Overlap methods, and Parametric analysis (fit) of Ripley K function are described in
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