== Plugins › BIOP › Image Analysis › BIOP JACoP == Based on [[ https://imagejdocu.tudor.lu/plugin/analysis/jacop_2.0/just_another_colocalization_plugin/start| JACoP ]] = Available methods/metrics = [[ https://en.wikipedia.org/wiki/Pearson_correlation_coefficient | Pearson's correlation Coefficient ]] [[ https://en.wikipedia.org/wiki/Pearson_correlation_coefficient | Mander's coefficient Articles ]] [[ https://imagej.net/_images/e/e2/Costes_etalColoc.pdf | Costes Article ]] [[ https://imagej.net/_images/9/9e/LietAlColoc.pdf| Li Article ]] = Input = To run BIOP JACoP you will need - A multi-channel image ( no need to split into independent channels ) - To define channels to use - To define Thresholds, either using an [[ https://imagej.net/Auto_Threshold | Automatic Thresholding Method ]] or a User-selected manually fixed value. {F13395950, size = full} //We recommend to use the Automatic threshold methods. They will compensate for subtle changes in illumination (microscopes are not perfect), or (in case of variable expression of fluorescent proteins) the intensity from one cell to another ( you would need to define a ROI for each cell first). Nevertheless, it might fail if your images are very heterogenous (number of cells, intensities range, areas of signal...) If you can't find any auto-threshold method that gives satisfying results, then you should consider using a manually defined fixed value (Based on controls). // (IMPORTANT) We urge you to have controls (mono-stained samples, acquired the same way as the test ones, same number of channels, etc...) to verify that the defined thresholds are above the signal one can observe in an unstained sample. You will always have some crosstalk/bleedthrough, always! = Outputs = - Results Table - Output image with thresholded mask, Fluorogram (optional), Randomized image (new feature in BIOP JACoP) {F13396006, size = full} = Some Functionnnalities = == Region of Interest (ROIs) == With many ROIs in the ROI manager (defining cells for exemple), you will get an analysis per ROI. - **Crop ROIs** , generates a cropped image for each ROI. == Z-stack == - **Consider Z slices Separately** , output a single Z-stack image **BUT** the analysis is performed on each individual slice. //We recommend always starting your pilot experiment with z-stacks and assess (using this option) if the result depends on doing the analysis on 2D or 3D image and continue your acquisition campaign accordingly.// - **Set Auto Thresholds On Stack Histogram** , when using one of the [[ https://imagej.net/Auto_Threshold | automatic threshold methods ]] you can apply the auto-threshold either on individual slices or on the entire stack. WARNING: OLI: This checkbox is only valid if **Consider Z slices Separately** is checked???? {F13395937, size = full} == Set Advanced Parameters == To set size of the fluorogram {F13395918} = Installation= Please use our [[ https://c4science.ch/w/bioimaging_and_optics_platform_biop/image-processing/imagej_tools/update-site/ | PTBIOP update site ]] = Source = Oli implemented a few extra features to JACoP via a refactoring of the original JACoP code. The repository is hosted on C4Science at rJACOPB. = Macro = Macro using this tool to manage a folder and remember parameters, using [[ https://c4science.ch/w/bioimaging_and_optics_platform_biop/image-processing/imagej_tools/biop-basics/ | BIOP BASICs ]] {F3882437}

Documentation migrated to https://github.com/BIOP/ijp-jacop-b