However, when an appropriate mask is used, these cell to cell correlations will be subtracted out during the analysis. This is because without a mask this plugin will find correlations at any distance, and, if say you are studying nuclear proteins, can easily correlate one nuclei to the nuclei of a neighboring cell (cells are often highly repetitive and spaced relatively evenly). The mask is very important and not using it could easily lead to undesired correlations. If you were studying cytoplasmic proteins, you would want your mask to cover the entire cytoplasm. As an example, say you are studying correlation between two nuclear proteins, then you would want your mask to cover the nucles, which could be created easily using a DAPI or Hoechst stain (the mask itself does not need to be generated from either image your are trying to correlate). This mask should contain all possible localizations for that stain/dye, or it should be a mask of localization for your null hypothesis ( i.e if you hypothesize a protein is localized to the mitochondria, you would want your mask to encompass the entire cell). To get the best possible results, you will want to create and save a segmented mask for one of your images. Randomization mask: An example of an appropriate mask for analyzing cross-correlation of cytoplasmic proteins(right), generated from an actin stain (left). Prepare a mask for the pixel randomization Measure and subtract the mean background for both images (Process > Math > Subtract).Convert images to 32-bit (Image > Type > 32-bit).
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