# Colocalization

- Open ImageJ Software
- Prior to performing colocalisation analysis save cell images as experiment files
- Open an experiment file in ImageJ by File->Open->Select File
- Select File and create composite by Image->color>create composite
- Select File and split by Image->color->Split
- Select each image and change to 8-bit greyscale by Image->type->8bit
- Assess colocalisation by Plugins->Analyze->Colocalisation Finder

For a z-stack

- Open image and change to 8-bit (Image->Type->8 bit)
- Flatten z-stacks by going Image->Z-Project and selecting the range of images (typically the first x images are one color and then the last x images are the next color)
- Select max intensity from the pulldown list
- Repeat for the other images

**Assessment**

**Rr**

This is the Pearson’s correlation coefficient. Zero-zero pixels are not included in this calculation.

This is a popular method of quantifying correlation in many fields of research from psychology to economics. In many forms of correlation analysis the values for Pearson’s will range from 1 to -1. A value of 1 represents perfect correlation; -1 represents perfect exclusion and zero represents random localisation. However, this is not the case for images. While perfect correlation gives a value of 1, perfect exclusion does not give a value of -1. Low (close to zero) and negative values for Pearson’s correlation coefficient for fluorescent images can be difficult to interpret. However, a value close to 1 does indicate reliable colocalisation.

**R**

This is Mander’s Overlap coefficient. This is easier than the Pearson’s coefficient to comprehend. It ranges between 1 and zero with 1 being high-colocalisation, zero being low. However, the number of objects in both channel of the image has to be more or less equal.

**Slope**
Slope of the line represents the "red to green" ratio, as a measure of both image intensity and colocalization. Ideally, the slope should be equal to 1 (y=x), however, it is more likely that one of the immunostained colors will be darker than the other causing the slope to tend more towards that axis. Good colocalization will give a scatterplot which is best fit by a linear curve, where the slope of this curve is representative of the ratio of immunostained colors.

See following link for more information:
[1]