Difference between revisions of "Colocalization"
(Created page with '#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 Fi...') |
Davebridges (Talk | contribs) (updated protocol) |
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#Select File and split by Image->color->Split | #Select File and split by Image->color->Split | ||
#Select each image and change to 8-bit greyscale by Image->type->8bit | #Select each image and change to 8-bit greyscale by Image->type->8bit | ||
| − | #Assess colocalisation by Plugins->Colocalisation | + | #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''' | '''Assessment''' | ||
Latest revision as of 16:53, 16 March 2011
- 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]
