Difference between revisions of "Colocalization Test"

(Link to MBF documentation)
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| latest version        = june 2009
 
| latest version        = june 2009
 
| website                = [http://www.macbiophotonics.ca/imagej/colour_analysis.htm#6.2%20Colocalisation%20Test MBF ImageJ manual]
 
| website                = [http://www.macbiophotonics.ca/imagej/colour_analysis.htm#6.2%20Colocalisation%20Test MBF ImageJ manual]
| status                = testing, likely to be stable enough for real use,
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| status                = Depreciated, use [[Coloc_2]] instead.
 
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== Documentation ==
 
== Documentation ==
 +
 +
===Note: this plugin is now depreciated. You should use the newer Coloc_2 plugin instead===
  
 
See the great documentation for this plugin at [http://www.macbiophotonics.ca/imagej/colour_analysis.htm#6.2%20Colocalisation%20Test MBF ImageJ manual page]
 
See the great documentation for this plugin at [http://www.macbiophotonics.ca/imagej/colour_analysis.htm#6.2%20Colocalisation%20Test MBF ImageJ manual page]

Revision as of 03:07, 20 October 2011

Colocalization Test (ImageJ - Fiji)
Author Tony Collins (and others?)
Maintainer Dan White
File Colocalization.jar
Source Colocalisation_Test.java on gitweb , modified from MBF
Latest version june 2009
Development status Depreciated, use Coloc_2 instead.
Website MBF ImageJ manual



Purpose

Performs one of a set of three statistical tests, comparing the Persons correlation coefficient of 2 colour channels in the real image data against the same image data with one of the colour channels scrambled or spatially shifted, repeated a number of times.

Documentation

Note: this plugin is now depreciated. You should use the newer Coloc_2 plugin instead

See the great documentation for this plugin at MBF ImageJ manual page

This tells you if the colocalization that you measure with other plugins, such as Colocalization Threshold is better than random chance. For a busy image with lots of signal in both chanels and very little area with no signal, there will be lots of random overlap, and this is easy to confuse with real colocalization with some biological meaning. It gives a P vaule (not a p-value) where 1 means all the randomised images had worse correlation than the real images, as you would expect for a real colocalised signel. A P value less than 0.95 is lower than the usual 95% statistical confidence limit.

The methods implemented are Costes (most robust, use 100 iterations), van Steensel and Fay. These differ in how the randomised images are generated from the real image.

See also the Colocalization Analysis tutorial and the original documentation for this plugin on the MacBioPhotonics site.