# Difference between revisions of "Colocalization Test"

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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. | 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. | + | The methods implemented are White Noise Image Approximation of Costes' image randomisation (most robust, use 100 iterations), van Steensel and Fay (image shift methods). These differ in how the randomised images are generated from the real image or from white noise. None of them implement the Costes image randomisation test as described in his paper. [[Coloc_2]] does though. |

See also the [[Colocalization Analysis]] tutorial and the original documentation for this plugin on the [http://www.macbiophotonics.ca/imagej/colour_analysis.htm MacBioPhotonics site]. | See also the [[Colocalization Analysis]] tutorial and the original documentation for this plugin on the [http://www.macbiophotonics.ca/imagej/colour_analysis.htm MacBioPhotonics site]. |

## Revision as of 08:15, 28 November 2011

Colocalization Test (ImageJ - Fiji) | |
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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 | Deprecated, 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 White Noise Image Approximation of Costes' image randomisation (most robust, use 100 iterations), van Steensel and Fay (image shift methods). These differ in how the randomised images are generated from the real image or from white noise. None of them implement the Costes image randomisation test as described in his paper. Coloc_2 does though.

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