Measure image colocalisation

Calculates colocalisation of two input images.

Description

Calculates colocalisation of two input images. All measurements are associated with the first input image. Measurements can be restricted to specific region using image or object-based masking. To measure colocalisation on an object-by-object basis please use the "Measure object colocalisation" module.

All calculations are performed using the Coloc2 plugin.

Parameters

Parameter	Description
Input image 1	First image for which colocalisation will be calculated. Measurements will be associated with this image.
Input image 2	Second image for which colocalisation will be calculated.
Masking mode	Controls which regions of the image will be evaluated for colocalisation: "Mask using image" A binary image (specified using "Mask image") determines which pixels are evaluated for colocalisation. The "Image mask logic" parameter controls whether the pixels to be evaluated are black (0 intensity) or white (255 intensity). "Mask using objects" An object collection (specified using "Input objects") determines which pixels are evaluated for colocalisation. The "Object mask logic" parameter controls whether the pixels to be evaluated are inside or outside the objects. "None" No mask will be applied. All pixels in the image will be evaluated for colocalisation.
Mask image	If "Masking mode" is set to "Mask using image", this is the binary image which will control the pixels to be evaluated for colocalisation. The "Image mask logic" parameter controls whether the pixels to be evaluated are black (0 intensity) or white (255 intensity).
Image mask logic	Controls whether colocalisation is measured for pixels coincident with black (0 intensity) or white (255 intensity) pixels in the mask image.
Input objects	If "Masking mode" is set to "Mask using objects", this is the object collection which will control the pixels to be evaluated for colocalisation. The "Object mask logic" parameter controls whether the pixels to be evaluated are inside or outside the objects.
Object mask logic	Controls whether colocalisation is measured for pixels inside or outside objects in the masking object collection.
Thresholding mode	Controls how the thresholds for measurements such as Manders' are set: "Bisection (correlation)" A faster method to calculate thresholds than the Costes approach. "Costes (correlation)" The "standard" method to calculate thresholds for Manders' colocalisation measures. This approach sets the thresholds for the two input images such that the pixels with intensities lower than their respective thresholds don't have any statistical correlation (i.e. have PCC values less than or equal to 0). This is based on Costes' 2004 paper (Costes et al., Biophys. J. 86 (2004) 3993–4003. "Image measurements" Thresholds for each image will be set equal to measurements associated with each object. "Manual" Threshold values are manually set from user-defined values ("Threshold (C1)" and "Threshold (C2)" parameters). "None" No threshold is set. In this instance, Manders' metrics will only be calculated above zero intensity rather than both above zero and above the thresholds. Similarly, Pearson's correlation coefficients will only be calculated for the entire region (after masking) rather than also for above and below the thresholds.
Image measurement (C1)	If "Thresholding mode" is set to "Image measurements", this is the measurement associated with "Input image 1" that will be applied to the first image.
Image measurement (C2)	If "Thresholding mode" is set to "Image measurements", this is the measurement associated with "Input image 2" that will be applied to the second image.
Threshold (C1)	If "Thresholding mode" is set to "Manual", this is the threshold that will be applied to the first image.
Threshold (C2)	If "Thresholding mode" is set to "Manual", this is the threshold that will be applied to the second image.
PCC implementation	Controls whether PCC should be calculated using the classic algorithm or using the Coloc2-default "fast" method.
Measure Kendall's Rank Correlation	When selected, Kendall's rank correlation will be calculated. This works in a similar manner to Pearson's PCC, except it's calculated on ranked data rather than raw pixel intensities.
Measure Li's ICQ	When selected, Li's ICQ (intensity correlation quotient) will be calculated. This measure reports the frequency with which both corresponding pixels for both channels are either both above or both below their respective means. Values are scaled into the range -0.5 to +0.5, with values below 0 corresponding to anti-correlation and values above 0 indicating correlation.
Measure Manders' Correlation	When selected, Manders' M1 and M2 coefficients will be calculated. "Proportional to the amount of fluorescence of the colocalizing pixels or voxels in each colour channel. You can get more details in Manders et al. Values range from 0 to 1, expressing the fraction of intensity in a channel that is located in pixels where there is above zero (or threshold) intensity in the other colour channel." Description taken from https://imagej.net/imaging/colocalization-analysis
Measure PCC	When selected, Pearson's Correlation Coefficient (PCC) will be calculated. "It is not sensitive to differences in mean signal intensities or range, or a zero offset between the two components. The result is +1 for perfect correlation, 0 for no correlation, and -1 for perfect anti-correlation. Noise makes the value closer to 0 than it should be." Description taken from https://imagej.net/imaging/colocalization-analysis
Measure Spearman's Rank Correlation	When selected, Spearman's rank correlation will be calculated. Spearman's rho is calculated in a similar manner to Pearson's PCC, except the image intensities are replaced by their respective rank. Spearman's correlation works with monotonic relationships. As with PCC, values are in the range -1 to +1.