Modules

Measure object colocalisation

Calculates colocalisation of two input images individually for each object.

Description

Calculates colocalisation of two input images individually for each object. Measurements for each object only consider pixels within that object. All measurements are associated with the relevant object. Colocalisation analysis has many potential pitfalls, so users are advised to read the Fiji Colocalization analysis page and/or the Dunn et al 2011 review.

All calculations are performed using the Coloc2 plugin.

Parameters

ParameterDescription
Input objectsObjects for which colocalisation will be measured. For each object, colocalisation will be independently measured for the pixels coincident with the object's coordinates. Measurements will be associated with the corresponding object.
Input image 1First image for which colocalisation will be calculated.
Input image 2Second image for which colocalisation will be calculated.
Thresholding modeControls 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 implementationControls whether PCC should be calculated using the classic algorithm or using the Coloc2-default "fast" method.
Measure Kendall's Rank CorrelationWhen 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 ICQWhen 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' CorrelationWhen 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 PCCWhen 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 CorrelationWhen 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.