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Colocalization Coefficient Calculator

Colocalization refers to the presence of two or more molecules in the same physical location, often suggesting a meaningful interaction. In cellular studies, this could mean molecules are attached to the same receptor, while in fluorescence imaging, colocalization occurs when signals from different fluorescent markers overlap within the same pixel. Evaluating colocalization helps determine whether a significant, non-random spatial correlation exists between two channels in a dual-color image.

This calculator analyzes cellular images to determine colocalization by calculating Pearson's correlation coefficient, Manders Overlap coefficient, and Manders colocalization coefficients.

Start by uploading an image
Additional Information
Pearson's Correlation Coefficient (PCC) is calculated according to the following formula:
PCC
=
∑i ( C1i − C1 ) × ( C2i − C2 )
√
∑i ( C1i − C1 ) 2 ×  ∑i ( C2i − C2 ) 2
In the above formula for PCC, C1 and C2 are the signal intensity of pixels in the first and second channels, respectively, and C1 and C2 refer to the mean intensity of the first and second channels, respectively. PCC values range from -1 to 1, where 1 corresponds to images with complete correlation, -1 for images with complete inverse correlation, and 0 for no correlation.
Manders Overlap Coefficient (MOC) is calculated according to the following formula:
MOC
=
∑i ( C1i × C2i )
√
∑i C1i2 × ∑i C2i2
In the above formula for MOC, C1 and C2 are the signal intensity of pixels in the first and second channels, respectively. MOC values range from 0 to 1.
Manders Colocalization Coefficients (MCC), M1 and M2, are calculated according to the following formulas:
M1
=
∑i C1i, colocal
C1i
M2
=
∑i C2i, colocal
C2i
For the M1 formula, C1i,colocal = C1i if C2i > 0 and C1i,colocal = 0 if C2i = 0,
and for M2 formula, C2i,colocal = C2i if C1i > 0 and C2i,colocal = 0 if C1i = 0.
This calculator attemps to determine thresholds for both channels based on the Costes et al. approach. This approach aims to automatically determine threshold values by identifying the range of pixel values for which a positive PCC is obtained. PCC is first calculated for all pixels, and then repeated while progressively lowering the each channel's intensity values along a regression line. This continues until the PCC drops to or below zero, at which point each channel's intensity values are used as the channel's thresholds.
These threshold values can then be used for the same MCC cauculations above where, for example, C1i,colocal = C1i if C2i is above it's threshold.
For additional reading, please see the following:
Feedback
Have a question or a feature request about this tool? Feel free to reach out to us and let us know! We're always looking for ways to improve!
References
This online resource may be cited as follows
MLA
"Quest Calculate™ Colocalization Coefficient Calculator." AAT Bioquest, Inc., 23 Sep. 2025, https://www.aatbio.com/tools/colocalization-coefficient-calculator.
APA
AAT Bioquest, Inc. (2025, September 23). Quest Calculate™ Colocalization Coefficient Calculator. AAT Bioquest. https://www.aatbio.com/tools/colocalization-coefficient-calculator.
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  • Click the "Choose Image(s)" button upload.
  • Choose between a single multi-color image, or two single-color images. If two single-color images are selected, they will be combined after upload.
  • Once processed, the "Images" section will show a Scatter Plot of the unique color values for all pixels in the image, a Heat Map showing the relative frequencies of the different colors in the Scatter Plot, and a composite image of the two color channels.
  • Click the "Show Overlay" button to toggle overlays on the images. The Scatter Plot and Heat Map image overlays correspond to the channel thresholds and the linear regression line. The composite image overlay highlights any colocalized areas, based on the calculated channel thresholds.