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No More Noise
It is more than likely that image noise is corrupting your correlation analysis, but this does no longer have to be the case!
No More Noise has the solution to deal with noise. We have developed a method to eliminate the noise contribution in correlation measurements by acquiring replicate datasets. The method is called RBNCC for Replicate Based Noise Corrected Correlation. A patent for RBNCC has been granted in the United States and several others are pending.
Advantages using RBNCC:
You get the correct answer.
You can make measurements from very poor images.
Your imaging time can be shortened and photobleaching therefore reduced.
Shorter imaging times means that the cells move less during image acquisition.
You can get more images out of your fluorophores making longer time series possible.
Phototoxicity is reduced which is important for live cell imaging.
A detailed description of RBNCC can be found at:
Replicate Based Noise Corrected Correlation for Accurate Measurements of Colocalization. Journal of Microscopy 230, 121-133 (2008) Adler, J., Pagakis, S. & Parmryd, I.
Mathematical proof of why and how RBNCC works can be found in:
Analysis of bias in the apparent correlation coefficient between image pairs corrupted by severe noise. Journal of Mathematical Imaging and Vision 37, 204-219 (2010) Bergholm, F., Adler, J. & Parmryd, I. Journal of Mathematical Imaging and Vision
A comparison of correlation coefficients argues that three correlation coefficients, due to their inadequacy, should be abandoned: Quantifying colocalization by correlation: the Person correlation coefficient is superior to the Mander's overlap coefficient. Cytometry Part A (2010) Adler, J. & Parmryd, I. Cytometry Part A
The step-by-step use of RBNCC for quantitative colocalization analysis is described in: Visualization and Analysis of Vascular Receptors Using Confocal Laser Scanning Microscopy and Fluorescent Ligands (2012) Daly, C. J., Parmryd, I. & McGrath, J. C. Methods in Molecular Biology
Step-by-step protocols for quantitative analysis of colocalization divided into co-occurrence and correlation analyses including RBNCC can be found in:
Colocalization Analysis in Fluorescence Microscopy (2012) Adler, J. & Parmryd, I. Methods in Molecular Biology
A detailed demonstration of why only pixels containing both fluorophores should be included in a correlation analysis.
Quantifying colocalization: thesholding, void voxels and the Hcoef. PLoS One 9:e111983 Adler, J. & Parmryd, I. (2014) PLoS One
Noise will give a always result in the underlying correlation being underestimated and since noise is unavoidable, you simply will not get the correct answer out of your correlation analysis unless RBNCC is employed.
September 2011. RBNCC part of the Colocalization Analyzer option in Huygen's software of Scientific Volume Imaging.
January 2011. No More Noise signs agreement with Scientific Volume Imaging to use RBNCC.
April 2010. Article comparing colocalisation coefficients.
March 2010. Article proving that RBNCC is sound from a mathematical perspective.
September 2008. Review on RBNCC in Microscopy and Analysis.
July 2008. Note on RBNCC in Microcopy and Analysis.
April 2008. Article describing RBNCC in Journal of Microscopy.