Statistical validation of triple colocalization analysis

Abstract

In the last decades, colocalization analysis of fluorescently tagged biomolecules has proven to be a powerful approach to studying functional relationships between these biomolecules. However, in many cases, to give this analysis a biological meaning, colocalization coefficients must be tested statistically, comparing them with the colocalization expected by chance. Aim: It addressed the statistical significance of triple colocalization to distinguish real triple colocalization and classify different triple signal scenarios. Methods: we use biological and generated images of triple signal scenarios to contrast seven independent statistical facts with independent statistical tests. Three of these tests correspond to pairwise relationships (double scrambling tests), and the others correspond to triple relationships: single scrambling tests (red, green, and blue scrambling) and the triple scrambling test. The analysis and methodology proposed can be reproduced using the application developed in our laboratory. Results: In the study approach, we found true triple relationships ignored by using traditional methods of computing the statistical significance, while we could reinterpret cases of not significant triple colocalization wrongly considered as significant by traditional methods. Discussion: single scrambling tests can reveal significant triple colocalization for low levels of triple co-occurrence, even when all pairwise relationships were exclusion relationships. Moreover, on the other hand, single scrambling tests can reveal the absence of a significant triple colocalization for high levels of triple co-occurrence, even when all pairwise relationships were significant colocalization. Conclusion: all scrambling tests are useful to classify a specific scenario of a triple relationship. Dynamics like mitosis can be distinguished into their phases by triple signal relationships using these 7 independent statistical tests.