  Super-resolution imaging techniques have largely improved our capabilities to
visualize nanometric structures in biological systems. Their application
further enables one to potentially quantitate relevant parameters to determine
the molecular organization and stoichiometry in cells. However, the inherently
stochastic nature of the fluorescence emission and labeling strategies imposes
the use of dedicated methods to accurately measure these parameters. Here, we
describe a Bayesian approach to precisely quantitate the relative abundance of
molecular oligomers from segmented images. The distribution of proxies for the
number of molecules in a cluster -- such as the number of localizations or the
fluorescence intensity -- is fitted via a nested sampling algorithm to compare
mixture models of increasing complexity and determine the optimal number of
mixture components and their weights. We test the performance of the algorithm
on {\it in silico} data as a function of the number of data points, threshold,
and distribution shape. We compare these results to those obtained with other
statistical methods, showing the improved performance of our approach. Our
method provides a robust tool for model selection in fitting data extracted
from fluorescence imaging, thus improving the precision of parameter
determination. Importantly, the largest benefit of this method occurs for
small-statistics or incomplete datasets, enabling accurate analysis at the
single image level. We further present the results of its application to
experimental data obtained from the super-resolution imaging of dynein in HeLa
cells, confirming the presence of a mixed population of cytoplasmatic single
motors and higher-order structures.
