Nested Sampling approach for the multicomponent fitting of data from segmented super-resolution images (NS_multicomp)
We developed a Bayesian approach based on the Nested Sampling algorithm for the multicomponent fitting of data obtained from the segmentation of super-resolution image. The algorithm provides an estimation of the Bayesian evidence to perform model ranking, calculates the model best-fit parameters and their confidence interval. Please refer to the article below for further information. A simulated dataset of 300 datapoints with ground truth values K=5 and decreasing weights 𝜶=(0.3333,0.2667,0.2000,0.1333,0.0667) is provided for testing. The calibration function used to generate the data is a lognormal distribution with µ=3.3491 and 𝜎=0.8462.
The R and Matlab code was developed by T. Košuta and C. Manzo in 2019 as part of a research project of the QuBI lab carried out at the Faculty of Science and Technology of the Universitat de Vic - Universitat Central de Catalunya (Spain) in collaboration with F. Cella Zanacchi.
Please cite the publication below in all your documents and manuscripts that made use of the software included in this repository.
@article{kosuta2020pccp,
title={Bayesian analysis of data from segmented super-resolution images for quantifying protein clustering},
author={Ko\v{s}uta, Tina and Cullel-Dalmau, Marta and Cella Zanacchi, Francesca and Manzo, Carlo},
journal={Phys. Chem. Chem. Phys.},
volume={22},
pages={1107--1114},
year={2020},
doi={10.1039/c9cp05616e},
publisher={The Royal Society of Chemistry},
url ={http://dx.doi.org/10.1039/C9CP05616E}
}
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