  Involved in mitotic condensation, interaction of transcriptional regulatory
elements or isolation of structural domains, understanding loop formation is
becoming a paradigm in the deciphering of chromatin architecture and its
functional role. Despite the emergence of increasingly powerful genome
visualization techniques, the high variability in cell populations and the
randomness of conformations still make loop detection a challenge. We introduce
a new approach for determining the presence and frequency of loops in a
collection of experimental conformations obtained by multiplexed
super-resolution imaging. Based on a spectral approach, in conjunction with
neural networks, this method offers a powerful tool to detect loops in large
experimental data sets, both at the population and single cell level. The
method's performance is confirmed by applying it to recently published
experimental data, where it provides a detailed and statistically quantified
description of the global architecture of the chromosomal region under study.
