RT Journal Article
SR Electronic
T1 Unbiased choice of global clustering parameters in single-molecule localization microscopy
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.02.22.432198
DO 10.1101/2021.02.22.432198
A1 Nold, Andreas
A1 Sun, Chao
A1 Heilemann, Mike
A1 Schuman, Erin M.
A1 Tchumatchenko, Tatjana
YR 2021
UL http://biorxiv.org/content/early/2021/02/22/2021.02.22.432198.abstract
AB Single-molecule localization microscopy resolves objects below the diffraction limit of light via sparse, stochastic detection of target molecules. Single molecules appear as clustered detection events after image reconstruction. However, identification of clusters of localizations is often complicated by the spatial proximity of target molecules and by background noise. Clustering results of existing algorithms often depend on user-generated training data or user-selected parameters, which can lead to unintentional clustering errors. Here we suggest an unbiased algorithm (FINDER) based on adaptive global parameter selection and demonstrate that the algorithm is robust to noise inclusion and target molecule density. We benchmark the most common density based clustering algorithms in test scenarios which we designed based on experimental datasets. We show that FINDER can keep the number of false positive inclusions low while also maintaining a low number of false negative detections in densely populated regions.Competing Interest StatementThe authors have declared no competing interest.