Anisotropic DBSCAN for 3D SMLM Data Clustering.
Pilar LörzingPhilipp SchakeMichael SchlierfPublished in: The journal of physical chemistry. B (2024)
Single-molecule localization microscopy (SMLM) advanced biological discoveries beyond the diffraction limit. Various implementations enable 3D SMLM to reconstruct volumetric cell images. Yet, the inherent anisotropic point spread function of optical microscopes often limits the localization precision in the axial direction compared to the lateral precision. Such localization anisotropy could also expand spherical cellular structures to ellipsoidal cellular structures. Structure identification, however, is often performed using DBSCAN cluster algorithms, considering an isotropic search volume. Here, we show that an anisotropic DBSCAN search volume identifies anisotropic clusters more reliably using simulated ground truth data sets. Given experimental localization precisions, we suggest optimized search parameters based on an expanded computational grid search and show an enhanced performance of anisotropic DBSCAN amidst variations in localization precision. We demonstrate the capability of anisotropic DBSCAN on experimental data and anticipate that the algorithm allows for a more rigorous identification of clusters in cells, considering the anisotropic localization precisions of astigmatism-based 3D SMLM.
Keyphrases
- single molecule
- finite element
- high resolution
- electronic health record
- deep learning
- machine learning
- big data
- single cell
- induced apoptosis
- optical coherence tomography
- high speed
- cell therapy
- stem cells
- bone marrow
- cell cycle arrest
- mesenchymal stem cells
- convolutional neural network
- data analysis
- atomic force microscopy
- cell death
- genome wide
- dna methylation