Diinamic

Diinamic combines different density-based analyses and optional thresholding to facilitate the detection of clusters and nanodomains in single-molecule localization microscopy (SMLM) data.

Key Features:

• Density-based clustering analysis: Integrates multiple density-based analytical approaches to identify molecular clusters across diverse SMLM datasets.
• Optional thresholding: Allows adjustable thresholding parameters to adapt cluster detection to different data types and experimental conditions.
• Nanodomain detection: Identifies subdomains within clusters that exhibit non-homogeneous distribution of detections.
• Robust performance on noisy data: Correctly identifies clusters of different sizes and densities even in datasets with multiple detections per fluorophore.

Scientific Applications:

• Cluster detection in SMLM data: Detects clusters of molecules in super-resolution microscopy data accounting for local density, molecule size, labeling technique, fluorophore photo-physics, and imaging conditions.
• Nanoscale biological organization analysis: Reveals organizational patterns of molecules at the nanoscale through pointillistic analysis of SMLM detection positions.
• Analysis of non-homogeneous molecular distributions: Detects subdomains within larger clusters where detections are unevenly distributed.

Methodology:

Diinamic combines different density-based analyses with optional thresholding.