LOCAN

LOCAN provides Python data structures and analysis methods to process single-molecule localization microscopy (SMLM) point-localization datasets for quantitative super-resolution microscopy studies.

Key Features:

• Data Structures: Well-defined Python data structures tailored to represent and manage SMLM point-localization data.
• Analysis Methods: A suite of analysis methods for quantitative and comprehensive examination of SMLM localization datasets.
• Customization: Flexible customization of analysis procedures to adapt analysis workflows to specific experimental needs.
• Script and computable-notebook execution: Analysis methods can be executed within scripts or computable notebooks.

Scientific Applications:

• Super-resolution microscopy: Processing and quantitative analysis of SMLM datasets for super-resolution imaging studies.
• Cellular biology: Analysis of nanoscale spatial organization in cellular biology using point-localization data.
• Neurobiology: Investigation of molecular distributions and nanoscale structures in neurobiology with SMLM data.
• Molecular interaction studies: Characterization of molecular interactions and nanoscale spatial relationships from localization data.

Methodology:

LOCAN implements Python-based data structures combined with versatile analysis methods to handle and analyze complex SMLM localization point datasets.