LocAlization Microscopy Analyzer (LAMA)
© 2015-2017 Sebastian Malkusch
Single-molecule localization microscopy (SMLM) enables studies on the molecular organization of proteins in the cellular context. With LocAlization Microscopy Analyzer (LAMA), we offer a comprehensive software tool that extracts quantitative information from single-molecule super-resolution imaging data (Malkusch, Heilemann 2016). LAMA allows the characterization of cellular features by size, shape, density and stoichiometry. LAMA uses coordinate-based algorithms such as density-based spatial clustering of applications with noise (DBSCAN) (Ester et al. 1996) and coordinate-based colocalization (CBC) (Malkusch et al. 2012). Additional tools include an automated bead detection for multi-channel registration (Zessin et al. 2013) and a nearest-neighbor analysis (NeNA) to estimate the localization precision (Endesfelder, Heilemann 2014). LAMA is platform independent (developed and tested under Windows, OSX, Ubuntu, openSUSE), as it is written in Python, Cython and C. It is published open source under GNU publishing license (GPL) version 3 or later and comes free of charge.
(latest version: 17.07)
Instructions and Documentation
- LAMA manual on localization precision
- LAMA manual on Ripley's K-function
- LAMA manual on cluster analysis
- LAMA manual on registration
- LAMA manual an data set conversion
- test data TNFR1.tif
Ester, M., Kriegel, H.-P., Sander & J., Xu, X. (1996): A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. AAAI Press.
Malkusch, S., Endesfelder, U., Mondry, J., Gelléri, M., Verveer, P. J. & Heilemann, M. (2012) Coordinate-based colocalization analysis of single-molecule localization microscopy data. Histochemistry and cell biology. 137 (1), 1–10.
Last Update: 2017-08-16