Localization Analyzer for Nanoscale Distributions (LAND)

LAND is a software package for quantitative analysis of single molecule localization microscopy (SMLM) data. It has been specifically designed for the evaluation of large sample sizes and data with high emitter densities.

• Analysis of 2D and 3D SMLM data
• Algorithms:
  • Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
  • Voronoi-based cluster analysis
  • Ripley's function
  • Radial Distribution Function (RDF)
  • Nearest Neighbor analysis (NN)
  • Distance analysis
• Algorithms for quantifying the conformation and texture of the nuclear nanostructure (SMLM-ConText)
• Visualization of data
• User Interface with batch processing capabilities

Documentation

Detailed instructions on how to use the software including examples can be found in the wiki.

Getting Started

The follwong sections describe how to get a copy of the software and how to install it on your local machine. Detailed instructions on how to use the software including examples can be found in the wiki.

Requirements

• MATLAB R2014b or newer

  • Statistics and Machine Learning Toolbox
  • Image Processing Toolbox

• (optional, but highly recommended for a much faster computation) ataiya/kdtree

• multiWaitbar (a copy is included in this distribution)

At least 8 GByte RAM are recommended.

Installation

• download the software package from https://github.com/Jan-NM/LAND/releases
• extract LAND-master.zip
• copy the generated LAND-master directory into your local MATLAB working directory
• to use LAND, right click on LAND-master in MATLAB's current folder panel, go to Add to Path and click on Selected Folders and Subfolders

LAND can be used via the command window or by opening a user interface. To open the user interface type startClusterAnalysis in the command window. Detailed instructions on how to use the software including examples can be found in the wiki.

Recommended Installation: ataiya/kdtree

• configure a MEX environment by downloading and installing a Matlab-supported C++ compiler
• open "...\LAND-master\utilities\ataiya_kdtree..." in Matlab's current folder panel
• execute the following commands in MATLAB's command window to setup the mex environment

mex -setup C++
mex kdtree_build.cpp
mex kdtree_ball_query.cpp
mex kdtree_delete.cpp

Input Data Format

Input data should be in contained in a numeric nx12 .mat file. Each row (n) should correspond to a single molecule signal. The columns should be ordered in the following way:

• column 2/3/11 = x/y/z-position
• column 4/5/12 = x/y/z-localization precision
• column 9 = frame number

The remaining columns can be filled with zeros. If the data does not contain any localization precision and z-position (i.e. 2D data), these columns should be also filled with zeros.

Contributing, Bug Reports and Feature Requests

You are encouraged to contribute to this project. Please use the issue tracker on GitHub to report bugs or to open feature requests.

License

LAND is licensed under the GNU GPL - see the LICENSE file for details. LAND includes multiWaitbar, which comes with a separate license.

Included Third-Party Software Packages

• multiWaitbar

• ataiya/kdtree: Andrea Tagliasacchi (2020). ataiya/kdtree (https://www.github.com/ataiya/kdtree), GitHub. Retrieved April 5, 2020.

Cite As

If you are using this code in one of your publications, please cite this paper:

Neumann et al., "Nanoscale distribution of TLR4 on primary human macrophages stimulated with LPS and ATI", (2019).

Notes

DBSCAN is based on the paper: Ester at al., "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise", (1996).