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NearestNeighborSML

Investigation of nearest neighbor distributions of single molecule localizations (SML) in cells. The analysis is executed in a jupyter notebook (NearestNeighbor1234). The input files are hdf5 files that contain a column "com_x" for the x-localizations and a column "com_y" for the y-localizations of clusters, created in Picasso after the DBSCAN. The notebook requires four targets and two conditions. Since the names are input parameters, the notebooks can easily be applied to other measurements with different targets and measurement conditions.

NearestNeighbor1234

This analysis compares four measured target (e.g. FGFR1/2/3/4) localization distributions between not active and active cells by using nearest neighbor distances:

  • Calculate the nearest neighbor distances
  • Calculate the percentage of nearest neighbor distances less or equal to a threshold
  • Determine the nearest neighbor types

Requirements

We recommend to run the notebooks in a virtual environment, for example via Anaconda/Miniconda with its powerful package manager.

Set up the environment in conda & start the notebooks

  1. open the anaconda prompt and create a new virtual environment: conda create --name NeighborDistributions python=3.7
  2. activate the environment: conda activate NeighborDistributions
  3. install the required packages (see below)
  4. save the notebooks in a directory
  5. navigate to the directory containing the notebooks in the anaconda prompt
  6. run the notebooks with the command: jupyter notebook

Install required packages

conda install -c anaconda h5py
conda install -c anaconda ipywidgets
conda install -c anaconda numpy
conda install -c anaconda pandas
conda install -c plotly plotly
conda install -c conda-forge tqdm

Install the following packages to get a clearer notebook:
conda install -c conda-forge jupyter_contrib_nbextensions
conda install -c conda-forge jupyter_nbextensions_configurator
Navigate to Nbextensions in the notebook process and check "Collapsible Headings"

Tested versions: python: 3.7.6 h5py: 2.10.0 ipywidgets: 7.5.1 numpy: 1.18.1 pandas: 1.0.0 plotly: 4.5.0 tqdm: 4.42.0

Literature

FGFR Methods Paper: M. S. Schröder, M.-L. I. E. Harwardt, J. V. Rahm, Y. Li, P. Freund, M. S. Dietz, M. Heilemann, Imaging the fibroblast growth factor receptor network on the plasma membrane with DNA-assisted single-molecule super-resolution microscopy, Methods in Extracellular Vesicles and Mimetics 2020, DOI: https://doi.org/10.1016/j.ymeth.2020.05.004

Picasso: J. Schnitzbauer, M. T. Strauss, T. Schlichthaerle, F. Schueder, R. Jungmann, Super-Resolution Microscopy with DNA-PAINT, Nature Protocols 2017, 12, 1198-1228; DOI: https://doi.org/10.1038/nprot.2017.024

Web

For more information visit: https://www.uni-frankfurt.de/43272324/Welcome_to_the_homepage_of_the_Heilemann_Group