FINDER

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by Andreas Nold and Pietro Verzelli

FINDER is a (meta)-clustering algorithm developed for Single Molecule Localization Microscopy (SMLM). It was introduced in our paper 'Unbiased choice of global clustering parameters for single-molecule localization microscopy'.

Please consider citing it:

@article{verzelli2022unbiased,
  title={Unbiased choice of global clustering parameters for single-molecule localization microscopy},
  author={Verzelli, Pietro and Nold, Andreas and Sun, Chao and Heilemann, Mike and Schuman, Erin M and Tchumatchenko, Tatjana},
  journal={Scientific Reports},
  volume={12},
  number={1},
  pages={22561},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

For some examples of its usage and all the code used to generate the experiments of the paper, you can refer to this repo.

Installation

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The simplest way to install Spektral is from PyPi:

pip install finder_smlm 

To install FINDER on Google Colab:

! pip install finder_smlm

Using FINDER

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Using FINDER is really simple. Here we provide a minimal working example in which we cluster some randomly generated data.

from finder import Finder
import numpy as np

XC = np.random.rand(100, 2) # generate random data to cluster

FD = Finder() # define the model
labels = FD.fit(XC) # fit the data, returning the labels
result = FD.selected_parameters 
print(result)

to your code, analogous to DBSCAN in the sklearn.cluster package. FINDER will choose global clustering parameters according to the overall noise levels / the robustness detected in the dataset.