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.

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 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.