Divisive iK-means algorithm implementation
Project description
divik
Python implementation of Divisive iK-means (DiviK) algorithm.
Tools within this package
This section will be further developed soon.
divik
- runs DiviK in GAP-only scenariodunn-divik
- runs DiviK in GAP & Dunn scenariokmeans
- runs K-means with GAP statisticlinkage
- runs agglomerative clusteringinspect
- visualizes DiviK resultvisualize
- generates.png
file with visualization of clusters for 2D mapsspectral
- generates spectral embedding of a dataset
Installation
Docker
The recommended way to use this software is through
Docker. This is the most convenient way, if you want
to use divik
application.
To install latest stable version use:
docker pull gmrukwa/divik
To install specific version, you can specify it in the command, e.g.:
docker pull gmrukwa/divik:2.4.8
Python package
Prerequisites for installation of base package:
- Python 3.6 / 3.7
- compiler capable of compiling the native C code and OpenMP support
Installation of OpenMP for Ubuntu / Debian
You should have it already installed with GCC compiler, but if somehow not, try the following:
sudo apt-get install libgomp1
Installation of OpenMP for Mac
OpenMP is available as part of LLVM. You may need to install in with:
brew install llvm libomp
DiviK Installation
Having prerequisites installed, one can install latest base version of the package:
pip install divik
or any stable tagged version, e.g.:
pip install divik==2.4.8
If you want to have compatibility with
gin-config
, you can install
necessary extras with:
pip install divik[gin]
Note: Remember about \
before [
and ]
in zsh
shell.
Known Issues
Mac OS & Numba
Certain code compilation is not supported for Mac OS, as it started to
freeze. Therefore it is disabled and slower. You can try yourself by
installation of numba
package, but I had no luck up to this point.
References
This software is part of contribution made by Data Mining Group of Silesian University of Technology, rest of which is published here.
- P. Widlak, G. Mrukwa, M. Kalinowska, M. Pietrowska, M. Chekan, J. Wierzgon, M. Gawin, G. Drazek and J. Polanska, "Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium - application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data," Proteomics, vol. 16, no. 11-12, pp. 1613-21, 2016
- M. Pietrowska, H. C. Diehl, G. Mrukwa, M. Kalinowska-Herok, M. Gawin, M. Chekan, J. Elm, G. Drazek, A. Krawczyk, D. Lange, H. E. Meyer, J. Polanska, C. Henkel, P. Widlak, "Molecular profiles of thyroid cancer subtypes: Classification based on features of tissue revealed by mass spectrometry imaging," Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 2016
- G. Mrukwa, G. Drazek, M. Pietrowska, P. Widlak and J. Polanska, "A Novel Divisive iK-Means Algorithm with Region-Driven Feature Selection as a Tool for Automated Detection of Tumour Heterogeneity in MALDI IMS Experiments," in International Conference on Bioinformatics and Biomedical Engineering, 2016
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