divik 2.5.7

Divisive iK-means algorithm implementation

divik

Python implementation of Divisive iK-means (DiviK) algorithm.

Tools within this package

This section will be further developed soon.

1. divik - runs DiviK in GAP-only scenario
2. dunn-divik - runs DiviK in GAP & Dunn scenario
3. kmeans - runs K-means with GAP statistic
4. linkage - runs agglomerative clustering
5. inspect - visualizes DiviK result
6. visualize - generates .png file with visualization of clusters for 2D maps
7. spectral - 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:2.5.7

Python package

Prerequisites for installation of base package:

• Python 3.6 / 3.7 / 3.8
• 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==2.5.7

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.

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