divik 2.3.1b0

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 one of many scenarios
2. kmeans - runs K-means
3. linkage - runs agglomerative clustering
4. inspect - visualizes DiviK result
5. visualize - generates .png file with visualization of clusters for 2D maps
6. 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, since it requires MATLAB Compiler Runtime and more dependencies.

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:2.1.8

Python package

Prerequisites for installation of base package:

• Python 3.5

These are required for using divik application and GMM-based filtering:

• MATLAB Compiler Runtime, version 2016b or newer, installed to default path
• compiled package with legacy code

Installation process may be clearer with insight into Docker images used for application deployment:

• python_mcr image - installs MCR r2016b onto Python 3.5 image
• python_msi image - installs compiled legacy code onto MCR image
• divik image - installs DiviK software onto legacy code image

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==2.1.8

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