Spectre-v5.0.2.1802

In this version:

• Docker image tagging schema got more flexible. It will be easier to track changes now, with less updates of service versions.
• Message after successful analysis scheduling now displays success instead of error.
• Aspects are centered on the screen, instead of wide.
• Finished analyses are normalized with other views.

Spectre-v5.0.1.1785

This release reduces repository structure to contain only web client and docker-compose for deployment. It supports generic workers API maintained through master API node.

Spectre-v2.24.7.1118

Release notes

Web Client

• Angular-based web client has been added
• It has been updated to version 4
• interactive visualization of each spectrum
• interactive visualization of each mass channel
• interactive visualization of DiviK result

Web API

• Web API has been extended to provide information about preparations
• It has been cleaned up

Algorithms

GMM

• added
• splitters implementation is still ongoing
• dynamic programming based initialization condition is not yet approached

DiviK

• awaits implementation of peak detection method

Genetic training set selection

• genetic algorithm has been implemented
• dataset splitting has been implemented
• fitness function implementation is ongoing

DiviK-v1.0.359

This version contains simple WPF client for DiviK algorithm.

It requires MATLAB Compiler Runtime in version 9.1.

More details are included in the manual embedded in the archive.

Spectre

Spectre is a versatile tool used for analysis of MALDI-MSI data sets.

For the sake of simplicity, the toolset provided is available to be used
through interfacing with web application, which is currently a work-in-progress.

In order to build and run the application, please refer to the
installation section.

About

The project is currently in its early stage. However, it comprises the
implementation of our own spectra modelling based on Gaussian Mixture Models,
and Divisive IK-means algorithm for unsupervised segmentation, which can be
used for efficient dataset compression as well as for knowledge discovery.
Aformentioned algorithms have already been published and links refering
have been enclosed under references section.

Also, several classification and clusterization methods will be provided soon,
along with supporting statistics.

Install

Please refer to docs.

Exemplary usage

Please refer to docs.

How to contribute?

Please contact us by an e-mail. We will
answer you in details.

Environment

Please refer to docs.

References

This software is part of contribution made by Data Mining Group of Silesian University of Technology, rest of which is published here.

• Marczyk M, Polanska J, Polanski A: Comparison of Algorithms for Profile-Based
  Alignment of Low Resolution MALDI-ToF Spectra. In Advances in Intelligent
  Systems and Computing, Vol. 242 of Man-Machine Interactions 3, Gruca A,
  Czachorski T, Kozielski S, editors. Springer Berlin Heidelberg 2014, p. 193-201
  (ISBN: 978-3-319-02308-3), ICMMI 2013, 22-25.10.2013 Brenna, Poland
• 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
• A. Polanski, M. Marczyk, M. Pietrowska, P. Widlak and J. Polanska, "Signal
  partitioning algorithm for highly efficient Gaussian mixture modeling in mass
  spectrometry," PloS one, vol. 10, no. 7, p. e0134256, 2015