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Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2020-20-6-807-814
METHOD OF JOINT CLUSTERING IN NETWORK AND CORRELATION SPACES
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Article in Russian
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Abstract
For citation:
Gainullina A.N., Artyomov M., Sergushichev A.A. Method of joint clustering in network and correlation spaces. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2020, vol. 20, no. 6, pp. 807-814 (in Russian). doi: 10.17586/2226-1494-2020-20-6-807-814
Abstract
Subject of Research. The joint clustering method in network and correlation context is designed to identify active modules in metabolic graphs based on transcriptomic data represented by a large number of samples. The active modules obtained by this method describe the dynamic metabolic regulation in all samples of the analyzed dataset. The paper presents modifications of the proposed method for application on real data. Method. For results stability study the modified method was repeatedly run on real data with small variations of the initial parameters. For result analysis, several metrics were formulated that display modules similarity and representation under various start-up conditions. Main Results. The analysis results are sufficiently robust. For the most modules, their profiles are detected well in the noisy data, and the most genes are also preserved. Practical Relevance. The results of the presented study have shown that the modified method analyzes successfully real data by producing active modules that are stable and easy in interpretation.
Keywords: clustering, correlation, graphs, metabolic networks, gene expression, transcriptomic data
Acknowledgements. This work was supported by the Government of the Russian Federation, Investigation Research Grant 08-08.
References
Acknowledgements. This work was supported by the Government of the Russian Federation, Investigation Research Grant 08-08.
References
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