METHOD OF JOINT CLUSTERING IN NETWORK AND CORRELATION SPACES
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 i...
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| Format: | Article |
| Language: | English |
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Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)
2020-12-01
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| Series: | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| Subjects: | |
| Online Access: | https://ntv.ifmo.ru/file/article/20004.pdf |
| _version_ | 1818945736072495104 |
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| author | Anastasiia N. Gainullina Maxim Artyomov Alexey A. Sergushichev |
| author_facet | Anastasiia N. Gainullina Maxim Artyomov Alexey A. Sergushichev |
| author_sort | Anastasiia N. Gainullina |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T08:03:51Z |
| format | Article |
| id | doaj.art-ffd57137ff254abda0479699b86f84a5 |
| institution | Directory Open Access Journal |
| issn | 2226-1494 2500-0373 |
| language | English |
| last_indexed | 2024-12-20T08:03:51Z |
| publishDate | 2020-12-01 |
| publisher | Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University) |
| record_format | Article |
| series | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| spelling | doaj.art-ffd57137ff254abda0479699b86f84a52022-12-21T19:47:27ZengSaint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki2226-14942500-03732020-12-0120680781410.17586/2226-1494-2020-20-6-807-814METHOD OF JOINT CLUSTERING IN NETWORK AND CORRELATION SPACESAnastasiia N. Gainullina0https://orcid.org/0000-0003-3796-2337Maxim Artyomov1https://orcid.org/0000-0002-1133-4212Alexey A. Sergushichev2https://orcid.org/0000-0003-1159-7220Software Developer, ITMO University, Saint Petersburg, 197101, Russian FederationPhD, Chemistry, Professor (Researcher), ITMO University, Saint Petersburg, 197101, Russian FederationPhD, Associate Professor, Associate Professor, ITMO University, Saint Petersburg, 197101, Russian FederationSubject 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.https://ntv.ifmo.ru/file/article/20004.pdfclusteringcorrelationgraphsmetabolic networksgene expression |
| spellingShingle | Anastasiia N. Gainullina Maxim Artyomov Alexey A. Sergushichev METHOD OF JOINT CLUSTERING IN NETWORK AND CORRELATION SPACES Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki clustering correlation graphs metabolic networks gene expression |
| title | METHOD OF JOINT CLUSTERING IN NETWORK AND CORRELATION SPACES |
| title_full | METHOD OF JOINT CLUSTERING IN NETWORK AND CORRELATION SPACES |
| title_fullStr | METHOD OF JOINT CLUSTERING IN NETWORK AND CORRELATION SPACES |
| title_full_unstemmed | METHOD OF JOINT CLUSTERING IN NETWORK AND CORRELATION SPACES |
| title_short | METHOD OF JOINT CLUSTERING IN NETWORK AND CORRELATION SPACES |
| title_sort | method of joint clustering in network and correlation spaces |
| topic | clustering correlation graphs metabolic networks gene expression |
| url | https://ntv.ifmo.ru/file/article/20004.pdf |
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