informИнформатикаInformatics1816-03012617-6963UIIP NASBinform-125Research ArticleБИОИНФОРМАТИКАBIOINFORMATICSМЕТОД ПОСТРОЕНИЯ КЛАСТЕРОВ ГЕНЕТИЧЕСКИХ ДАННЫХMETHOD OF CONSTRUCTION OF GENETIC DATA CLUSTERSНовоселоваН. А.NovoselovaN. A.novosel@newman.bas-net.byТомИ. Э.TomI. E.Объединенный институт проблем информатики НАН БеларусиBelarus201603102016016474Copyright © Новоселова Н.А., Том И.Э., 20162016Новоселова Н.А., Том И.Э.Novoselova N.A., Tom I.E.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://inf.grid.by/jour/article/view/125

Предлагается метод построения кластеров (функциональных модулей) генетических данных, основанный на использовании рандомизированных матриц. Для построения кластеров выполняется выделение и анализ главных компонент матрицы корреляций генных профилей. В качестве конечных выбираются главные компоненты, которые соответствуют собственным значениям, значимо отличающимся от полученных при анализе случайным образом сгенерированной корреляционной матрицы (рандомизированной). В сравнительном вычислительном эксперименте с аналогами метод показал свое преимущество в возможности выделять статистически значимые кластеры малых и больших размеров, способности отфильтровывать неинформативные признаки, а также получать биологически интерпретируемые функциональные модули, адекватные реальной структуре данных.

The paper presents a method of construction of genetic data clusters (functional modules) using the randomized matrices. To build the functional modules the selection and analysis of the eigenvalues of the gene profiles correlation matrix is performed. The principal components, corresponding to the eigenvalues, which are significantly different from those obtained for the randomly generated correlation matrix, are used for the analysis. Each selected principal component forms gene cluster. In a comparative experiment with the analogs the proposed method shows the advantage in allocating statistically significant different-sized clusters, the ability to filter non- informative genes and to extract the biologically interpretable functional modules matching the real data structure.

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The authors declare that there are no conflicts of interest present.