Segmentation of biological multivariate time-series data

Scientific Reports
Nooshin OmranianZoran Nikoloski

Abstract

Time-series data from multicomponent systems capture the dynamics of the ongoing processes and reflect the interactions between the components. The progression of processes in such systems usually involves check-points and events at which the relationships between the components are altered in response to stimuli. Detecting these events together with the implicated components can help understand the temporal aspects of complex biological systems. Here we propose a regularized regression-based approach for identifying breakpoints and corresponding segments from multivariate time-series data. In combination with techniques from clustering, the approach also allows estimating the significance of the determined breakpoints as well as the key components implicated in the emergence of the breakpoints. Comparative analysis with the existing alternatives demonstrates the power of the approach to identify biologically meaningful breakpoints in diverse time-resolved transcriptomics data sets from the yeast Saccharomyces cerevisiae and the diatom Thalassiosira pseudonana.

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Citations

Jan 23, 2016·Molecular Systems Biology·Zhe ChengChristine Vogel
Dec 17, 2016·BMC Bioinformatics·Svetlana BulashevskaArmin B Cremers
Apr 21, 2018·Science Advances·Meenakshi Chatterjee, Murat Acar
Jun 19, 2018·Network Neuroscience·Taylor BoltLucina Q Uddin
May 31, 2019·BMC Bioinformatics·Maryam Masnadi-ShiraziShankar Subramaniam
Jan 19, 2019·Proceedings of the National Academy of Sciences of the United States of America·Antonio C CostaGreg J Stephens

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Software Mentioned

LASSO
R package
MTS
R
seg

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