TY  - JOUR
A1  - Omranian, Nooshin
A1  - Müller-Röber, Bernd
A1  - Nikoloski, Zoran
T1  - Segmentation of biological multivariate time-series data
JF  - Scientific reports
N2  - 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.
Y1  - 2015
U6  - https://doi.org/10.1038/srep08937
SN  - 2045-2322
VL  - 5
PB  - Nature Publ. Group
CY  - London
ER  -