Detecting biological oscillation in time series omics data
using combination of bootstrap and maximal information coefficient
Systems Biology Program, Graduate School of Media and
Governance
Hitohis Iuchi
Hitohis Iuchi
Oscillation in biological process plays a important roll to keep
biological homeostasis. It is generated by intercellular negative feedback loop
of transcripts and proteins. Then, detection of oscillating molecules in large
data sets such as transcriptome, proteome and metabolome has been huge issue.
Traditional method based on Fourier transform is widely used, but sensitive for
experimental and biological noises. Here we present non-parametric rhythm
detection method using bootstrap and maximal information-based nonparametric
exploration (MINE) statistics. Requipment study for parameter setting was
performed and applied for open mouse liver proteome data. Then, not only
oscillating molecules detected in previous study but also novel significant
rhythmic protein were detected. This combination approach using bootstrap and
MINE is valuable for biological oscillation
study