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Optimal experimental design to estimate statistically significant periods of oscillations in time course data.

ABSTRACT: We investigated commonly used methods (Autocorrelation, Enright, and Discrete Fourier Transform) to estimate the periodicity of oscillatory data and determine which method most accurately estimated periods while being least vulnerable to the presence of noise. Both simulated and experimental data were used in the analysis performed. We determined the significance of calculated periods by applying these methods to several random permutations of the data and then calculating the probability of obtaining the period's peak in the corresponding periodograms. Our analysis suggests that the Enright method is the most accurate for estimating the period of oscillatory data. We further show that to accurately estimate the period of oscillatory data, it is necessary that at least five cycles of data are sampled, using at least four data points per cycle. These results suggest that the Enright method should be more widely applied in order to improve the analysis of oscillatory data.

SUBMITTER: Mourao M

PROVIDER: S-EPMC3974819 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Optimal experimental design to estimate statistically significant periods of oscillations in time course data.

Mourão Márcio M Satin Leslie L Schnell Santiago S

PloS one 20140403 4

We investigated commonly used methods (Autocorrelation, Enright, and Discrete Fourier Transform) to estimate the periodicity of oscillatory data and determine which method most accurately estimated periods while being least vulnerable to the presence of noise. Both simulated and experimental data were used in the analysis performed. We determined the significance of calculated periods by applying these methods to several random permutations of the data and then calculating the probability of obt ...[more]

PMID: 24699692

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Project description:MotivationMost modern intensive care units record the physiological and vital signs of patients. These data can be used to extract signatures, commonly known as biomarkers, that help physicians understand the biological complexity of many syndromes. However, most biological biomarkers suffer from either poor predictive performance or weak explanatory power. Recent developments in time series classification focus on discovering shapelets, i.e. subsequences that are most predictive in terms of class membership. Shapelets have the advantage of combining a high predictive performance with an interpretable component-their shape. Currently, most shapelet discovery methods do not rely on statistical tests to verify the significance of individual shapelets. Therefore, identifying associations between the shapelets of physiological biomarkers and patients that exhibit certain phenotypes of interest enables the discovery and subsequent ranking of physiological signatures that are interpretable, statistically validated and accurate predictors of clinical endpoints.ResultsWe present a novel and scalable method for scanning time series and identifying discriminative patterns that are statistically significant. The significance of a shapelet is evaluated while considering the problem of multiple hypothesis testing and mitigating it by efficiently pruning untestable shapelet candidates with Tarone's method. We demonstrate the utility of our method by discovering patterns in three of a patient's vital signs: heart rate, respiratory rate and systolic blood pressure that are indicators of the severity of a future sepsis event, i.e. an inflammatory response to an infective agent that can lead to organ failure and death, if not treated in time.Availability and implementationWe make our method and the scripts that are required to reproduce the experiments publicly available at https://github.com/BorgwardtLab/S3M.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Optimal experimental design to estimate statistically significant periods of oscillations in time course data.

Publications

Optimal experimental design to estimate statistically significant periods of oscillations in time course data.

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