swMATH ID: 17206
Software Authors: J. A. Perea, A. Deckard, S. B. Haase, J. Harer
Description: SW1PerS: Sliding windows and 1-persistence scoring; discovering periodicity in gene expression time series data.Background: Identifying periodically expressed genes across different processes (e.g. the cell and metabolic cycles, circadian rhythms, etc) is a central problem in computational biology. Biological time series may contain (multiple) unknown signal shapes of systemic relevance, imperfections like noise, damping, and trending, or limited sampling density. While there exist methods for detecting periodicity, their design biases (e.g. toward a specific signal shape) can limit their applicability in one or more of these situations. Methods: We present in this paper a novel method, SW1PerS, for quantifying periodicity in time series in a shape-agnostic manner and with resistance to damping. The measurement is performed directly, without presupposing a particular pattern, by evaluating the circularity of a high-dimensional representation of the signal. SW1PerS is compared to other algorithms using synthetic data and performance is quantified under varying noise models, noise levels, sampling densities, and signal shapes. Results on biological data are also analyzed and compared. Results: On the task of periodic/not-periodic classification, using synthetic data, SW1PerS outperforms all other algorithms in the low-noise regime. SW1PerS is shown to be the most shape-agnostic of the evaluated methods, and the only one to consistently classify damped signals as highly periodic. On biological data, and for several experiments, the lists of top 10
Homepage: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0645-6
Related Software: TDA; javaPlex; PersistenceImages; Ripser; GitHub; Eirene; TopologyNet; Persistence Landscape; AlexNet; CliqueTop; Adam; ImageNet; LOF; Ripser.py; giotto-tda; PersistenceCurves; teaspoon; factoextra; Gudhi; betareg
Cited in: 15 Documents

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