SW1PerS: Sliding windows and 1-persistence scoring; discovering periodicity in gene expression time series data

BMC Bioinformatics

Methodology Article

Steve B. Haase¹  John Harer²  Jose A. Perea³  Anastasia Deckard⁴ 

[1] Center for Systems Biology, Institute for Genome Sciences & Policy, Duke University, 27708, Durham, NC, USA;Department of Biology, Duke University, 27708, Durham, NC, USA;Department of Mathematics, Duke University, Science Dr, 27708, Durham, NC, USA;Center for Systems Biology, Institute for Genome Sciences & Policy, Duke University, 27708, Durham, NC, USA;Department of Computer Science and Department of Electrical and Computer Engineering, Duke University, Science Dr, 27708, Durham, NC, USA;Department of Mathematics, Duke University, Science Dr, 27708, Durham, NC, USA;Institute for Mathematics and its Applications (IMA), University of Minnesota, Minneapolis, MN, USA;Program in Computational Biology and Bioinformatics, Duke University, 27708, Durham, NC, USA;

关键词: Periodicity;    Gene expression;    Time series;    Sliding windows;    Persistent homology;   

DOI  :  10.1186/s12859-015-0645-6

 received in 2014-12-21, accepted in 2015-06-10,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundIdentifying 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.MethodsWe 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.ResultsOn 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% genes ranked with SW1PerS recover up to 67% of those generated with other popular algorithms. Moreover, the list of genes from data on the Yeast metabolic cycle which are highly-ranked only by SW1PerS, contains evidently non-cosine patterns (e.g. ECM33, CDC9, SAM1,2 and MSH6) with highly periodic expression profiles. In data from the Yeast cell cycle SW1PerS identifies genes not preferred by other algorithms, hence not previously reported as periodic, but found in other experiments such as the universal growth rate response of Slavov. These genes are BOP3, CDC10, YIL108W, YER034W, MLP1, PAC2 and RTT101.ConclusionsIn biological systems with low noise, i.e. where periodic signals with interesting shapes are more likely to occur, SW1PerS can be used as a powerful tool in exploratory analyses. Indeed, by having an initial set of periodic genes with a rich variety of signal types, pattern/shape information can be included in the study of systems and the generation of hypotheses regarding the structure of gene regulatory networks.

【 授权许可】

CC BY   
© Perea et al. 2015