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

ABSTRACT:

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 evaluat

SUBMITTER: Perea JA 

PROVIDER: S-EPMC4537550 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature