Understanding how such rhythms couple to biological processes requires statistical methods that can identify cycling time series in typical genome-Wide data.

In this paper, we improve on a method used to identify cycling time series by better estimating the statistical significance of periodic patterns and, in turn, by searching for a Wider range of patterns than traditionally investigated.

Despite the decreasing cost of measuring transcript levels, profiling time series genome-wide continues to present formidable challenges: tissue-specific samples are difficult to collect, and, in contrast to imaging, measuring transcript levels is destructive in nature, requiring separate samples for each time point.

As a result, gene expression time series are typically sparsely sampled (e.g., every 2—4 hours (h) in circadian studies), often without multiple measurements per time point, which we refer to here as “replicates”.

Quantitative methods are thus needed to identify rhythmic time series from minimal data with statistical confidence.

In contrast, F24 and ITK_CYCLE compare the time series in question to a reference waveform, which is typically sinusoidal.

In this way, stable persistence tries to robustly assess overall monotonicity of a time series .

The numerator is the number of pairs that vary concordantly between the two time series minus the number that vary discordantly (Fig.