2016
DOI: 10.1093/bioinformatics/btw405
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MetaCycle: an integrated R package to evaluate periodicity in large scale data

Abstract: Summary: Detecting periodicity in large scale data remains a challenge. While efforts have been made to identify best of breed algorithms, relatively little research has gone into integrating these methods in a generalizable method. Here, we present MetaCycle, an R package that incorporates ARSER, JTK_CYCLE and Lomb-Scargle to conveniently evaluate periodicity in time-series data. MetaCycle has two functions, meta2d and meta3d, designed to analyze two-dimensional and three-dimensional time-series datasets, res… Show more

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Cited by 470 publications
(438 citation statements)
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“…ARSER detrends data and then detects rhythmic signals with a period between 20 and 28 hours through a combination of autoregressive spectral analysis (alternative to the classical fast Fourier transformation) and harmonic regression (sinusoidal fits) and then reports relevant parameters such as period, phase, and amplitude along with significance statistics. ARSER was run through the MetaCycle package implemented in R (Wu et al., 2016). ARSER has been shown to frequently perform better than other popular circadian gene identification algorithms when analyzing data collected over two days with 4-hour resolution (Wu et al, 2016; Yang and Su, 2010).…”
Section: Star Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…ARSER detrends data and then detects rhythmic signals with a period between 20 and 28 hours through a combination of autoregressive spectral analysis (alternative to the classical fast Fourier transformation) and harmonic regression (sinusoidal fits) and then reports relevant parameters such as period, phase, and amplitude along with significance statistics. ARSER was run through the MetaCycle package implemented in R (Wu et al., 2016). ARSER has been shown to frequently perform better than other popular circadian gene identification algorithms when analyzing data collected over two days with 4-hour resolution (Wu et al, 2016; Yang and Su, 2010).…”
Section: Star Methodsmentioning
confidence: 99%
“…ARSER was run through the MetaCycle package implemented in R (Wu et al., 2016). ARSER has been shown to frequently perform better than other popular circadian gene identification algorithms when analyzing data collected over two days with 4-hour resolution (Wu et al, 2016; Yang and Su, 2010). Cutoffs of p<0.05 and Benjamini and Hochberg false-discovery rate (FDR) <0.20 were used to identify circadian transcripts.…”
Section: Star Methodsmentioning
confidence: 99%
“…p<0.05 was considered significant. JTK Cycle was used as described (Wu et al, 2016). The program cosinor was downloaded from www.Circadian.org (last accessed 09/01/2016).…”
Section: Methodsmentioning
confidence: 99%
“…There have been steady developments in software to extract rhythmic signals from the non-rhythmic background; these are now highly developed and in passing a few technical issues should be noted. There are more stringent (JTK, 92, 93) and less stringent (ARSER, 94)) software and algorithms available for calling rhythmic genes. To be dependably able to call rhythmic genes sampling density must be high; samples taken at least every 2 hrs over 2 days are needed to reliably identify even 80% of rhythmic genes, and hourly sampling is better (92).…”
Section: Output From the Clockmentioning
confidence: 99%