| Autor: |
Cenek L; Mathematics and Statistics, Amherst College, Amherst, Massachusetts., Klindziuk L; Mathematics and Statistics, Amherst College, Amherst, Massachusetts., Lopez C; Mathematics and Statistics, Amherst College, Amherst, Massachusetts., McCartney E; Neuroscience Program, Smith College, Northampton, Massachusetts., Martin Burgos B; Neuroscience Program, Smith College, Northampton, Massachusetts., Tir S; Neuroscience Program, Smith College, Northampton, Massachusetts., Harrington ME; Neuroscience Program, Smith College, Northampton, Massachusetts., Leise TL; Mathematics and Statistics, Amherst College, Amherst, Massachusetts. |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of biological rhythms [J Biol Rhythms] 2020 Apr; Vol. 35 (2), pp. 214-222. Date of Electronic Publication: 2020 Jan 28. |
| DOI: |
10.1177/0748730419900866 |
| Abstrakt: |
Circadian rhythms are daily oscillations in physiology and behavior that can be assessed by recording body temperature, locomotor activity, or bioluminescent reporters, among other measures. These different types of data can vary greatly in waveform, noise characteristics, typical sampling rate, and length of recording. We developed 2 Shiny apps for exploration of these data, enabling visualization and analysis of circadian parameters such as period and phase. Methods include the discrete wavelet transform, sine fitting, the Lomb-Scargle periodogram, autocorrelation, and maximum entropy spectral analysis, giving a sense of how well each method works on each type of data. The apps also provide educational overviews and guidance for these methods, supporting the training of those new to this type of analysis. CIRCADA-E (Circadian App for Data Analysis-Experimental Time Series) allows users to explore a large curated experimental data set with mouse body temperature, locomotor activity, and PER2::LUC rhythms recorded from multiple tissues. CIRCADA-S (Circadian App for Data Analysis-Synthetic Time Series) generates and analyzes time series with user-specified parameters, thereby demonstrating how the accuracy of period and phase estimation depends on the type and level of noise, sampling rate, length of recording, and method. We demonstrate the potential uses of the apps through 2 in silico case studies. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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