Bootstrapping and Empirical Bayes Methods Improve Rhythm Detection in Sparsely Sampled Data

There is much interest in using genome-wide expression time series to identify circadian genes. However, the cost and effort of such measurements often limit data collection. Consequently, it is difficult to assess the experimental uncertainty in the measurements and, in turn, to detect periodic pat...

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Bibliographic Details
Published in:Journal of biological rhythms 2018-08, Vol.33 (4), p.339-349
Main Authors: Hutchison, Alan L., Allada, Ravi, Dinner, Aaron R.
Format: Article
Language:English
Subjects:
Algorithms
Atrophy
Bayes Theorem
Bayesian analysis
Circadian Rhythm - genetics
Circadian rhythms
Computational Biology
Confidence intervals
Data Analysis
Data collection
Datasets as Topic
Gene expression
Gene Expression Profiling - methods
Genome
Genomes
Humans
Rhythm
Sampling methods
Shrinkage
Time Factors
Time series
Uncertainty
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Summary:There is much interest in using genome-wide expression time series to identify circadian genes. However, the cost and effort of such measurements often limit data collection. Consequently, it is difficult to assess the experimental uncertainty in the measurements and, in turn, to detect periodic patterns with statistical confidence. We show that parametric bootstrapping and empirical Bayes methods for variance shrinkage can improve rhythm detection in genome-wide expression time series. We demonstrate these approaches by building on the empirical JTK_CYCLE method (eJTK) to formulate a method that we term BooteJTK. Our procedure rapidly and accurately detects cycling time series by combining information about measurement uncertainty with information about the rank order of the time series values. We exploit a publicly available genome-wide data set with high time resolution to show that BooteJTK provides more consistent rhythm detection than existing methods at typical sampling frequencies. Then, we apply BooteJTK to genome-wide expression time series from multiple tissues and show that it reveals biologically sensible tissue relationships that eJTK misses. BooteJTK is implemented in Python and is freely available on GitHub at https://github.com/alanlhutchison/BooteJTK.
ISSN:0748-7304
1552-4531
DOI:10.1177/0748730418789536