Bootstrapping and Empirical Bayes Methods Improve Rhythm Detection in Sparsely Sampled Data.
J Biol Rhythms
; 33(4): 339-349, 2018 08.
Article
em En
| MEDLINE
| ID: mdl-30101659
ABSTRACT
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 .
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Teorema de Bayes
/
Ritmo Circadiano
/
Genoma
/
Perfilação da Expressão Gênica
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Análise de Dados
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
J Biol Rhythms
Assunto da revista:
FISIOLOGIA
Ano de publicação:
2018
Tipo de documento:
Article