title: Comparison of methods for identifying periodically varying genes.
abstract: Several methods have been reported for identifying periodically varying genes from gene expression datasets. We compare the performance of five existing methods and a combination of G-statistic and autocovariance (called GVAR) using simulated sine-function-based and cell-cycle-based datasets. Based on this analysis we recommend appropriate methods for different experimental situations (length of the time series, sampling interval and noise level). No single method performs the best under all tested conditions. None of the evaluated methods perform well at high noise levels for short time series data. At lower noise levels, GVAR performed the best.
authors.affiliation: Chemical Engineering and Process Development, National Chemical Laboratory, CSIR, Pune 411008, India. vini.vij06@gmail.com
authors.forename: Vinaya
authors.initials: V
authors.lastname: Vijayan
authors.forename: Prachi
authors.initials: P
authors.lastname: Deshpande
authors.forename: Chetan
authors.initials: C
authors.lastname: Gadgil
authors.forename: Mugdha
authors.initials: M
authors.lastname: Gadgil
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling; D018816:Genes, cdc; D008957:Models, Genetic; D015233:Models, Statistical