Title: Comparison of methods for identifying periodically varying genes

Authors: Vinaya Vijayan; Prachi Deshpande; Chetan Gadgil; Mugdha Gadgil

Addresses: Chemical Engineering and Process Development, National Chemical Laboratory, CSIR, Pune 411008, India ' Chemical Engineering and Process Development, National Chemical Laboratory, CSIR, Pune 411008, India ' Chemical Engineering and Process Development, National Chemical Laboratory, CSIR, Pune 411008, India ' Chemical Engineering and Process Development, National Chemical Laboratory, CSIR, Pune 411008, India

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.

Keywords: periodically varying genes; microarrays; autocovariance; time series; gene expression; simulation; cell cycle; periodic genes; bioinformatics.

DOI: 10.1504/IJBRA.2013.050653

International Journal of Bioinformatics Research and Applications, 2013 Vol.9 No.1, pp.53 - 70

Received: 16 Oct 2010
Accepted: 06 Dec 2010
Published online: 06 Sep 2014 *

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