Identification of under-detected periodicity in time-series microarray data by using empirical mode decomposition.

ABSTRACT: Detecting periodicity signals from time-series microarray data is commonly used to facilitate the understanding of the critical roles and underlying mechanisms of regulatory transcriptomes. However, time-series microarray data are noisy. How the temporal data structure affects the performance of periodicity detection has remained elusive. We present a novel method based on empirical mode decomposition (EMD) to examine this effect. We applied EMD to a yeast microarray dataset and extracted a series of intrinsic mode function (IMF) oscillations from the time-series data. Our analysis indicated that many periodically expressed genes might have been under-detected in the original analysis because of interference between decomposed IMF oscillations. By validating a protein complex coexpression

SUBMITTER: Chen CR 

PROVIDER: S-EPMC4221108 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature