Open AccessIdentification of Under-Detected Periodicity in Time-Series Microarray Data by Using Empirical Mode Decomposition
Author(s) -
Chaang-Ray Chen,
WunYi Shu,
Cheng-Wei Chang,
Ian C. Hsu
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0111719
Subject(s) - hilbert–huang transform , microarray analysis techniques , time series , series (stratigraphy) , microarray , gene chip analysis , data mining , computer science , mode (computer interface) , computational biology , biology , pattern recognition (psychology) , algorithm , artificial intelligence , genetics , gene , gene expression , machine learning , paleontology , filter (signal processing) , computer vision , operating system
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 analysis, we revealed that 56 genes were newly determined as periodic. We demonstrated that EMD can be used incorporating with existing periodicity detection methods to improve their performance. This approach can be applied to other time-series microarray studies.