Dynamic-model-based method for selecting significantly expressed genes from time-course expression profiles.
This paper proposes a dynamic-model-based method for selecting significantly expressed (SE) genes from their time-course expression profiles. A gene is considered to be SE if its time-course expression profile is more likely time-dependent than random. The proposed method describes a time-dependent gene expression profile by a nonzero-order autoregressive (AR) model, and a time-independent gene expression profile by a zero-order AR model. Akaike information criterion (AIC) is used to compare the models and subsequently determine whether a time-course gene expression profile is time-independent or time-dependent. The performance of the proposed method is investigated on both a synthetic dataset and a real-life biological dataset in terms of the false discovery rate (FDR) and the false nondiscovery rate (FNR). The results show that the proposed method is valid for selecting SE genes from their time-course expression profiles.
Wu
Fang-Xiang
FX
Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N5A9, Canada. faw341@mail.usask.ca
Zhang
Wen-Jun
WJ
D000465:Algorithms; D030541:Databases, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D012044:Regression Analysis; D015203:Reproducibility of Results