Detecting Significantly Expressed Genes from Their Time-Course Expression Profiles and Its Validation

This paper proposes a model-based method for detecting significantly expressed genes from their time-course expression profiles. A gene is considered to be significantly expressed 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 non-zero 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 biological dataset in terms of the false discovery rate (FDR) and the false non-discovery rate (FNR). The results show that the proposed method is valid for detecting significantly expressed genes from their time-course expression profiles.