Finding Significantly Expressed genes from time-course expression profiles.
This paper proposes a statistical method for finding Significantly Expressed (SE) genes from time-course expression. SE genes are time-dependent while non-SE genes are time-independent. This method models time-dependent gene expression profiles by autoregressive equations plus Gaussian noises, and time-independent ones by Gaussian noises. The statistical F-testing is used to calculate the probability (p-value) that a profile is time-independent. Both a synthetic dataset and a biological dataset were employed to evaluate the performance of this method, measured by the False Discovery Rate (FDR) and the False Non-discovery Rate (FNR). Results show that the proposed method outperforms traditional methods.
Wu
Fang-Xiang
FX
Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, SK, S7N 5A9, Canada. faw341@mail.usask.ca
Xia
Zhonghang
Z
Mu
Lei
L
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D005189:False Positive Reactions; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity