title: New criteria for selecting differentially expressed genes.
abstract: One of the major concerns in detecting changes in higher moments is these changes may be due to outliers or process errors that are not biologically significant. For example, a larger variance observed in the expression levels may simply due to the larger variation in the data collecting process. Several outliers, which exhibit some extreme expression levels than the rest of the samples, may also increase the variance or skewness of the expression levels significantly. So it is very important to reduce the effect of outliers and process errors by proper experimental designs [27], such as technical replicates and biological replicates, before high sensitivity criterion, such as ADS, can be applied. We have presented and demonstrated the operation of two new criteria, ADS and the MDS, for identifying differentially expressed genes. These two criteria were compared with several commonly used criteria, namely WTS, WRS, FCS, and ICE. Experiments with simulated data show ADS to be more powerful than the WTS. When high-sensitivity screening is required, ADS appears to be preferable to WTS. When an FPR similar to WTS is desired, MDS should be used. The popular Wilcoxon rank sum is a more conservative approach that should be employed when the lowest FPR is desired, even at the expense of lower TPRs. ICE is a less desirable criterion because it does not perform well for data generated by the normal model. FCS gave results similar to those of WTS. Evaluation of these algorithms using real biological datasets showed that ADS and MDS flagged several biologically significant genes that were missed by WTS, besides selecting most of the genes that are also selected by WTS.
authors: Green Comprehensive Center for Computational and Systems Biology, University of Texas Southwestern Medical Center, USA.
authors: Lit-Hsin
authors: LH
authors: Loo
authors: Samuel
authors: S
authors: Roberts
authors: Leonid
authors: L
authors: Hrebien
authors: Moshe
authors: M
authors: Kam
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity