Breaking news: A Robust Tool for Gene Expression Analysis Developed by UMBI Researchers Published in PLoS One
Identification of differentially expressed (DE) genes between two or more conditions with multiple patterns of expression is one of the main objectives of gene expression studies. Numerous statistical approaches, including one-way analysis of variance (ANOVA), are commonly used to identify DE genes. However, most of these methods provide misrepresentative results for two or more conditions with multiple patterns of expression in the presence of outlying genes. Our team of researchers have successfully developed a hybrid one-way ANOVA approach, which has been published in the PLoS One journal. This tool unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression.
The proposed method exhibited better performance than the other methods namely ANOVA, SAM, LIMMA, EBarrays, eLNN, Kruskal-Wallis test, and robust BetaEB. Therefore, this proposed approach would be more suitable and reliable on average for the identification of DE genes between two or more conditions with multiple patterns of expression.
Mollah MM, Jamal R, Mokhtar NM, Harun R, Mollah MN.A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns. PLoS One. 2015 Sep 28;10(9):e0138810. doi: 10.1371/journal.pone.0138810. eCollection 2015.
This research was funded by a grant from the Ministry of Education under the Higher Institution Centre of Excellence (HICoE) programme (10-64-01-005).
