title: A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns.
abstract: BACKGROUND
  Identifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that 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.
  
  
  RESULTS
  The proposed method relies on a β-weight function, which produces values between 0 and 1. The β-weight function with β = 0.2 is used as a measure of outlier detection. It assigns smaller weights (≥ 0) to outlying expressions and larger weights (≤ 1) to typical expressions. The distribution of the β-weights is used to calculate the cut-off point, which is compared to the observed β-weight of an expression to determine whether that gene expression is an outlier. This weight function plays a key role in unifying the robustness and efficiency of estimation in one-way ANOVA.
  
  
  CONCLUSIONS
  Analyses of simulated gene expression profiles revealed that all eight methods (ANOVA, SAM, LIMMA, EBarrays, eLNN, KW, robust BetaEB and proposed) perform almost identically for m = 2 conditions in the absence of outliers. However, the robust BetaEB method and the proposed method exhibited considerably better performance than the other six methods in the presence of outliers. In this case, the BetaEB method exhibited slightly better performance than the proposed method for the small-sample cases, but the the proposed method exhibited much better performance than the BetaEB method for both the small- and large-sample cases in the presence of more than 50% outlying genes. The proposed method also exhibited better performance than the other methods for m > 2 conditions with multiple patterns of expression, where the BetaEB was not extended for this condition. Therefore, the 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.
authors.affiliation: Institut Perubatan Molekul UKM (UMBI), University Kebangsaan Malaysia (UKM), Jalan Ya'acob Latiff, Bandar Tun Razak, Cheras 56000 Kuala Lumpur, Malaysia.
authors.forename: Mohammad Manir Hossain
authors.initials: MM
authors.lastname: Mollah
authors.affiliation: Institut Perubatan Molekul UKM (UMBI), University Kebangsaan Malaysia (UKM), Jalan Ya'acob Latiff, Bandar Tun Razak, Cheras 56000 Kuala Lumpur, Malaysia.
authors.forename: Rahman
authors.initials: R
authors.lastname: Jamal
authors.affiliation: Institut Perubatan Molekul UKM (UMBI), University Kebangsaan Malaysia (UKM), Jalan Ya'acob Latiff, Bandar Tun Razak, Cheras 56000 Kuala Lumpur, Malaysia; Department of Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
authors.forename: Norfilza Mohd
authors.initials: NM
authors.lastname: Mokhtar
authors.affiliation: Institut Perubatan Molekul UKM (UMBI), University Kebangsaan Malaysia (UKM), Jalan Ya'acob Latiff, Bandar Tun Razak, Cheras 56000 Kuala Lumpur, Malaysia.
authors.forename: Roslan
authors.initials: R
authors.lastname: Harun
authors.affiliation: Laboratory of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh.
authors.forename: Md Nurul Haque
authors.initials: MN
authors.lastname: Mollah
mesh_terms: D000704:Analysis of Variance; D003110:Colonic Neoplasms / Q000235:genetics; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D010190:Pancreatic Neoplasms / Q000235:genetics; D018401:Sample Size